← case studies
Published
How a Media Team Cut RFP Prep From 3 Hrs to 15 Min
An enterprise sales team piped its past proposals into an AI that reads each RFP and drafts the reply — cutting prep from 3 hours to 15 minutes and clearing 76% of its backlog.
3+ hrs
Saved on every RFP
2–4 months
Implementation Time
$25K – $100K
Project Cost
the challenge
Every RFP needed example content, and this team built it by hand every single time. They had a library of past work sitting right there. But using it meant digging through it manually, finding the closest match, copying it, and reworking it for the new prospect.
It worked. It was just slow.
And here's the part that actually cost them: content was the thing capping their growth. Not demand. Not interest. They couldn't take on more RFPs because they couldn't turn content around fast enough to keep up. A library full of good work, and almost none of it was easy to reach.
what they built
We built a retrieval system on Claude, connected to a vector database.
First, we tagged every piece of content in the library with structured metadata: use case, industry, and content type. Then we made the whole thing searchable by meaning instead of just keywords.
When a new RFP comes in, the system reads what it's actually asking for, pulls the most likely matches from the library, checks each one for how well it fits, and returns the single best piece. Then it copies that piece and adapts it to the prospect.
And we plugged it straight into their CRM and content management system. It doesn't sit off to the side as a separate tool the team has to remember to open. It lives inside the software they already work in.
What used to be a blank page is now a near finished draft.
Here's the part most teams don't expect: nobody runs this. The moment an RFP lands in the CRM, the whole thing happens on its own. No one opens a tool. No one kicks it off. The draft is just there, waiting.
The build started with observation rather than specification: the team watched how the client actually handled RFPs, which surfaced content — not demand — as the real bottleneck. The first major decision was the metadata schema. Before tagging anything, they settled on the dimensions that mattered for matching an RFP to past work — use case, industry, and content type — because everything downstream depended on getting it right. Next, the full content library was tagged against that schema programmatically rather than by hand, then loaded into a vector database so it could be searched by meaning instead of keywords. The team then built the retrieval logic. Since the closest semantic match isn't always the best fit, they added an evaluation step that checks each candidate against the specific RFP and returns only the strongest one, which is then adapted for the prospect. The final call was where to split machine and human: they kept a person on a roughly fifteen-minute final pass and wired the system into the client's existing CRM and content management system so it triggers the moment an RFP lands. Start to finish, the build took about three months.
best fit for
The teams that would get the most out of this share a specific shape. They respond to a steady stream of RFPs, proposals, or pitches, and every response leans on example content or past work that has to be found and tailored each time. They've been doing it long enough to have a real library built up, but that library is an archive people dig through, not something they can reach quickly.
Two conditions make it work. First, there has to be enough volume that the manual searching is a genuine drag, not a once-a-month task. Second, the past work has to be reusable, close enough from one response to the next that a good match is a real head start rather than a blank page with a different logo on it.
Industry matters less than that shape. We built this in digital media, but the same setup fits agencies, professional services firms, consultancies, publishers, and any B2B sales team that lives in a CRM and answers a lot of RFPs. The one practical requirement is a content library and systems worth connecting to, since the real payoff comes from wiring it into the tools the team already uses rather than standing up something separate.
If a team is turning away RFPs because they can't produce responses fast enough, and the answer is buried in work they've already done, that's the sweet spot.
Ai ROLE
The AI does a few distinct jobs in sequence. First it reads each incoming RFP and interprets what's actually being asked for. Then it searches the tagged content library and retrieves the past pieces most likely to fit. It evaluates each of those candidates against the specific RFP and recommends the single best match. Finally, it generates a tailored draft by adapting that piece to the prospect, which a person reviews and finishes. • So in the prompt's terms: it's interpreting, retrieving, evaluating for fit, and generating, with a person on the final review.
impact
The result: 3+ hrs saved on every RFP that comes through the door.
Content is no longer the ceiling on how many RFPs the team can take on.
15 min of content work per RFP, down from three hours, a 90% reduction in human modification and intervention
Quality maintained, while speed to delivery is revolutionized
76% reduction in RFP backlog
Sales teams get the tools they need to win the bids, budget and work before their competitors.
implementation complexity
We started by watching how the team actually handled RFPs, not how they described it. That's where it became clear that content was the real bottleneck. They had a library of good past work, but reaching the right piece meant digging through it by hand every time.
From there the build went in a few stages.
First, we sorted out the library and decided how to describe each piece. That meant settling on the metadata that actually matters for matching an RFP: use case, industry, and content type. Getting that schema right was the most important early call, because everything downstream leans on it.
Next, we tagged the whole library against that schema programmatically instead of by hand, then loaded it into a vector database so it could be searched by meaning, not just keywords.
Then we built the retrieval logic. A plain semantic search gets you close, but the closest match isn't always the best fit, so we added a step where each candidate is checked against what the specific RFP is asking for, and only the strongest one comes back. That piece then gets copied and reshaped for the prospect.
The last decision was where to draw the line between the machine and a person. We kept a human on the final pass, about fifteen minutes of editing, rather than letting it go out on its own. And we wired the whole thing into their existing CRM and content management system, so it runs the moment an RFP lands and nobody has to open a separate tool.
Start to finish, it took roughly 3 Months.
Matthew Wood
Founder of Checkmark Automations, an AI and automation design-build firm that maps real workflows and builds custom AI-native systems, often for VC- and PE-backed companies.
Get an intro
Talk to this teamindustry
Media & Entertainment
business organization
Sales & Revenue
AI TYpe
Knowledge Management & Search (RAG)
value type
Revenue Growth
frequently asked questions
How can a media company cut RFP content prep from hours to minutes with AI?
A large media enterprise worked with AI experts to build a retrieval system on Claude, backed by a vector database of its past proposal content. The system reads each incoming RFP, finds and ranks the best-fit past work, and drafts a tailored response — cutting content prep from about three hours to fifteen minutes per RFP.
What AI tools and approach were used to automate RFP responses?
The build used a knowledge-management and retrieval (RAG) approach: content tagged with structured metadata and stored in a vector database for semantic search, with Claude interpreting each RFP and generating the draft. It was wired into the team's CRM and content management system using tools including Salesforce, Zapier, Airtable, and Fivetran.
What results did the media company see from AI-assisted RFP responses?
Content prep dropped from about three hours to fifteen minutes per RFP — roughly a 90% reduction in manual work — and the RFP backlog fell by 76%. Response quality was maintained while turnaround sped up, and content stopped being the ceiling on how many RFPs the team could take on.
How long did it take to implement the AI RFP system?
The build took roughly three months end to end, with first meaningful results landing in the two-to-four-month range.
Who is this AI RFP content approach best for?
Teams that answer a steady, high volume of RFPs, proposals, or pitches and lean on reusable past work each time — agencies, professional services firms, consultancies, publishers, and B2B sales teams that already live in a CRM and have a content library worth connecting.
Have a similar challenge?
A network of pre-vetted applied AI experts and AI-native service organizations.
©2026. All rights reserved
