Complex RFQs can take a month to turn into a bid.
Requirements hide across specifications, drawings, datasheets, amendments, equipment lists, and supplier quotes. Miss one dependency and you can lose the bid—or inherit an expensive scope gap after award.
Where critical requirements hide
The core problem
The requirements are not in one place.
Every document can change the scope established by another.
Specifications
Performance and compliance requirements
P&IDs and drawings
Quantities, interfaces, and process dependencies
Datasheets
Materials, certifications, and operating constraints
Amendments
Late changes that can override earlier requirements
Supplier quotes
Commercial limits and configuration boundaries
One missed dependency is enough. It can lose the bid, destroy margin, or surface as a costly change after contract award.
The deadline stays fixed while the package grows. One missed requirement can lose the bid—or become a costly scope gap after award.
Why the obvious shortcut fails
Manual review is too slow. Generic AI is not reliable enough.
Frontier models are built to generate plausible language. A large RFQ demands something different: complete extraction across conflicting documents, explicit uncertainty, and proof for every positive claim.
The manual path
Up to a month of expert review
Engineers trace requirements, amendments, quantities, and dependencies by hand. It can be trustworthy, but it consumes scarce proposal and engineering time.
The frontier-model shortcut
Fast answers that cannot be trusted
Across hundreds of pages, a fluent model can miss cross-document constraints or invent unsupported scope. One confident error can become a failed project and erase trust in AI.
The Revive solution
From a 300-page RFQ to a verified, reviewable bid.
Purpose-trained models read the complete package locally, link each extracted requirement to evidence, surface conflicts, and assemble the response for engineer approval—without asking a frontier model to guess.
Train for RFQ extraction—not general conversation
Training signal
Model effect
“The enclosure likely meets the required rating.”
Certification · NEMA 4X
Area classification drawing · evidence linked
Primary workflow
RFP to Bids, end to end.
Not another chat window. Revive gives proposal and engineering teams a controlled workflow from first-pass read through final technical proposal.
Read the whole package
Extract requirements across specifications, drawings, appendices, and revisions without losing their source context.
Separate fact from assumption
Distinguish verified project scope from general guidance, missing quantities, and claims that need engineering review.
Build a consistent response
Combine approved product content with current-project facts to assemble a structured, reviewable proposal.
Product walkthrough
From uploaded RFQ package to assembled proposal
How local intelligence works
Local extraction. Formal verification. Human approval.
Ingest the bid package
Bring in RFPs, drawings, datasheets, equipment lists, amendments, and supplier documents together.
Extract every requirement
Revive’s locally deployed extraction model turns scattered technical and commercial language into structured, source-linked requirements.
Verify scope and conflicts
Quantities, certifications, package boundaries, and contradictory claims are surfaced for review.
Build the response structure
The bid is organized around the customer’s requested format, sections, and compliance obligations.
Assemble the proposal
Approved product content and verified project facts become a consistent technical response.
Review and release
Engineers keep control with evidence, exceptions, and an audit trail available at every decision.
Why Revive
Engineering intelligence you can verify.
Oil and gas teams do not need generic answers. They need traceable conclusions, deployment control, and a workflow that makes uncertainty explicit.
Grounded in engineering evidence
Every positive scope claim points back to a project document. If the RFP does not establish it, Revive says so.
Revive-trained models, deployed locally
RFQ extraction runs inside your environment instead of routing sensitive documents through a third-party frontier model.
Human approval stays in the loop
AI accelerates the read and assembly work. Your commercial and engineering teams retain final authority.
Start with one live RFQ
See what gets missed—and how quickly your team can get to a reviewable bid.
Beyond the bid workflow