USE CASE · OPERATIONS
Your inbox shouldn't run your operations team.
The AI Operations Worker reads incoming requests and documents, updates your systems, chases what's missing and hands exceptions to the right person. It runs in your stack, under your rules, and every action is logged.
PROBLEM
Operations teams in growing B2B companies end up as human middleware.
- Requests arrive by email, PDF, spreadsheet and phone, and someone has to make sense of each one.
- The same data gets typed into two or three systems, with errors along the way.
- Chasing missing documents and confirmations takes hours every week.
- Procedures live in people's heads, so every question goes to the same few senior staff.
- More volume means more hires, because the work doesn't scale any other way.
Off-the-shelf automation breaks on unstructured inputs. Chat tools don't touch your systems. Neither handles the exceptions where the risk actually is.
CURRENT MANUAL WORKFLOW
- 1.A request or document lands in a shared inbox.
- 2.Someone opens it, works out what it is and who owns it.
- 3.They read the attachment and copy the details into the ERP, TMS or ticketing system.
- 4.They spot that something is missing and email the sender.
- 5.They track the chase in their head or a spreadsheet.
- 6.When something doesn't match, they ask a senior colleague how it's normally handled.
- 7.At week's end, someone builds a status report by hand.
AI-OPERATED WORKFLOW
Intake
The worker picks up every new email, ticket, form or upload in the queues you define.
Classify
It identifies the request type, urgency and owner.
Extract
It pulls structured data from the message and attachments, including scans and spreadsheets.
Validate
It checks the data against your systems and business rules and flags mismatches.
Update
It writes validated records into your systems, or proposes them for approval above your thresholds.
Chase
It drafts follow-ups for missing information and tracks them until they're resolved.
Answer
It responds to internal process questions from your SOPs and past cases, with sources.
Escalate and report
It sends exceptions to named owners with full context and produces daily or weekly summaries.
HUMAN CONTROL
Automation handles the routine. People own the judgement.
Approvals
You set thresholds per action. Low-risk updates can run automatically. Credits, commitments and anything unusual wait for sign-off.
Review queue
Low-confidence extractions and proposed changes are shown next to the source document for quick approve, edit or reject.
Audit trail
Every read, extraction, system write and message is logged with its source, output, approver and timestamp.
Escalation
Each exception type has a named owner and SLA timer, so nothing stalls silently.
Data boundaries
Least-privilege access, deployment in your cloud or a UK/EU-region environment, and access controls respected in knowledge search.
IMPLEMENTATION APPROACH
We pick one high-volume workflow, such as document intake to system update or shared-inbox triage, and get it into production first.
| Phase | What happens | Timeline |
|---|---|---|
| 1. Workflow review | You show us the workflow, systems and pain points. We agree scope, success measures and data access. | Week 0 (one call plus a short written scope) |
| 2. Discover & design | We baseline volumes, handling time and error rates, map systems, rules and approval points, and build an evaluation set from real documents and requests. | Week 1–2 |
| 3. Build & integrate | We connect inboxes, document stores and target systems, then build the worker, review queue and logging. | Week 2–4 |
| 4. Shadow mode | The worker processes live work in parallel, with no system writes, so we can compare against the team and the baseline. | Week 4–5 |
| 5. Go live & operate | We switch on progressively by request type, with monitoring, a runbook and a monthly review. Then we extend. | Week 6 onwards |
Phases 2–5 are delivered as a Production AI Sprint. Production AI Sprint
FAQ
- Can it handle scanned PDFs, spreadsheets and messy emails?
- Yes. That's the main reason to use an AI worker rather than rule-based automation. Anything it can't read confidently goes to the review queue with the source alongside, instead of being guessed.
- Our core system is old or has a limited API. Is that a blocker?
- Usually not. We work with whatever access exists: API, database views, file drops or structured exports. We confirm what's possible in the workflow review before committing to a build.
- What happens when it gets something wrong?
- Low-confidence outputs go to review, not into your systems. High-risk actions always need approval. Every action is logged and can be traced back to its source. Corrections are added to the evaluation set so the same mistake is caught next time.
- Will it work with our SOPs and internal documents?
- Yes. We index your procedures, contracts and past cases with access controls in place, and the worker cites its sources when it answers.
- Do we need a data team to run it?
- No. You get a review queue for your operations team and a runbook. We can operate and improve it with you after go-live.
Show us the workflow your team is buried in.
Bring a real example: the inbox, the documents, the systems. We'll tell you plainly what an AI worker could take on, what should stay with people, and what the first production version would look like.