Unimot Energia i Gaz frees its specialists
for the bids worth winning
An AI tender automation workflow runs the compliance review on every public tender,
drafts the formal questions, and hands a ready package to the specialist for sign-off.
The challenge
Expert time, spent on the routine.
The offering team spent its week on routine, replicable work - the same compliance review on every tender, the same questions to draft, the same document structures to verify. They knew exactly what to look for; the work itself was mechanical.
The brief came from C-level: place AI at the heart of the offering workflow, free human potential for higher-value work, keep them owning every decision that leaves the company.
The solution
Seven stages. One human gate.
We built an AI workflow that ingests incoming tenders, runs a 100+ point compliance review on each one, auto-drafts the formal questions, and routes everything to a specialist for approval. It runs in seven stages, from email arrival to a ready-for-review package; each stage is owned by a specialised AI agent, and the whole flow sits behind one human-controlled gate.
Tenders arrive
Incoming tenders land by email - often several in a single message.
Profile filter
Profile-mismatched tenders are filtered out before any model cost is spent on full analysis.
Read & structure
Each tender is read and broken into the structures the review needs.
100+ point compliance review
Every tender is checked against a 100+ point compliance checklist.
Confidence-banded verdicts
Every verdict carries a confidence band, so the specialist sees how sure the model is on each check.
Auto-draft the questions
Where the document doesn't carry an answer, the system says “I don't know” and drafts a formal question to the contracting authority.
Specialist sign-off
Nothing leaves the system until a specialist approves it - the gate is built into the system, not just a rule.
Governance & compliance
Designed to ask when it isn't sure.
Runs on the client's own infrastructure
The workflow runs on Unimot Energia i Gaz's own infrastructure, under their direct Anthropic and OpenAI subscriptions - no third-party SaaS sits between the company and the models.
Public-domain inputs only
Every document the workflow reads is a published tender from public portals. No PII enters the workflow.
One structural human gate
No formal question and no compliance verdict leaves the system without specialist approval - that gate is built in, not a policy that can be waived.
Per-call audit log
Every model invocation is recorded - provider, model, time, cost - and is replayable months later for any audit or regulator request.
Results
A typical batch, processed while no specialist watched.
Take a typical batch - several tenders arriving in a single email, profile-mismatched ones filtered out before any model cost is spent on full analysis.
The system processed the rest on its own:
- Under 20 minutes end-to-end, from email arrival to all tenders analysed and questions drafted.
- Hundreds of compliance checks - each gap turned into a formal question for the specialist to send.
- Under $1 per tender in model cost.
- Zero specialist intervention during processing - specialist time is spent only at the approval gate.
What the numbers don't show
- The offering team moves up the value chain. Repetitive checklist work is gone - specialist time now goes into the work that actually needs an expert, reviewed against confidence-banded verdicts instead of an opaque model output.
- Every run is auditable. A regulator, an internal auditor, or a future bid review can reconstruct every decision the system made, what the model saw, and what the specialist approved.
- The results stay with the client. The workflow runs on Unimot Energia i Gaz's own infrastructure; the data and the system both live in the client's environment.
Volume to the AI. Judgment to the specialist. That's the split that scales any regulated,
document-heavy workflow. That's governed AI, already in production.
Questions