AI Bid Qualification System for Construction and MEP Contractors
Client: Construction / MEP Contractor || Industry: Construction / MEP || Category: Document Intelligence || Platform: n8n + PostgreSQL || Duration: 6 weeks || Complexity: Advanced
Problem
Contractors bidding on public tenders and private RFPs get a constant stream of opportunities, and every one comes with a folder of documents. Someone has to read all of it just to find out if the firm is even allowed to bid. A large share of opportunities get disqualified for reasons that have nothing to do with technical fit — the firm isn't licensed in that state, insurance limits are too high, the bonding requirement is beyond capacity, or there's an MBE/WBE participation requirement that can't be met. None of that needs a read of the technical scope, but under a manual process, someone reads it anyway because there's no way to know in advance.
Solution
Built an automated pipeline that takes a bid opportunity from discovery to a go/no-go verdict with no manual reading involved.
A scraper runs on a schedule (or on demand, by keyword) and pulls new tenders into a PostgreSQL database, with keywords managed directly in a dashboard so the team can widen or narrow search coverage without touching any backend code. Every scraped opportunity fires a webhook into the qualification workflow, carrying the title, closing date, location, issuing organization, and a link to the full document package.
From there, the system retrieves every document in the package and runs each one through strict, word-for-word transcription — not a summary. Tables are kept as structured markdown, and text inside scanned images is pulled out through OCR, since large tender packages are often full of scanned addenda. Everything merges into a single combined record, turning a 200-page package into one searchable document.
Before any scoring happens, the opportunity goes through a killer clause gate — it's checked against a stored profile of the firm's actual capability (licensed states, insurance limits, bonding capacity, licenses held, MBE/WBE availability, minimum revenue, years in business) across six hard disqualifier categories. If even one fails, the pipeline stops immediately, the bid is logged as disqualified, and the team gets a notification — no technical scope gets read on a bid the firm legally can't take.
If there's no killer clause, the opportunity moves to full scoring. An AI agent rates it across five weighted dimensions — Schedule and Resources (25%), Technical Scope (25%), Contract and Legal (20%), Client and Payment (15%), and Strategic Fit (15%) — combined with deterministic keyword matching against the firm's core scope (data centers, HVAC, electrical, MEP). The weighted score resolves into a verdict: Strong Go (above 70%), Conditional Go (50–70%), or No Go (50% or below).
Everything lands in a Google Sheet with 35+ fields per opportunity — project details, market sector, scope summary, mandatory and insurance requirements, strengths, weaknesses, win probability, required actions, all five dimension scores, and the final verdict — with a notification firing as each one completes.
How It Works — The Pipeline
Scrape tenders — Auto-discovers RFPs on a schedule or on demand, by keyword, into PostgreSQL.
Fire webhook — Each scraped opportunity dispatches its title, dates, location, contacts, and document folder link into the qualification workflow.
Retrieve and transcribe documents — Downloads every file in the package, routes by file type, and runs strict verbatim transcription with OCR for scanned content. All output merges into one combined record.
Killer clause gate — Screens six categories of hard disqualifier against the firm's stored capability profile. A single failure stops the pipeline and logs the bid as disqualified.
Weighted AI scoring — Rates surviving opportunities across five dimensions, applies the weights, and combines it with keyword matching against the firm's core scope.
Verdict and output — Resolves to Strong Go, Conditional Go, or No Go, and populates a 35+ field record in Google Sheets with a completion notification.
The Right Fit — and When It Isn't
This system suits contractors and firms bidding regularly on public tenders or private RFPs, where opportunity volume is high, packages are large, and eligibility depends on hard legal and financial criteria that can be defined up front. It applies to any document-heavy review process with clear pass/fail gates.
It's less suited to firms bidding on only a handful of opportunities a year, where the volume doesn't justify the build, or to processes where eligibility is genuinely judgment-based and can't be reduced to a stored capability profile.
Tools & Integrations
n8n, PostgreSQL, Google Sheets, OpenAI, Custom Web Dashboard, Zoho CRM, Zoho Projects, Supabase
Results
Deployed in 6 weeks
200-page document packages reduced to a single searchable record per tender
6 categories of hard disqualifier screened before any scoring
35+ data fields populated automatically per opportunity
Zero manual reading required on legally ineligible bids
Tender packages processed within 3–5 minutes of discovery
Client rating: 5.0 / 5