How to use AI to analyze RFPs and government tenders

It should produce structured lists a bid manager can check, not a summary to admire. A tender package is a legal instrument spread across conditions booklets, technical specifications, drawings, bills of quantities, forms and amendments, and the dangerous requirements are rarely on page one. A clause requiring a specific certificate can sit in an annex that nobody opens until the week of submission.

Every line the agent writes must point back to the exact file and page, so a reviewer can confirm it in seconds. If the agent cannot find something, such as a bid bond amount, it should say so plainly instead of guessing. Those two rules matter more than which model sits underneath.

By answering the disqualifying questions in minutes, before anyone senior spends an afternoon reading. Most tenders a contractor sees are not worth a full proposal, and the cost of reading each one in depth falls on the most expensive people in the company. The quick check exists to protect their time.

Our tender agent for a Saudi holding company runs that quick check by default on every package: are we eligible, what must we hold, when is it due, is the scope ours. The full brief, with the compliance matrix and risk flags, is generated only on demand for the tenders that pass. That split keeps the agent fast on the daily flow and thorough where it counts, and it is the main reason leadership uses it. The build is described here.

• Eligibility: required contractor classification, registrations and prior experience thresholds.
• Capacity: whether the schedule overlaps work you have already won.
• Money: bid bond, performance guarantee, advance payment terms and retention.
• Fit: whether the scope matches what you deliver well, or drifts into work you would subcontract at thin margin.

Etimad is the Ministry of Finance's digital procurement platform, launched in January 2018 for government entities and private contractors, and it is where Saudi government tenders are published and bids are submitted. A single package can mix Arabic conditions with English datasheets, drawings and specifications. The agent has to read both languages and keep each requirement in its original wording next to the translation, because a reviewer checking a guarantee clause needs the source text, not a paraphrase.

The rules are moving. Saudi Arabia has issued a new Government Tenders and Procurement Law under Royal Decree M/76, replacing the 2019 law issued under Royal Decree M/128, and the law text published in the Official Gazette says it takes effect 120 days after that publication. Under the new law, bids are still submitted encrypted through the portal, and government entities coordinate local content requirements with the Local Content and Government Procurement Authority. A tender agent's checklist has to be updated when the regulations change, which is a maintenance job, not a one-time build.

Our guide to AI in Saudi Arabia covers where tender data should be held and why submission stays with a person.

The documents are in English and the structure is more predictable, but the volume is enormous and the formatting rules are unforgiving. The Government Accountability Office counted $755.1 billion in federal contract obligations in fiscal year 2024. Opportunities are posted on sam.gov, and under the Federal Acquisition Regulation an offeror must be registered in the System for Award Management when submitting an offer and at the time of award, so a lapsed registration is a disqualifier the agent should check first.

Negotiated federal solicitations generally use the uniform contract format, where Section L holds the instructions to offerors and Section M the evaluation factors for award. An agent that cross-references every Section L instruction against the Section M factor it is scored under produces a better outline than most first drafts. State, county and city agencies use their own portals and formats, so the agent should learn your most frequent buyers' templates rather than assume one shape.

It goes wrong in predictable places, and each one has a design answer. The worst failure is a confident, wrong answer to an eligibility question, because that is the one a busy manager will not double-check.

• Scanned pages: stamped and signed forms arrive as images, so optical character recognition (OCR) quality decides what the agent can see. Low-confidence pages should be flagged for a person.
• Tables split across pages: bills of quantities and price schedules break across page boundaries and must be stitched back together before anything is extracted.
• Amendments: an addendum silently changes a date or a requirement. Every new file should be compared against the previous version, with changes listed at the top of the brief.
• Invented requirements: a large language model (LLM) can produce a plausible clause that is not in the documents. Requiring a file and page for every line, and rejecting lines without one, removes most of this.
• Translation drift: legal terms such as guarantee types and penalty clauses lose precision in casual translation, so the original wording stays visible.

Everything that commits the company. The agent reads, sorts and drafts; a named person signs off the compliance matrix, sets the price, accepts or negotiates the commercial terms and decides whether to bid. That division is not a limitation of the technology. It is how accountability works in a bid team, and it is also what makes reviewers trust the agent's lists enough to use them.

A practical rule: the agent may say a requirement exists and where; only a person may say the company meets it. Built that way, a custom AI agent for RFP and tender analysis shortens the reading without moving the judgment. Bid teams serving public buyers can see the wider picture on our government contractors page.