Reads an incoming tender against your own qualification criteria, extracts what it actually requires, and gives you a bid decision before anybody has spent a day on it.
An AI agent for RFP intake handles the first pass on incoming tenders and questionnaires: it extracts the requirements, deadlines, mandatory criteria and scope, checks them against your capability record and bid criteria, identifies disqualifying conditions early, and presents a bid or no-bid recommendation with the reasoning. The decision to respond is one of the largest discretionary costs in a services business — a serious tender consumes days of senior time — and it is usually made on a skim reading and an instinct about whether the deal feels winnable. The failures are predictable in hindsight: mandatory requirements nobody spotted, and tenders plainly written around an incumbent.
Makes the bid decision an informed one.
Extracts requirements, deadlines and mandatory criteria
Checks mandatory conditions against what you can evidence
Identifies disqualifying requirements before work begins
Flags signals that a tender is written around an incumbent
Assembles previously used answers to repeated questions
Presents a bid recommendation with the reasoning attached
A tender that consumes forty hours and fails on a mandatory criterion stated on page nineteen is not an unlucky loss, it is an avoidable one. The same applies to tenders where the specification names capabilities only one provider plausibly has: recognizing that pattern early saves the effort for a competition you might win. Underneath both is a volume problem — questionnaires repeat heavily between tenders, and organizations answer the same security and capability questions from scratch each time because nobody maintains a reusable set. Pulling previous answers together does not write the bid, but it changes the starting point from a blank document to a draft with the routine sections already populated.
A three-step flow before any proposal work begins.
The agent reads the document and pulls out scope, deadlines, mandatory criteria and the evaluation basis, rather than relying on somebody skimming it.
It checks mandatory conditions against what you can evidence, and applies your own bid criteria on size, sector and shape.
It presents bid or no-bid with the specific reasons, and assembles previously approved answers for the sections that repeat.
A tender with a mandatory criterion on page nineteen.
Scenario: a provider was responding to most tenders that arrived and winning a small fraction, with two losses that year on requirements nobody had noticed until late. A public sector tender comes in. The agent extracts the requirements and finds a mandatory certification the provider does not hold and cannot obtain within the timescale — stated once, deep in an annex. That alone settles it, and it settles it on day one rather than day four. It also notes that the specification names a combination of platforms unusual enough to suggest the requirements were written with a particular provider in mind, which is context for the decision rather than a conclusion. The bid team declines and spends the week on a tender they can actually win. The extracted criteria are kept, so when a similar tender arrives from the same buyer the reasoning is already on file.
Anybody spending senior time on competitive bids.
Bid effort is a large discretionary cost decided informally.
A wasted bid consumes the week your team had for winnable ones.
You inherit whatever the skim reading missed.
Tender response is the same problem at a larger unit cost.
Panels and frameworks generate constant questionnaire volume.
Mandatory criteria are unforgiving and buried by design.
Reads the tender, tests the criteria, drafts the routine parts.
Receives tender documents and holds the answer library.
Holds bid criteria, capability evidence and previous responses.
Takes tenders arriving by email and carries clarification questions.
Presents the bid recommendation to the team with the reasoning.
Records the opportunity, the decision and eventually the outcome.
Reports bid and win rates by tender type and buyer.
The tender work that happens before a proposal is written.
Questions about deciding which tenders to answer.
An AI agent for RFP intake handles the first pass on tenders: it extracts requirements, deadlines and mandatory criteria, checks them against your capability record and bid criteria, identifies disqualifying conditions, and presents a bid recommendation with reasoning.
It recommends. The decision involves strategic considerations — a relationship worth investing in, a sector you want a reference in — that no document contains. What it removes is the case where the decision was made without knowing what the tender required.
It can assemble previously approved answers for the sections that repeat, which is a real saving because questionnaires overlap heavily. The parts that win a tender are specific to the buyer and should be written by people.
Suggestive rather than conclusive, and it should be presented that way. Unusually specific requirements sometimes reflect a genuine environment rather than a preferred supplier, and treating the signal as proof will cost you winnable work.
It needs an owner and a review date, because approved answers go stale — a security response that was accurate two years ago may no longer be. Reusing an out-of-date answer in a tender is a representation problem, not a convenience one.
It is usually the most valuable output over time. Win rate by tender type, buyer and source tells you where to spend bid effort, and almost no services business has that number despite bidding being one of its largest costs.
Those are where the volume is. Short vendor questionnaires arrive constantly, repeat almost entirely, and consume time nobody accounts for because each one individually looks trivial.
Reads an incoming tender against your own qualification criteria, extracts what it actually requires, and gives you a bid decision before anybody has spent a day on it.