At a glance
- An agent can draft the repeatable parts of an RFP or SF330 response from your own records; licensed professionals and BD leads decide every qualification and staffing claim.
- The workflow table splits five components (Go/No-Go, Section E resumes, Section F project narratives, the Section G cross-check and final QA) into what the agent drafts and what a person reviews.
- Draft quality depends on the staff and project records behind it, so fix stale records before you automate.
- Vantagepoint documents its own proposal and SF330 features; ask any AI vendor to show written documentation of its integration rather than assuming one.
- A won proposal holds the clean data a project setup needs; hand it over deliberately.
AI proposal automation for A&E firms is the use of an AI agent to assemble first drafts of repeatable proposal parts, such as resumes, project descriptions and cross-checks, from a firm's own records, with a named person approving each part before submission.
This article covers RFP response automation for A&E and AEC firms: drafting and review for RFP responses and the SF330, the federal Architect-Engineer Qualifications form. Design, engineering judgment and stamped work stay with your licensed professionals.
Where RFP and SF330 preparation actually burns hours
The SF330 asks for the same facts in many places. The form says "Complete one Section E for each key person" and, for Section F, "Present as many projects as requested by the agency, or 10 projects, if not specified. Complete one Section F for each project." [1] Section G is a matrix tying people to example projects. Time these steps on your next response:
- Retyping the same resume into a new layout.
- Chasing current education and registration details from each person.
- Rewriting project descriptions so they are relevant to this contract's scope.
- Reconciling names, roles and firms between Sections C, E, F and G.
- Final assembly, formatting and proofreading.
For a sense of scale, a November 2023 Federal Register notice from the Department of Defense, GSA and NASA lists the SF 330 information collection (Parts I and II combined) at 682 respondents, 2,728 total annual responses and 79,112 total burden hours. [2] Dividing the published totals gives about 29 hours per response. That is the government's burden estimate, not any firm's measured time, and your hours vary by solicitation and team.
The proposal workflow table: what an agent drafts and who reviews it
Use this table as a working checklist. The middle columns split drafting from judgment, and the last names the record the draft must match. Go/No-Go is the decision whether to pursue a solicitation.
| Proposal component | What the AI agent drafts | What a person reviews and decides | Record the draft must match |
|---|---|---|---|
| Go/No-Go analysis | A summary of the solicitation (scope, due date, evaluation criteria, required forms) set beside your past projects and team availability, with gaps listed. | The BD lead and a principal decide whether to pursue, using the firm's own criteria. The agent does not make the call. | Solicitation notice, pipeline and staffing records, past-project list. |
| Section E resumes | A draft for each named key person: role, experience, education, registration fields and relevant projects, taken from the staff record and tailored to the role. | The individual and their discipline lead confirm each fact. A licensed professional approves every technical qualification statement. | Staff database, the person's own license records, their confirmation. |
| Section F past-performance narratives | A brief description of each example project: scope, size, cost and why it is relevant to this contract, written from the project record. | The project manager of record checks scope, size, cost, dates and role. The BD lead picks the projects that show the team most clearly. | Project record, contract and closeout documents, owner contact. |
| Section G cross-check | A draft matrix of key personnel by example project, plus a list of mismatches in names, roles and firms across Sections C, E, F and G. | The proposal coordinator resolves each mismatch. The BD lead signs off on the final team. | Approved Sections E and F. |
| Final QA | A checklist run against the solicitation: required parts present, length limits, consistent names, unverified statements flagged. | A reviewer who did not write the section reads the whole package. The firm's authorized representative decides what is submitted. | The solicitation, the form and its instructions. |
Two rules make the table work. The agent drafts only from the named record and marks a blank as missing. A person, not the agent, approves each row. Our document review with human approval page describes the same pattern.
Can AI generate Section E and F resumes from our project database?
Yes, for the draft; not for the approval. An agent can read your staff and project records and propose Section E and F text, provided the records hold the fields the form asks for. A person must still confirm each fact.
Those fields are named on the form itself: for each key person, education (degree and specialization), current professional registration (state and discipline) and relevant projects [1]. For each example project, Section F asks for a brief description of the project and its relevance to this contract, including scope, size and cost. [1] If your database lacks a field, the agent has nothing to draft from.
- List the fields
Copy every Section E and F field into a list and mark where each lives today: staff system, project system or someone's file.
- Fix the source records
Correct stale education, registration and role entries, and add missing project scope, size and cost. The agent repeats whatever the record says.
- Draft from records only
Have the agent write each resume and project description from the named record, and mark any field it cannot find as missing.
- Review by the owner of the fact
Each person confirms their own resume. The project manager of record confirms each project. A licensed professional approves technical qualification statements.
- Write approved text back
Store the approved wording as the new master, so the next response starts from reviewed text.
The agent copies the registration field from your record and does not verify it. Someone with the license record checks it, and the firm decides how credentials are represented.
Does AI proposal software integrate with Deltek Vantagepoint?
That depends on the specific product, and only that vendor's written documentation can settle it. We have not verified any named AI proposal tool's Vantagepoint integration, and this article does not rank or describe vendors. What it can do is report what Deltek's own help pages say about Vantagepoint.
- On proposals in general, Deltek's help lists the ability to "Integrate records, fields, images, and other elements directly from the hubs, ensuring data accuracy and consistency." [3]
- On the SF330 module, it says "Most of the data that you enter in the SF330 form is in the Vantagepoint database." and "Many blocks in the SF330 are auto-populated or non-editable." [4]
- The same page states "Vantagepoint CRM Plus is required for the SF330 module." [4]
- On starting from a project, it says "However, the project record does not contain all of the information you need to fill out an SF330 form". [5]
- On pulling records into a proposal, it says "You can pull information directly from existing hub records into a new custom proposal." [6]
For outside applications, Deltek's 7.1 help says "You must create an API authorization for your application in order to access Vantagepoint through the API." [7] That describes Vantagepoint's own API access, not any AI product.
Where the native SF330 module already fills a block from your database, keep using it. An agent earns its place on what a form cannot fill: tailoring a narrative to a solicitation, flagging mismatches and drafting the Section G cross-check. We recommend asking each AI vendor these questions before planning around an integration.
- Which Vantagepoint versions and editions does the integration support, and where is that written down?
- Is access read-only, or can the tool write back to Vantagepoint records?
- Which records does it read: employees, projects, proposals, or all of them?
- Who holds the API credentials, and who rotates them?
- What happens to a draft when the underlying hub record changes?
The handoff from proposal to project setup
A won proposal already holds much of what a new project needs. The form carries the solicitation or project number, the proposed team and each person's role in the contract, and the project owner's information. [1] Retyping it when the project opens invites typos that travel downstream.
We recommend that the agent compile a project-setup sheet from the approved proposal: contract title and number, owner contact, team and roles as approved, and the scope description. The project manager then checks the sheet against the signed contract. Where the two differ, we suggest treating the signed contract as the reference and correcting the proposal data.
- Record the outcome of every pursuit (won, lost or no-go) and the reason, in the same system as the proposal, so win-rate review draws on real data from your CRM and accounting records.
- Stop at award. Work-in-process accounting and billing formats are a separate workflow and are outside this article.
Our AI agent implementation page explains how a scoped workflow like this is built and handed over.
How do you compare AI proposal tools and measure time saved?
There is no single best AI tool for SF330 proposals, and we do not rank vendors. Compare on fit instead: does the tool draft from your own records, show each draft's source record, route every part to a named approver, and document its integration claims in writing?
No independent, dated study of time saved is recorded here; treat vendor figures as vendor claims. Measure your own. Time your last few responses by component: Go/No-Go, each Section E, each Section F, the Section G cross-check, final QA and review of every draft. If one Section E takes N minutes and a response has K key people, Section E costs N times K minutes. Count review minutes on the agent's draft too; they offset the saving.
When this approach does not apply
- The native SF330 module already fills the blocks you need from clean records, and your narratives rarely change.
- Your staff and project records are incomplete or out of date. Fix them first; an agent only repeats them faster. See when not to use an AI agent.
- No one is available to review each draft. Without a named reviewer for every row of the table, do not automate that row.
- You are unsure which workflow to start with. The 360° AI Blueprint is where we start when the starting point is not clear.
Frequently asked questions
What is the best AI tool for writing SF330 proposals?
There is no single best AI tool for SF330 proposals, and this article does not rank vendors. Compare on whether the tool drafts from your own staff and project records, shows the source of each statement, routes every part to a named reviewer, and documents its integrations in writing.
How long does it take to prepare an SF330 proposal manually?
The federal paperwork-burden totals for the SF 330 (Parts I and II combined) are 79,112 hours over 2,728 annual responses; dividing them gives about 29 hours per response. That is a government estimate, not a firm's measured hours, and real time varies with the solicitation and team. Time your own last few responses by component for a baseline.
Can AI generate Section E and F resumes from our project database?
Yes, as a draft. An agent can write Section E resumes and Section F project descriptions from your staff and project records, if those records hold the fields the form asks for. Each person and project manager must confirm every fact, and a licensed professional approves technical statements.
Does AI proposal software integrate with Deltek Vantagepoint?
It depends on the specific product, and only that vendor's own written documentation can confirm it. Ask each AI vendor for written documentation naming supported Vantagepoint versions, the records it reads and whether it can also write back to them.
How much time can AI actually save on A&E RFP responses?
No independent, dated study is recorded here, so treat vendor figures as vendor claims. Measure your own: time each component on recent responses, including review time on the draft. The saving is real only where review minutes are fewer than the drafting minutes removed.
Related reading
- Document review with human approval: preparing documents for a qualified person to approve.
- AI agent implementation: how a scoped workflow is built and handed over.
- 360° AI Blueprint: where to start when the first workflow is not clear.
- When not to use an AI agent: when a rule or existing tool is the better choice.
- How to measure human review workload: counting the review time drafts create.
- AI for professional services and A&E firms: how firms like yours approach agents.
Sources
- U.S. General Services Administration, Standard Form 330, Architect-Engineer Qualifications, Part I (Rev. 7/2021) (accessed 2026-10-29)
- DOD, GSA and NASA, Submission for OMB Review; Architect-Engineer Qualifications (SF-330), Federal Register (2023-11-14) (accessed 2026-10-29)
- Deltek, "Proposals", Vantagepoint Help (version 7.0) (accessed 2026-10-29)
- Deltek, "SF330 Proposals", Vantagepoint Help (version 7.1) (accessed 2026-10-29)
- Deltek, "SF330 Create from Project", Vantagepoint Help (version 7.1) (accessed 2026-10-29)
- Deltek, "Working with Hub Records in Proposals", Vantagepoint Help (version 7.1) (accessed 2026-10-29)
- Deltek, "Set Up Access to the Application Programming Interface (API)", Vantagepoint Help (version 7.1) (accessed 2026-10-29)
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