The reliable spine
The non-AI spine is the source-linked workflow: clean records, rules, calculations, integrations, exception queues, approvals and reporting for Tender and RFQ discovery.
Engineering & Capital Goods
Tender and RFQ discovery
EPC and project houses live on tenders (GeM, CPPP, state portals, private RFQs) but discovery is manual and inconsistent, so relevant tenders are missed and bid effort is spread thin. Quoting is the most manual thing the firm does and the place revenue is won or lost. Turnaround is days, depends on which estimator picks it up, and is inconsistent enough that a wide cost divergence between estimators on the same job is normal. Heavy WIP, long receivables, a retention slice nobody chases, and progress invoices that should have been raised but were not. In EPC, only a fraction of earned value is invoiced on time; the rest stays trapped in WIP and retention.
Who has it
Project and EPC engineering houses live on tenders, and custom machine builders carry the longest build cycles, so both feel the trapped-cash and slow-quote pain most acutely; heavy fabricators and machine-tool makers feel it too on every make-to-order quote. The work of turning an engineered enquiry into a priced quote shares its costing spine with the margin-by-job view and its follow-up twin is quote chasing.
What we build
A tender-and-RFQ watch that surfaces relevant opportunities by keyword, value, and geography into one pipeline, with a bid/no-bid checklist and deadline tracking. One shared RFQ register so no enquiry is forgotten, a standard parametric costing template, and a rate-and-process library, so a quote becomes a calculation, not a guess. Turnaround drops from days to hours. A milestone-billing schedule, an alert the moment a milestone is met but not invoiced, a retention register chased like any other receivable, and a WIP-aging view, so trapped cash is named and released.
What is automated, where AI helps, who signs off
Automation for the routine. A person on every decision that matters.
Where AI helps
AI is limited to bounded reading, extraction, matching, clustering or drafting from the firm's own data for Tender and RFQ discovery; it never owns the number, the approval, the promise or the decision.
Who signs off
A named person signs off anything touching money, stock, a customer promise, a regulated filing, a payment, a price, a credit decision or a people decision.
What changes day to day
Fewer missed tenders; bid effort focused on winnable work. Illustrative: more qualified bids submitted. Faster, more consistent quotes; fewer jobs lost to slow response. Illustrative: quote turnaround cut by 40 to 60%; a measurable win-rate lift on fast-response enquiries. Forgotten invoices raised; retention recovered. Illustrative: several weeks of working capital released; retention recovery materially improved.
Illustrative outcome
Fewer missed tenders; bid effort focused on winnable work. Illustrative: more qualified bids submitted. Faster, more consistent quotes; fewer jobs lost to slow response. Illustrative: quote turnaround cut by 40 to 60%; a measurable win-rate lift on fast-response enquiries. Forgotten invoices raised; retention recovered. Illustrative; final numbers come from your own data.
Illustrative; final numbers come from your own data.
Path to the build
How this one gets built.
Book a free 60-minute call, then a free Blueprint on the firm's own records. Deep-dive and build, followed by run and govern so the workflow keeps paying back.
Related builds
What tends to pair with this one.
Same industryPurchase-requisition to PO approval workflowSee what we buildSame industrySpares and installed-base registerSee what we buildSame industryDemand and inventory sanity layerSee what we buildAgri-Inputs & AgritechEnquiry-to-sale pipeline with finance and subsidy follow-upSee what we buildAuto Components & AncillaryFaster, more accurate quoting / revenue operationsSee what we buildAutomotive DealershipsRevenue operations: true acquisition cost and quote disciplineSee what we build
Find the one build worth funding first.
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