SOLUTIONS · PROPOSAL DRAFTING
Professional services
AI proposal drafting from past engagements means an agent finds the engagements most like the new opportunity, then builds a first draft from what your firm actually did: the scope that was delivered, the team that staffed it, the case summary and the fee that was billed. OutcomeCatalyst does this for consulting, advisory, engineering and accounting firms by connecting SharePoint, the CRM (DealCloud or Salesforce) and the time and billing system (often BillQuick) into one governed context layer. A partner reviews and signs every proposal, and OC delivers and runs the system, so the firm does not need a data team.
The problem, in the words we hear from firms
"We did almost this exact job for a hospital system in 2021. Who has the proposal?"
That question goes out on a Thursday afternoon, with the response due Monday. Someone digs through SharePoint for a folder named after a client code. The partner who led the job is on a plane. The BD coordinator copies a proposal that looks close and starts editing. Nobody checks what that job really cost, so the new fee is anchored to the old quote, not the old actuals.
The result is a proposal built from whatever was easiest to find. Bios are two years stale. And the pricing repeats the write-off that hurt realization last time.
The volume makes it worse. In the 2025 RFP Response Trends and Benchmarks Report, published by Loopio with the Association of Proposal Management Professionals (APMP), 1,544 respondents reported spending an average of 25 hours writing a single RFP response, and management consulting firms said RFPs drive almost half of their revenue (Loopio and APMP, 2025). For a firm without a proposal department, those hours come out of partner and senior manager time.
What a good first draft needs, and where it lives
Most AI proposal writing tools start from a library of approved answers. That works well for repeat questionnaires. AI proposal writing for consulting firms is a different job, because its most persuasive parts are specific to past work, and that evidence is scattered.
Part of the proposal | Where it usually lives | What goes wrong when it is copied by hand |
|---|---|---|
Relevant past engagements | CRM (DealCloud or Salesforce) and SharePoint | The firm cites the engagement someone remembers, not the closest match |
Team bios | SharePoint, HR files, old proposals | Bios list old titles and miss the work people have done since |
Case summaries | Deliverables and closing memos in SharePoint | The summary describes what was sold, not what was delivered |
Staffing plan | Time and billing records (BillQuick or similar) | The plan assumes the hours that were quoted, not the hours that were worked |
Fee and pricing history | Billing system and pricing spreadsheets | The fee repeats an underpriced quote because realization was never checked |
A useful draft needs most of those systems at once. That is the core of our position: proposal drafting should be grounded in billing actuals and staffing history as well as past proposal text. Past text tells you how the firm described its work. Billing data tells you what the work took.
What the proposal agent does, step by step
Reads the opportunity. An RFP, a warm request or a partner's meeting notes goes to the agent, which pulls out client type, industry, situation, scope, deadlines and evaluation criteria.
Finds the closest past engagements. The agent searches the brain, the knowledge graph OC builds over your systems, for engagements that match on industry, situation, size and scope. Each match links to its CRM record, its SharePoint documents and its billing project, so the agent knows which proposal led to which job and what that job earned.
Assembles the team. It suggests people who staffed similar work, based on hours actually logged, and pulls their current bios. Where a bio is out of date, it flags the engagements that should be added.
Writes case summaries from delivered work. Summaries come from deliverables and closing documents, with links back to each source so the reviewing partner can check the claim.
Builds a pricing view. For each comparable engagement it shows the quoted fee, the billed amount, hours by role and any write-down, then proposes a fee range with the reasoning laid out. The partner sets the number.
Drafts the proposal in your format. The first draft follows your template and house style, with every factual claim tied to a source record.
Learns from the edits. What the partner changes or deletes trains the next draft, so the agent learns to choose precedent and scope the way your veteran partners do.
Pricing from what you billed, not what you quoted
Fixed-fee and capped engagements are where realization leaks, and the leak repeats because the next proposal copies the last quote. With the billing system connected, a partner pricing a new diligence engagement sees what the last three comparable jobs were quoted, what they billed, which roles ran over and which phase absorbed the extra hours.
The agent does not set the price. It puts the firm's own history next to the draft, in front of the person who does.
What stays human
The go or no-go call. The agent can summarize fit against past work. Whether to bid is a partner decision.
Every fee. The agent proposes a range with evidence. A partner sets the price.
Partner review before anything leaves the firm. No draft goes to a prospect without sign-off.
Conflicts and independence. The agent may surface related matters it finds, but your existing conflicts process stays the authority.
A firm sells judgment. An unreviewed draft is a risk, however polished.
What to measure, including win rate
Name the unit before anything is built. Projects that cannot say what they will count rarely show a return, which is the pattern we describe in why AI pilots stall before EBIT. For proposal drafting, the countable units are usually these:
Partner hours from opportunity received to first draft, per proposal.
Proposals drafted from a cited precedent engagement, per month.
Win rate on proposals drafted with the agent versus those written the old way, tracked by practice and by deal size. With each outcome recorded against the CRM opportunity, the comparison stays current without a separate spreadsheet.
Realization on won fixed-fee work where the fee was set from billing history.
For context, the same Loopio and APMP survey put the average reported win rate at 45%, up from 43% the year before (Loopio and APMP, 2025). Your own baseline, measured before go-live, matters more.
How this compares with Loopio and Responsive
When people search for RFP response AI for professional services, they mostly find dedicated RFP response software. If your firm answers a high volume of RFPs, security questionnaires and due diligence questionnaires, that software is a strong choice, and you should look at it.
Loopio calls itself RFP software "Built for the People Behind Every RFP, SQ, DDQ." Its AI drafts answers from your approved content library with citations and confidence scores and recommends subject matter experts from past contributions; it only generates answers from content the user has permission to see (Loopio, 2026). It lists integrations with Salesforce, Microsoft Dynamics 365, HubSpot, SharePoint, Google Drive, OneDrive, Slack and Teams, among others (Loopio, 2026).
Responsive, formerly RFPIO, describes its product as "fast, trusted answers that win deals," with an AI content library guided by subject matter experts and AI agents for response work (Responsive, 2026). Its Salesforce integration lets teams launch RFPs, security questionnaires and DDQs from Salesforce, track progress and publish finished packets back to the opportunity record (Responsive, 2026).
Both are built around a curated library of approved answers, with years of refinement behind that workflow. If your problem is answering the same 200 questions faster, they fit well.
Where a consulting or advisory firm may need more: neither vendor lists time and billing systems among its integrations on the pages we checked, so pricing history and actual staffing are not part of the draft unless someone adds them to the library by hand. And a library has to be maintained, which firms without a proposal team rarely do. OC takes a different route. It connects the systems where the evidence already lives, links each engagement to its documents, people and invoices, and is delivered and run for you. Some firms will use both: an RFP tool for questionnaires, and OC for the engagement-specific parts of a proposal.
How a project starts
It starts with a strategy call. We ask how proposals get written today, which systems hold past engagements and billing, and what you would count as success. If there is a fit, we scope drafting for one practice group and put the agent in front of a few partners on live opportunities. Proposal drafting is one of several jobs OC runs for firms; the wider picture is on our page on AI for professional services firms.
Frequently asked questions
Can AI draft a new proposal from our past engagements, pricing and case studies?
Yes, if it can read the systems where those live. The agent finds comparable engagements in your CRM and SharePoint, pulls bios and case summaries, shows what similar work billed, and drafts in your format. A partner edits and approves it.
Do we need a data team to run this?
No. OC builds and operates the connections, the brain and the agent. Your team tells us what a good proposal looks like and reviews what the agent drafts. It is designed for firms with an IT lead and no data engineers.
Can it connect to SharePoint, DealCloud, Salesforce and BillQuick?
Those are the systems we hear about most from consulting, advisory and accounting firms, and connecting the systems a firm already runs is the core of what OC does. Versions, hosting (cloud or on-premises) and access methods vary, so we confirm each connection on the first scoping call rather than promise it on a web page.
Will it reveal one client's details in another client's proposal?
Not if the rules say it cannot. Before the agent goes live, your firm decides which engagements can be named, which must be anonymized and which are off limits, and who can see what. Every draft is reviewed by a partner before it leaves. On security: OC is HIPAA-aligned and SOC 2 Type 2 aligned, with the formal audit underway and expected to complete before year-end.
We already use Loopio or Responsive. Do we need this?
Maybe not. If your proposals are mostly repeat questionnaires, your RFP tool may cover it. If your proposals depend on picking the right precedent engagements, staffing them and pricing from what similar jobs really cost, that evidence sits in your CRM and billing system, and OC connects it. The two can run side by side.
What happens after go-live if partners keep writing proposals the old way?
Then it failed, and we treat that as our problem. Usage and edit rates are measured from the first week, and heavily rewritten drafts go back into training. That is why OC runs the system after launch.
Why not build this ourselves?
Some firms should. If you have engineers who can maintain connections to four or five systems, keep permissions in sync and retrain the agent as your work changes, building can make sense. Most firms do not, and the maintenance costs continue long after launch. We cover both paths in buy vs build an AI context layer.
Sources
Loopio and APMP, "RFP Response Trends and Benchmarks Report 2025" (survey of 1,544 respondents, data analyzed by Qualtrics), accessed 6 October 2026: link.loopio.com/hubfs/loopio-trends-report-2025-digital.pdf
Loopio, homepage (2026), accessed 6 October 2026: loopio.com
Loopio, AI features (2026), accessed 6 October 2026: loopio.com/ai
Loopio, integrations (2026), accessed 6 October 2026: loopio.com/integrations
Responsive, homepage (2026), accessed 6 October 2026: responsive.io
Responsive, integrations (2026), accessed 6 October 2026: responsive.io/integrations
Responsive, Salesforce integration (2026), accessed 6 October 2026: responsive.io/integrations/salesforce
Book a strategy call to see this on your own firm.
Your systems, your documents, and what your people have been carrying around in their heads. Thirty minutes to see what an AI brain could look like in your company.
Book a strategy call
© 2026 OutcomeCatalyst