Agentic systems are AI-powered tools that can take initiative—planning actions, making decisions, and adapting to changing inputs—rather than just reacting to static commands. Unlike traditional automation, which waits for instructions, agentic systems operate more like digital coworkers, proactively identifying what needs to happen next. In the AEC industry, this might look like an AI that monitors deadlines, flags missing content, recommends win themes, or auto-assigns tasks based on RFP requirements. Joist AI is moving toward this agentic model—helping teams go beyond automation to intelligent, collaborative pursuit management.
Architects lead the design and planning of buildings and spaces, balancing aesthetics, function, sustainability, and code compliance. They collaborate with clients, engineers, and consultants to bring a project’s vision to life—from conceptual design through construction documents. In proposal settings, Architects contribute design narratives, innovation highlights, and relevant project experience. Joist AI supports Architects by organizing language from past work, generating polished drafts, and reducing the time required to contribute valuable input—without disrupting their design responsibilities.
Artificial intelligence is a broad field focused on building systems that can mimic human cognitive abilities, such as learning, reasoning, and problem-solving. In practice, AI often involves machine learning, natural language processing, and other techniques that allow software to make decisions or generate content. For AEC firms, AI can accelerate proposal creation, automate content retrieval, and analyze project documents with remarkable speed. Joist AI uses these capabilities to help AEC teams respond to RFPs more effectively—by learning from past work, analyzing new documents, and generating responses with precision. Rather than replacing people, it enhances human expertise at scale. AI turns institutional knowledge into a competitive advantage.
An award submission is a curated document or package created to showcase a firm’s excellence on a particular project or initiative, typically in response to a call for entries by industry organizations. These submissions highlight innovation, design quality, community impact, or project delivery success. They often repurpose proposal materials but require a more narrative, visual, and compelling tone. Joist AI accelerates the award submission process by pulling from past project write-ups, tagging notable achievements, and generating polished content that aligns with award criteria.
A bid is a formal offer to deliver a project or service at a specified price, timeline, and scope—typically submitted in response to an RFP. In AEC, bids often involve both technical and cost proposals, and require precise coordination across estimators, engineers, and marketers. Winning a bid hinges on alignment between the client’s needs and the firm’s capabilities. Joist AI supports this process by enabling faster, more accurate technical proposal creation—so teams can focus on pricing and strategy.
Black-box AI refers to systems that make decisions or generate outputs without offering transparency into how those results were reached. This can make it difficult to validate accuracy, trace logic, or troubleshoot errors—especially in high-stakes environments like AEC proposals. Joist AI is designed to avoid this issue by grounding outputs in retrievable, source-linked content. That means users can see where the information came from, review sources, and maintain confidence in the final product.
Black-box AI refers to systems that make decisions or generate outputs without offering transparency into how those results were reached. This can make it difficult to validate accuracy, trace logic, or troubleshoot errors—especially in high-stakes environments like AEC proposals. Joist AI is designed to avoid this issue by grounding outputs in retrievable, source-linked content. That means users can see where the information came from, review sources, and maintain confidence in the final product.
Brand identity is the visual and verbal expression of your firm’s brand—how it looks, feels, and communicates across all channels. It includes your logo, color palette, typography, tone, and messaging. In AEC marketing, a strong brand identity helps firms stand out in a crowded field and builds familiarity with clients. Joist AI helps uphold brand identity by leveraging your brand and messaging guidelines, keeping your team consistent and on brand, even across multiple geographical regions.
Brand voice is the distinct personality and tone that a firm uses in its written and spoken communications. For AEC firms, it reflects how you want clients to perceive you—whether that’s as technical experts, trusted partners, or creative problem-solvers. Maintaining a consistent brand voice across pursuits builds trust and reinforces differentiation. Joist AI helps preserve and replicate brand voice by learning from your past proposals and applying your style preferences to every draft.
Business development in the AEC industry refers to all the activities that generate new opportunities and relationships—before a formal RFP ever hits your inbox. It includes networking, client outreach, market research, and positioning your firm for future work. BD professionals lay the groundwork that marketing and technical teams build on during a pursuit. Joist AI supports business development by helping teams track pursuits, identify relevant past work, and quickly tailor content to nurture leads and win trust.
BD Directors and Managers identify new opportunities, build client relationships, and drive pursuit strategy. They often coordinate with technical teams and marketing to ensure client needs are met and expectations are exceeded. When repeat business or contract expansions arise, they play a key role in shaping pursuit strategy and facilitating responses. Joist AI supports BD leaders by surfacing relevant experience, helping tailor messaging for target clients, and generating early-stage materials that help warm leads before the RFP drops.
Chain of thought is a reasoning technique used by AI models to solve problems step by step—just like a person might talk through their thinking before arriving at a conclusion. Instead of jumping straight to an answer, the AI explains its logic, which helps improve accuracy and transparency. In the context of AEC marketing, this is especially useful for complex prompts like scoring an RFP, drafting a project approach, or justifying a win theme. Joist AI applies chain of thought reasoning to help users trace how responses are generated—making it easier to review, edit, and trust the final output.
A prompt chain breaks a complex task into smaller, sequenced steps. Each output feeds into the next prompt. This helps AI stay focused and reduces errors. It’s like giving directions one turn at a time instead of handing someone a full map. Joist AI uses this approach internally in certain workflows, such as using the RFP to generate proposal outlines section by section, ensuring focus and coherence. It's ideal for longform content like cover letters or detailed methodology sections. The result is smarter structure and less editing.
Example: Instead of asking the AI to “write a complete project approach,” a prompt chain might start with “summarize the client’s priorities,” then use that output to prompt “list the key project phases,” followed by “describe how our team will manage each phase,” and so on—building the section logically and collaboratively.
A chatbot is a software application designed to simulate conversation with users—often through text, and sometimes via voice. Chatbots can answer questions, direct users to information, or handle simple tasks. Unlike traditional chatbots, Joist AI is powered by a large language model, enabling it to understand context and generate thoughtful, domain-specific responses. But Joist goes further—it incorporates structured workflows, retrieval systems, and content templates to deliver accurate, workflow-specific results. Rather than just answering questions, Joist functions as a strategic assistant purpose-built for AEC proposal teams—guiding users through complex tasks, not just chatting at them.
The Chief Growth Officer is responsible for driving firm-wide growth by aligning business development, marketing, and strategic initiatives. In AEC firms, this often means expanding into new markets, optimizing pursuit processes, and improving win rates. CGOs rely on tools that help their teams respond faster, maintain consistency, and increase output without increasing headcount. Joist AI supports this mandate by accelerating proposal creation, streamlining access to firm knowledge, and enabling teams to pursue more opportunities with greater confidence.
The Chief Innovation Officer is tasked with identifying, evaluating, and implementing emerging technologies that can create competitive advantage and operational efficiency. In AEC firms, this includes exploring AI, automation, and digital collaboration tools that enhance how teams work and win. CINOs are often looking for tangible, scalable improvements—not just new tech for tech’s sake. Joist AI supports this mission by modernizing proposal development workflows, reducing reliance on manual effort, and demonstrating how AI can create immediate, measurable impact across pursuit and marketing operations.
The Chief Marketing Officer leads firm-wide brand strategy, marketing operations, and pursuit enablement—ensuring the firm presents a unified, compelling voice in every client interaction. In AEC firms, this role often spans everything from thought leadership to proposal systems to business development alignment. CMOs care about speed, quality, and consistency across distributed teams and regions. Joist AI supports CMOs by helping teams produce branded, compliant content at scale—reducing bottlenecks, shortening timelines, and unlocking more capacity for strategic initiatives.
The Chief Strategy Officer drives long-term growth by shaping market positioning, guiding firm priorities, and overseeing competitive and client intelligence. In AEC firms, CSOs often bridge business development, marketing, and operations to ensure strategic alignment. While they may not manage proposals directly, they care deeply about how the firm presents itself in pursuit efforts. Joist AI supports this role by standardizing messaging, improving responsiveness, and making it easier for teams to articulate value across sectors and client types.
Civil Engineers design, plan, and oversee the construction of infrastructure projects such as roads, bridges, water systems, and site developments. Their work involves technical analysis, permitting, and coordination across disciplines to ensure safety, compliance, and performance. In the context of proposals, Civil Engineers contribute scope details, methodologies, and QA/QC protocols that showcase the firm’s technical credibility. Joist AI reduces the burden on Civil Engineers by generating draft narratives, formatting resumes, and surfacing relevant project descriptions—so they can contribute efficiently without pulling away from billable work.
Client Services Directors manage key relationships and ensure long-term client satisfaction. They play a role in cross-selling and strategic positioning. Joist AI enables them to understand pursuit requirements, align content to client needs, and deliver consistent messaging across touchpoints.
Close-out in AEC marketing refers to the process of wrapping up a pursuit after submission. This may include debriefs with internal teams, saving and tagging final documents, recording outcomes, and documenting lessons learned. It’s a key opportunity to improve future pursuits and track win/loss trends. Joist AI helps teams close out effectively by organizing proposal artifacts, tagging key content for reuse, and generating summaries that capture what worked—and what didn’t.
This role sets the tone and voice of the firm—often overseeing both internal messaging and outward-facing communications like proposals, award submissions, and thought leadership. Joist AI supports them by enforcing brand voice, applying templates, and helping teams generate on-brand content at scale—without compromising quality.
Compliance in AEC marketing refers to how closely a proposal or qualifications submittal follows the specific requirements outlined in an RFP or RFQ. This includes formatting rules, page limits, section order, forms, and submission methods. Non-compliance can result in automatic disqualification—even if your content is strong. Joist AI supports compliance by checking for required elements, mirroring structure, and flagging missing sections early in the process, helping teams submit with confidence.
In CMAR, the construction manager commits to a guaranteed maximum price early in the project. They act as both advisor and builder. It’s a popular method for balancing cost control with schedule flexibility. Owners benefit from early cost insights and collaborative planning.
A content library is a centralized repository where firms store reusable marketing and proposal materials—such as resumes, project descriptions, boilerplate language, graphics, and templates. In AEC marketing, an organized content library saves teams time, ensures consistency, and reduces duplication. The challenge is keeping it updated and searchable. Joist AI turns your content library into a dynamic, intelligent system—automatically tagging assets, surfacing relevant content, and learning what gets used most to continually improve access.
Contextual prompting involves supplying the AI with relevant background information—such as past projects, client preferences, or company standards—to help it generate more accurate, tailored content. The more useful context the AI has, the stronger and more customized the output. Joist AI uses contextual prompting behind the scenes by drawing from your firm’s historical proposals, resumes, and narratives to inform each response. This ensures that content aligns with how your team has communicated in the past, making outputs feel both familiar and client-specific without requiring users to restate the details.
In the AI context, a copilot is a digital assistant that works alongside a human to support complex tasks—rather than automate them entirely. It augments decision-making by offering suggestions, drafting content, surfacing relevant information, or executing repetitive actions. Joist AI functions as a copilot for AEC proposal teams, helping marketers and technical staff work faster and smarter without replacing their expertise. The goal is not to take over the process, but to reduce the friction within it.
A cover letter is the first impression in any proposal submission. It introduces the team, affirms understanding of the client’s goals, and sets the tone for the rest of the document. A well-crafted cover letter is personalized and persuasive, rather than generic or overly formal. It often reflects the firm's voice and highlights its alignment with the project vision.
A CRM is your central hub for managing clients, prospects, and opportunities. From tracking outreach to storing contact details, it keeps sales and marketing aligned. In the AEC industry, it’s especially helpful for managing long sales cycles and complex stakeholder networks. Joist AI integrates with some CRM systems to pull in relationship data and ensure generated proposal content aligns with account context, helping teams maintain a single source of truth across marketing and business development.
A traditional project delivery method where design and construction are separate contracts. It’s linear, familiar, and widely used—but can be slower or riskier if coordination breaks down. Design is completed before construction begins, which gives the owner greater control but limits early collaboration.
A project delivery method that combines design and construction under one contract, fostering collaboration and speed. It’s gaining traction for its efficiency, especially when time-to-completion is critical. This approach allows for overlapping phases and streamlined communication.
Digital assets are the files and media your firm uses to communicate and market—think logos, photos, diagrams, infographics, boilerplate language, and video reels. In AEC marketing, these assets are critical for maintaining brand consistency across proposals and collateral. Organizing and retrieving them efficiently can make or break a tight submission timeline. Joist AI enhances digital asset usage by tagging, indexing, and surfacing the right files at the right time—so your best content never gets buried.
Digital Asset Managers (or DAM systems for short) store and organize your content—images, templates, diagrams, boilerplate language, and more. They’re essential when you're managing dozens of proposals and marketing assets. A strong DAM makes finding the right file as easy as typing a few keywords. It ensures brand consistency and reduces redundant work.
The Director of Operations oversees staffing, resource allocation, and project delivery workflows across the firm. In AEC organizations, they ensure that teams are properly assigned, deadlines are met, and project commitments align with available capacity. While not always directly involved in proposals, their role is crucial to maintaining balance between pursuit activity and billable work. Joist AI supports operational leaders by making staff experience easily searchable, simplifying resume generation, and enabling more informed decisions about team deployment during active pursuits.
A due diligence questionnaire is a detailed request for operational, legal, financial, or compliance-related information—typically issued before a partnership or contract is finalized. In the AEC space, DDQs often surface during private-sector pursuits, vendor onboarding, or mergers and acquisitions. They can be time-consuming to complete, requiring coordination across legal, finance, HR, and marketing teams. Joist AI streamlines the DDQ process by organizing previously submitted answers and ensuring consistent, compliant responses across the firm.
Embedding is the process of turning text into numerical vectors so that it can be compared or searched semantically. Joist AI uses embeddings to match prompts with the most relevant content in your library—even when the words don’t exactly match. It’s how the system “understands” that “stormwater management” and “drainage design” might mean the same thing in a given context.
ERP systems help firms manage their day-to-day operations in one place. This includes things like staffing, budgets, schedules, and invoices. While CRMs focus on external relationships, ERPs focus on internal resources—making sure the right people, time, and money are in place to deliver projects. In AEC firms, ERPs are critical for keeping projects on track and managing workloads across teams.
Explainability refers to how clearly an AI system can show the reasoning behind its outputs. It builds trust and allows users to validate content before using it. Joist AI supports explainability by referencing source materials, showing search queries generated as part of a chat, and making it easy to trace where content came from.
A fee proposal—or fee letter—is a document that outlines the costs associated with delivering a project, often submitted alongside or after a technical proposal. It typically includes breakdowns by phase, discipline, or task, and sometimes references contract terms. In AEC, these documents must be clear, accurate, and defensible—especially in competitive or public-sector bids. Joist AI helps by generating consistent narrative components, allowing fee writers to focus on pricing details without rewriting project descriptions or scope overviews.
Few-shot prompting gives the AI multiple examples to mimic. This helps improve output quality, especially for nuanced or high-stakes tasks. It’s like showing, not just telling. In technical or regulatory responses, this method produces more relevant and confident content.
A foundational model is a large, pre-trained AI model that powers more specific applications. It’s trained on massive datasets and can be fine-tuned for industry use. These models are the base layer of tools like Joist AI and serve as general-purpose engines that can be directed with prompts. Joist AI builds on these models with AEC-specific training and retrieval techniques. That’s how it understands the difference between “project approach” and “scope creep.”
The go/no-go process is a structured decision-making step where AEC firms evaluate whether to pursue a specific opportunity. It considers factors like client fit, scope, competition, resources, and win probability. Making smart go/no-go decisions improves hit rates and protects teams from burning out on low-probability pursuits. Joist AI supports this process by helping firms assess pursuit history, analyze opportunity alignment, and surface relevant data to inform strategic calls.
A go/no-go scorecard is a structured tool used to evaluate whether an AEC firm should pursue a project opportunity. It includes weighted criteria such as client relationship strength, project fit, team availability, and potential for repeat work. By scoring each factor, teams can make more objective, consistent decisions. Joist AI can support this by surfacing similar project data, organizing relevant pursuit history, and helping teams make informed, timely decisions during early bid evaluation
These visual communicators turn ideas into polished, on-brand layouts and graphics. Joist AI helps by keeping content structured and editable, reducing back-and-forth over text changes and allowing designers to focus on high-impact visuals.
In the context of AI, hallucination refers to when a language model generates content that sounds plausible but is factually incorrect or entirely made up. This can include invented project names, inaccurate firm credentials, or fabricated client quotes. In high-stakes documents like AEC proposals, hallucinations can damage credibility and lead to disqualification. Joist AI reduces hallucination by using retrieval-augmented generation (RAG) to ground responses in your firm’s actual project history, resumes, and past proposals.
Hit rate is the ratio of proposals won to those submitted, often expressed as a percentage. It's a critical metric for marketing and business development teams to track the effectiveness of their pursuit efforts. A rising hit rate may reflect stronger qualification, messaging, or targeting strategies. Conversely, a low hit rate may signal the need to reassess go/no-go decisions or improve proposal quality.
HITL means keeping humans involved in the AI process, especially for tasks that require judgment, creativity, or compliance. Joist AI is designed to augment—not replace—marketers, SMEs, and proposal managers. It gets you to a quality first draft so your team can focus on the polish.
An image library is a curated collection of photos, renderings, and graphics used in proposals, presentations, and marketing materials. For AEC firms, this might include project photography, team headshots, drone footage, or architectural illustrations. A well-maintained image library improves visual storytelling and reduces production time. Joist AI functions as a centralized image library by storing and indexing visual assets alongside narrative content—making it easy to search, retrieve, and reuse the right visuals for any pursuit.
Inference is the process of generating an output from a trained AI model. Every time Joist AI writes a resume blurb, summarizes a scope, or answers a compliance question, it's performing inference—applying its training and context to deliver a useful result.
Internal review is the stage in the proposal workflow where content is reviewed by subject matter experts, technical staff, or leadership. This step ensures the proposal is accurate, compliant, and strategically aligned. A thorough internal review process often includes checks for tone, grammar, completeness, and differentiators. It’s an essential part of producing competitive, professional submissions. Joist AI streamlines internal review by generating strong first drafts, reducing repetitive edits, and making it easier for reviewers to focus on strategic refinements rather than basic formatting or content assembly.
Internal review is the stage in the proposal workflow where content is reviewed by subject matter experts, technical staff, or leadership. This step ensures the proposal is accurate, compliant, and strategically aligned. A thorough internal review process often includes checks for tone, grammar, completeness, and differentiators. It’s an essential part of producing competitive, professional submissions. Joist AI streamlines internal review by generating strong first drafts, reducing repetitive edits, and making it easier for reviewers to focus on strategic refinements rather than basic formatting or content assembly.
A knowledge graph, or knowledge hub, organizes facts and relationships between concepts—like how a client, project, and location connect. In AEC, this structure helps teams understand the bigger picture, not just isolated details. It can support more strategic content reuse and better insight discovery. Joist AI uses this concept behind the scenes to understand relationships and improve content suggestions. It’s how Joist knows which projects are most relevant to a new pursuit.
Knowledge Managers oversee the organization, maintenance, and accessibility of a firm’s internal content—ranging from project descriptions and resumes to boilerplate language and proposal assets. Their goal is to ensure that valuable information is easy to find, reuse, and update across teams and pursuits. In AEC firms, this role is essential to reducing redundancy, improving proposal quality, and scaling institutional knowledge. Joist AI empowers Knowledge Managers by auto-tagging content and making libraries semantically searchable—so high-value assets are surfaced when and where they’re needed most.
A large language model is a type of AI trained to understand and generate human language. It learns by processing vast amounts of text—like books, websites, and documents—so it can recognize patterns, predict responses, and write content that sounds natural. The “large” refers to the billions of internal connections (or parameters) it uses to produce intelligent, context-aware output. In Joist AI, an LLM powers everything from resume blurbs to detailed project approaches—helping teams write faster, smarter, and with less effort.
Latent space is a kind of “mental map” that AI models use to understand how different ideas and concepts relate to each other—even when they’re not spelled out exactly the same. For example, the model might recognize that “green building” and “LEED certification” are connected because they show up together in similar contexts. This hidden layer of understanding helps the AI go beyond keywords to find meaning. Joist AI uses latent space to match prompts with the most relevant content in your library—surfacing examples that fit even when the wording is different.
A letter proposal is a brief, informal document used to propose work—often for smaller projects, change orders, or follow-on services with existing clients. It typically outlines the scope, timeline, and value of the work in a concise format, without the formality of a full RFP response. Despite its simplicity, a strong letter proposal still needs to be clear, professional, and aligned with the client’s expectations. Joist AI accelerates letter proposal creation by pulling relevant scope language, prior project context, and team qualifications—helping firms respond quickly while maintaining quality and consistency.
Machine learning is a branch of artificial intelligence where systems improve performance by learning from data, rather than being explicitly programmed. It powers everything from spam filters to recommendation engines—and in Joist AI, it helps understand proposal structure, identify relevant content, and generate high-quality responses. By learning from your firm’s past proposals, resumes, and narratives, Joist can improve over time, offering smarter suggestions and more personalized outputs with each use.
Marketing Coordinators handle the executional side of pursuit and content support—gathering resumes, updating project sheets, and ensuring materials are current. With Joist AI, they can instantly access approved content, auto-generate draft narratives, and surface resume and project examples by keyword or tag—giving them more time to focus on visual polish and proposal quality.
Marketing Managers in AEC firms oversee brand positioning, pursuit strategy, and content development. They manage both strategic direction and day-to-day operations, bridging the gap between BD, technical staff, and creative teams. Joist AI gives them visibility into active pursuits, simplifies oversight of content libraries, and helps maintain consistency across everything from SOQs to interview slides.
M&A refers to the process of two companies combining (merger) or one acquiring another (acquisition). In the AEC industry, M&A activity can be driven by market expansion, talent acquisition, service diversification, or geographic growth. For marketing and proposal teams, these transitions can create content challenges—like rebranding, integrating project histories, or aligning narratives.
A Model Context Protocol (MCP) defines how information is packaged and delivered to an AI model during a session. It determines what the AI should remember, prioritize, or ignore based on the task at hand. This includes things like user instructions, past interactions, relevant content, and system prompts. In Joist AI, a strong MCP ensures that responses stay consistent, focused, and aligned with both the prompt and the firm’s standards—especially during complex, multi-step proposal workflows.
Fine-tuning is the process of taking a general-purpose AI model and training it further on specialized, domain-specific data—like AEC proposals, resumes, or project descriptions. This helps the model learn industry language, firm-specific phrasing, and the structure of typical responses. In Joist AI, fine-tuning ensures that the content it generates reflects your firm’s voice, vocabulary, and standards. It’s one of the key ways the platform captures and scales your institutional memory.
Multi-modal AI refers to systems that can understand and generate content across multiple types of input—such as text, images, video, or audio. This means the AI isn’t limited to just reading and writing words; it can also interpret visuals or sounds to provide more complete responses. While Joist AI is currently focused on written content, future multi-modal capabilities could allow it to support diagrams, org charts, annotated plans, or even image-based project tagging. It’s the next step toward AI that sees and understands the way humans do.
Natural language processing is a subfield of AI focused on enabling machines to understand, interpret, and generate human language. It powers much of what Joist AI does—from summarizing RFQs to writing project narratives. NLP helps the system understand context, intent, and tone so that content reads like it was written by your team. The better the NLP, the more natural and accurate the output.
A one-shot prompt includes one example to guide the AI’s behavior. This helps it understand the tone, structure, or format you’re looking for. It’s a quick way to nudge the model in the right direction. It's useful when you want a lightweight, consistent approach to content generation.
Example: In Joist AI, a user might paste in one sample resume blurb for a structural engineer into the Chat feature—then prompt the AI to “Write a similar blurb for Sarah Martinez using her attached project history.” This guides tone and structure without requiring multiple examples.
Partners and Principals are senior leaders responsible for guiding firm strategy, building client relationships, and often delivering high-impact projects. In many AEC firms, they play a dual role—leading technical work while also driving business development and pursuit efforts. Their time is limited, but their influence is critical, especially when shaping project approaches, interviews, or client messaging. Joist AI supports Partners and Principals by making it easy to access project history, generate content with minimal effort, and contribute meaningfully to proposals without disrupting their day-to-day responsibilities.
A people-project matrix is a tool used by AEC marketing teams to map staff experience to specific projects—often for assembling resumes, staffing plans, or proposal graphics. It shows which individuals worked on which projects, and in what capacity, helping identify the best fit for new pursuits. Maintaining this matrix manually can be tedious and error-prone. Joist AI simplifies it by extracting team history from past proposals and making it searchable, so the right names and roles surface instantly.
Practice Area Leaders oversee specific disciplines or service lines within the firm—such as transportation planning, environmental engineering, or healthcare design. They are responsible for technical quality, staff development, and market differentiation within their area of expertise. In pursuit work, they contribute subject matter insights, develop strategic messaging, and ensure that content reflects the latest thinking and best practices. Joist AI supports Practice Area Leaders by capturing their institutional knowledge, surfacing relevant projects tied to their specialty, and enabling consistent, scalable messaging across multiple pursuits.
Preconstruction refers to the planning phase of a project that happens before any physical work begins. It includes estimating, scheduling, value engineering, constructability reviews, and bid preparation. For marketers and pursuit teams, it’s also a critical window to develop relationships, craft strategy, and shape proposals. Joist AI supports preconstruction efforts by helping teams create tailored content that aligns with early-stage decision drivers—like risk mitigation, team experience, and project delivery strategy.
Procurement refers to the structured process organizations use to acquire goods and services—often through RFPs, RFQs, or other formal solicitations. In the AEC industry, procurement is how public and private clients select design, engineering, and construction partners. It’s governed by specific rules, deadlines, and evaluation criteria. Joist AI helps firms navigate procurement by quickly interpreting solicitation requirements and generating compliant, compelling responses at scale.
The project approach is a key section in AEC proposals that outlines how a firm plans to deliver on the client’s objectives. It typically includes methodology, phasing, communication, risk management, and team roles. A strong project approach doesn’t just explain what you’ll do—it builds trust by showing you understand the client’s priorities. Joist AI helps teams craft project approaches by drawing from past successes, tailoring language to the specific opportunity, and aligning strategy with proposal themes.
Project Managers oversee the planning, coordination, and execution of projects—serving as the primary point of contact between clients, teams, and stakeholders. In architecture and engineering firms, they manage scope, schedules, budgets, and design deliverables, ensuring alignment with client goals and regulatory requirements. In construction, Project Managers focus more on logistics, subcontractor coordination, and delivery timelines. Across all disciplines, their leadership and communication skills are essential. For proposals, they often contribute team structure narratives, project execution plans, and client relationship insights. Joist AI helps Project Managers by auto-generating resumes, surfacing past project details, and streamlining their content input—so they can provide critical input without pulling away from project delivery.
A prompt is how you talk to AI. It’s a sentence, a question, or a set of instructions you give the model to get the response you need. The clearer and more specific your prompt, the better the result. Think of it like setting the stage before the performance begins.
Prompt engineering is the art (and science) of crafting effective inputs for AI. It’s about choosing the right words, structure, and examples to steer the model’s output. In proposal automation, this might mean guiding AI to sound on-brand, stay accurate, and follow your preferred format.
A proposal is a formal document that outlines a firm's qualifications, understanding of a project, and approach to delivering it—often in response to an RFP or RFQ. In the AEC industry, proposals are a cornerstone of business development, combining technical narratives, resumes, project experience, and visuals. They require coordination across marketers, subject matter experts, and leadership. Joist AI streamlines proposal development by surfacing relevant content, structuring responses, and reducing turnaround time—helping teams submit more proposals with less stress.
A Proposal Coordinator supports the proposal process through scheduling, formatting, content gathering, and administrative tasks. They ensure all pieces come together on time and meet submission requirements. Joist AI lightens their load by accelerating content assembly, flagging compliance gaps, and keeping everything searchable and organized. The result: fewer last-minute scrambles and more confident submissions.
This role focuses on improving the systems, tools, and processes that support proposal development. Joist AI aligns directly with this goal by automating repetitive tasks, standardizing templates, and improving collaboration
Proposal Writers translate technical input into compelling, compliant, and client-focused narratives. Joist AI supports them with structured prompts, content libraries, and examples from prior proposals—allowing them to spend less time formatting and more time storytelling.
A pursuit is the process of targeting and responding to a specific project opportunity—usually kicked off by an RFP or RFQ. It includes everything from go/no-go discussions and team selection to content creation, interviews, and follow-up. In AEC, managing pursuits well is the key to growth—but it’s often reactive and time-consuming. Joist AI brings structure and intelligence to pursuits by centralizing content, streamlining collaboration, and helping teams respond faster and smarter.
Pursuit Strategists shape an AEC firm’s approach to winning work—crafting win themes, guiding messaging, and positioning teams before and during a proposal. Joist AI helps them align narratives to client priorities, identify reusable themes from past wins, and automate first drafts that match their strategy. It’s like having a research assistant built into your strategy playbook.
RAG combines two powerful things: search and generation. First, it retrieves relevant documents from a database. Then, it uses a language model to generate a response using that data. It's like giving AI a memory boost, so answers stay accurate and grounded in your own materials. Great for proposal writing, where facts matter.
Redlines refer to edits or changes made to a document, typically tracked using markup tools in Word or PDF editors. In AEC proposals, redlines are part of the internal review process to ensure accuracy, consistency, and quality. Managing redlines efficiently helps teams maintain version control and meet tight deadlines. Too many late-stage redlines can signal gaps in planning or alignment.
An RFI is a preliminary document issued by a client to gather general information about a firm’s capabilities, experience, or approach—typically before a formal RFP or RFQ. It helps clients understand the market landscape and potential partner options. While less detailed than a proposal, RFIs are still important touchpoints that shape perception and future opportunities. Joist AI helps AEC teams respond to RFIs quickly and effectively by organizing past responses, surfacing relevant content, and generating polished narratives that align with client interests.
An RFP is a formal solicitation issued by an owner or client to invite qualified firms to submit a proposal for a specific project or service. It includes detailed requirements, evaluation criteria, and often contractual terms. Responding to RFPs is a core function of AEC marketing and pursuit teams, and involves compiling qualifications, narratives, resumes, and project experience. Success in RFP responses often hinges on speed, accuracy, and the ability to tailor messaging to each client’s unique priorities. Joist AI supports this process by extracting requirements, surfacing relevant content, and generating well-structured responses that help teams submit faster and with greater confidence.
An RFQ is issued to pre-screen firms based on their qualifications before they are invited to submit a more detailed proposal. It typically asks for information about firm experience, key personnel, relevant past projects, and technical expertise. In public procurement, RFQs are used to ensure only capable firms move forward in the process. A strong RFQ submission builds credibility and sets the stage for a compelling proposal. Joist AI streamlines this process by pulling in tailored resumes, past project narratives, and firm qualifications from your content library—helping teams submit polished, compliant responses faster.
Revenue intelligence software analyzes deal data, sales activity, and buyer behavior to provide insights that improve forecasting, pipeline health, and strategy. In traditional B2B SaaS, it’s used to understand why deals are won or lost. For Joist AI, revenue intelligence is evolving into pursuit intelligence—focused on understanding pursuit quality, response efficiency, and win themes across AEC proposals. It helps firms make smarter bid/no-bid decisions, identify patterns in client preferences, and focus effort where it’s most likely to pay off.
Role prompting assigns the AI a persona—like “proposal writer” or “civil engineer”—to shape its voice and decision-making. This helps generate more relatable, on-brand content. It's especially effective when tailoring communication to different audiences or subject matter areas. Use role prompting in Joist AI to ensure responses reflect the tone, vocabulary, and perspective of AEC professionals—producing content that feels authentic, whether it's technical, strategic, or client-facing.
Sales enablement refers to the tools, content, and processes that help business development and seller-doer teams engage prospects and win work. In the AEC industry, this includes qualifications decks, project examples, proposal narratives, and interview prep materials. Effective sales enablement ensures teams have quick access to persuasive, on-brand content when they need it most. Joist AI supports sales enablement by surfacing relevant content instantly, helping teams tailor messaging to each client, and reducing the manual lift of creating pursuit materials from scratch.
A security questionnaire is a standardized set of questions issued by a client to assess your firm's data privacy, cybersecurity practices, and IT policies. In the AEC industry, these are becoming more common—especially when working with government clients, large institutions, or cloud-based platforms. They often require input from IT, legal, and operations teams. Joist AI helps streamline responses by referencing previously approved answers, identifying relevant documentation, and reducing the time needed to complete repetitive or technical sections.
A seller-doer is a technical expert—like a project manager or principal—who also plays a business development role. They’re often on the front lines of client conversations, juggling delivery and pursuit efforts. Joist AI makes their lives easier by helping generate or refine pursuit content quickly, allowing them to focus on relationships and technical excellence.
Shortlisting is the process where clients narrow down received proposals to a smaller group of finalists. Being shortlisted typically leads to an interview, pricing round, or further evaluation. It’s a key milestone in the pursuit process and a signal that a firm's messaging resonated. Many firms track how often they are shortlisted as a measure of brand strength and proposal effectiveness. Joist AI helps improve shortlist rates by elevating narrative clarity, aligning content to client priorities, and enabling faster, more tailored responses.
An SOQ is a standalone document or submission in response to an RFQ. It highlights a firm's experience, project portfolio, leadership team, and core competencies. SOQs are foundational marketing tools for AEC firms seeking to win new business, especially in government and institutional sectors. They must be concise, visually clean, and tailored to the client's priorities. Joist AI helps streamline SOQ development by organizing reusable content, suggesting relevant projects and resumes, and formatting materials to match client requirements—making high-quality submissions faster and more consistent.
The Strategic Growth Executive is responsible for identifying and driving new avenues of revenue—whether through geographic expansion, market diversification, new services, or partnerships. In AEC firms, this role requires both a high-level view of industry trends and a deep understanding of internal capabilities and differentiators. These leaders work across business development, marketing, and operations to position the firm ahead of the curve. Joist AI supports their efforts by accelerating content creation for new markets, helping tailor messaging for emerging sectors, and enabling teams to respond to more opportunities without sacrificing quality.
Structured data is highly organized and formatted so it can be easily entered, stored, and searched within relational databases or spreadsheets. This includes things like client contact lists, project schedules, or timesheets. It’s the kind of data that traditional software tools excel at processing. Structured data is useful on its own, but when combined with unstructured data, it offers a more complete picture for decision-making. Joist AI draws on both structured and unstructured data to deliver smarter, more context-aware content—linking people, projects, and pursuits in ways that drive clarity and consistency.
Structured retrieval pulls data from organized sources, like a database or tagged content library. It ensures access to trusted, high-quality information and reduces the risk of errors or omissions. This approach is critical in fields like AEC where compliance and accuracy are non-negotiable. For Joist AI users, this means the platform isn’t guessing—it’s referencing your actual project history.
A style guide is a set of rules that defines how content should look and sound—covering everything from grammar and tone to fonts, logo usage, and voice. In AEC marketing, a style guide ensures consistency across resumes, project sheets, proposals, and branding materials. It’s especially important for firms with multiple offices or contributors. Joist AI can be configured to follow your firm’s style guide, automatically applying rules and tone preferences to every piece of generated content.
A subject matter expert is a professional with deep knowledge and hands-on experience in a specific discipline—such as structural engineering, environmental design, or transportation planning. In the AEC proposal process, SMEs play a critical role by contributing technical content, reviewing narratives for accuracy, and shaping strategy. They often juggle billable work and pursuit support, which makes their time especially valuable. Joist AI helps maximize that value by generating strong first drafts, so SMEs can spend less time wordsmithing and more time ensuring content is technically sound.
These are behind-the-scenes rules that shape how AI behaves. They control tone, length, style, and what kind of content to prioritize. Think of system instructions as a creative brief the AI follows every time it writes.Example: In Joist AI, system instructions might direct the AI to “write in a confident, professional tone,” “limit responses to 150 words,” or “always include the project name and client in resume blurbs.” These instructions ensure consistency across users and output—no matter who’s prompting the system.
System prompting sets the tone and behavior rules for the AI before any task begins. It’s a way of telling the model: “Here’s who you are, here’s what you’re doing, and here’s how you should sound.”
Example: In Joist AI, a system prompt might tell the model, “You are a proposal writer at a civil engineering firm responding to a municipal RFQ. Use a confident and concise tone. Prioritize clarity and compliance.” This guidance ensures the AI responds in a way that aligns with industry expectations and firm standards.
Tagging is the practice of labeling content with keywords or attributes to make it easier to organize and retrieve. In AEC marketing, tagging might include project type, location, client name, service line, or staff involvement. It’s the backbone of content reuse—especially when firms manage thousands of files across teams. Joist AI uses intelligent tagging to automatically classify resumes, project descriptions, and companies, making your content library searchable and your best material easy to find.
In AI systems, a task is a defined action or goal—like summarizing a project, creating a boilerplate, or scoring an RFP. Breaking work into tasks makes it easier for AI to execute accurately and for users to trust the results.
Technical Leads and Directors are responsible for the oversight, quality, and innovation of design and engineering solutions within their area of expertise. In AEC firms, they serve as both project champions and firm-wide thought leaders—often guiding methodology, QA/QC processes, risk mitigation strategies, and technical narratives in pursuits. Their input is critical in demonstrating credibility and differentiating the firm’s approach. Joist AI supports these leaders by capturing and reusing their insights across proposals—ensuring their expertise shapes narratives without requiring them to rewrite from scratch every time.
Templated guidance provides a consistent structure for content generation—offering prompts, formatting rules, and tone expectations that shape the AI’s output. It ensures that content doesn’t just sound good—it fits seamlessly into your firm’s standards. In AEC proposals, where precision, consistency, and compliance are critical, templated guidance helps teams work faster without sacrificing quality. Joist AI uses templated guidance to draft resume blurbs, project descriptions, and methodology sections that follow firm-specific patterns—making it easy to maintain brand alignment while reducing the burden on marketers and SMEs.
A tender is a formal invitation from a client asking contractors or consultants to submit a bid for a project. It often includes technical specifications, contract terms, and evaluation criteria. While “tender” is more commonly used outside the U.S., particularly in the UK, it functions similarly to an RFP in many contexts. Joist AI helps firms respond to tenders efficiently by interpreting requirements, aligning content, and automating repetitive sections—so teams can focus on strategy, not formatting.
Tokenization is how a language model breaks down text into smaller pieces—words, characters, or sub-words—before processing it. It affects how much content the model can understand at once and plays a role in speed and accuracy. Understanding tokenization helps users work around length limits in prompts and documents.
Training data is the information used to teach a machine learning model how to perform a task. For Joist AI, that might include thousands of past proposals, resumes, and project descriptions. High-quality training data results in more accurate, relevant, and brand-aligned outputs.
Transformer architecture is the underlying design of modern language models, including those used by Joist AI. It allows models to process context and relationships across long pieces of text, making them capable of writing entire proposals, not just sentences. It’s the tech that makes sophisticated language understanding possible.
Unstructured data refers to information that doesn’t follow a clear format, such as emails, PDFs, proposal documents, videos, or photos. It can’t be easily organized into a table or database, making it harder to search, manage, or analyze with traditional tools. In the AEC industry, most historical knowledge—like past RFP responses or project narratives—lives in this form. AI tools thrive on making sense of this chaos by finding patterns and insights that humans might miss. If you’ve ever spent hours hunting for that one quote from a past proposal, you’ve felt its pain. Joist AI organizes unstructured data into searchable, contextual content—making firm knowledge easy to find, reuse, and trust.
A vector database stores and retrieves embeddings—numeric representations of text or other data. It’s the backbone of semantic search. Joist AI uses a vector database to instantly surface content that’s similar in meaning, not just in keywords, making search intuitive and fast.
A win strategy—or win theme—is the core message that communicates why your firm is the best choice for a specific project. It connects your team’s strengths to the client’s goals, framing your differentiators in terms that resonate. Great win themes are clear, evidence-backed, and woven throughout the entire proposal—from the cover letter to the project approach. Joist AI helps capture and reinforce win strategies by suggesting language aligned with the pursuit’s strategic positioning and pulling in proof points from past wins.
White-box AI refers to systems that offer visibility into how decisions are made and where information comes from. This transparency is especially valuable in industries like AEC, where accuracy and accountability are critical. Joist AI is built with white-box principles—grounding responses in retrievable source content, tracking prompt history, and making reasoning visible when needed. It empowers teams to trust AI-generated content while staying in control of what gets submitted.
A zero-shot prompt gives the AI no examples—just instructions. It relies entirely on its own training to generate a response. It’s fast and flexible but less precise if the prompt isn’t well crafted. This method is best suited for general or exploratory tasks.Example: In Joist AI, a zero-shot prompt might be as simple as “Write a 100-word project summary for a wastewater treatment plant upgrade,” relying on the AI’s general knowledge and training to generate a first draft from scratch.
These are your trusted building blocks—ready-made content you reuse often. From company overviews to project win themes, this content saves time and ensures consistency. When properly managed, these narratives help maintain brand voice and reduce the effort needed to respond to common RFP questions.
