Unvessel AI

Routine work done by agents. People confirm what's uncertain.

Document agents, extraction, one operating surface. Every step recorded.

Black brackets rising like steps, a red cube at the base of the largest
10B+messages · founder's prior work
6warehouse domains · founder's prior work
239tech stacks audited · our tooling

Five modules

All of them share the same machinery: a verification queue, rules that accumulate, and a record of every step.

Document and spreadsheet agents

Files in, records out; a person confirms the uncertain.

What's included

Format recognition, column mapping, validation, write to the target system. Uncertain judgements queued for confirmation; confirmations become rules, so the next file from the same source is handled automatically.

Classification and extraction

Unstructured to structured: tickets, reviews, contracts, return reasons.

What's included

Taxonomies agreed with you; confidence thresholds; low-confidence items routed to a person. Output lands in the warehouse as structured fields.

Operations agent

One surface across back-offices; writes need confirmation.

What's included

Inventory, orders, finance, support in one view; every write action proposed by the agent, confirmed by a person, logged.

Ask your data

Plain-language questions, answered with the query shown.

What's included

A semantic layer over your definitions. Every answer shows the query it ran; no answer without a source. Runs on the Data Foundation.

Managed operations

Kept running as systems change. Monthly.

What's included

Interface-change tracking, queue and rule review, a monthly report of what ran, what was confirmed and what failed.

How it works

  1. Your dataFacts from systems of record
  2. Agent proposesA judgement, with evidence
  3. Person confirmsVerification queue
  4. Execute and recordWritten to your systems, logged
  5. Rules accumulateConfirmed judgements become rules

Step 05 feeds back into step 01

What we connect

The AI you already run in support and marketing stays; we make sure it sees the same data.

Your data stays yours

Agents that ask when unsure. Rules that accumulate. Records of everything.

How this compares with RPA, AI copilots and building in-house
RPAAI copilots in SaaSBuild in-houseUnvessel
Handles variationBreaks on changeWithin its productIf you build itJudges; asks when unsure
When unsureFailsGuessesDependsQueue to a person; answer reused
Data in viewOne screen at a timeIts own productDependsEvery connected system
VerifiabilityLogsBlack boxDependsEvery step recorded
Way inLicenceBundledHiringA two-to-three-week diagnostic

How to start

  1. DiagnosticWhich routine work first; included in the Data Health Diagnostic
  2. First workflowFixed price, accepted on its own
  3. Managed operationsFrom go-live, monthly

Questions

Is this RPA?

No. RPA replays clicks and breaks when a layout changes. These agents read and judge, and ask a person when they can't.

Do we need the Data Foundation first?

For document agents on one system, no. For anything that spans systems or answers with numbers, yes.

What happens when the agent is wrong?

Uncertain judgements never execute; confirmed ones are on record and can be withdrawn. Effects are traceable.

How is it priced?

Diagnostic at a fixed fee; each workflow at a fixed price with acceptance criteria agreed first; managed operations monthly.

Past work is on the case studies page.

Start with a diagnostic

Tell us which routine work eats the most hours and which systems it touches. We'll reply with the scope and the fee — or tell you honestly if we're not the right firm.

info@unvessel.ai