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 subscription - 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, with these figures averaged across production runs:
- Under 20 minutes end-to-end on average, from email arrival to all tenders analysed and questions drafted.
- 100+ compliance checks per tender, hundreds across a batch - each gap turned into a formal question for the specialist to send.
- $1–10 per tender in model cost, depending on how long the documents run.
- Zero specialist intervention during processing - specialist time is spent only at the approval gate.
Heavier tenders run longer and cost more. These are averages from live production runs, not a single best case.
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
Can a public tender workflow be automated end-to-end with AI?
How do you keep an LLM from hallucinating its way through a tender?
Does the system replace the offering team?
Where does the data live, and what leaves the company?
What does an AI tender pipeline cost to run per tender?
How long does a deployment like this take?
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