AI workflow automation
The processes that resisted automation for twenty years were the ones needing judgement: reading a document, deciding what it is, working out where it should go. Those are now automatable — carefully, and with humans still on the exceptions.
The work that traditional automation could never touch
Rule-based automation handles structured, predictable input. It falls over the moment a supplier changes an invoice layout, a customer writes an email that does not fit a category, or a form arrives as a photograph of a printout.
So those steps stayed manual — and they are usually the expensive ones. A team reading, classifying, re-keying and routing, at a cost that scales linearly with volume and a quality that degrades on a Friday afternoon.
Language models are good at exactly this class of work. The engineering problem is not capability, it is control: knowing when the model is unsure, routing those cases to a person, and measuring accuracy well enough that you can defend the automation to whoever owns the risk.
What we automate
Document intake and extraction
Invoices, purchase orders, contracts, claims, application forms — parsed into structured fields regardless of layout, including scans and photographs, with a confidence score on every field.
Classification and routing
Inbound email, tickets and forms sorted by intent, urgency and owner, then routed into your existing systems. Ambiguous cases escalate rather than guess.
Data reconciliation
Matching records across systems that disagree — supplier names spelled three ways, dates in four formats, partial identifiers — where deterministic matching gives up.
Drafting with review
First-draft responses, summaries, reports and internal notes prepared for a human to approve or edit. Speed gain without handing over the final say.
Exception handling by design
Confidence thresholds tuned per field and per document type. Below the line it goes to a person, and their correction is captured as evaluation data that improves the next run.
Audit trail
Every automated decision recorded with its inputs, confidence and reasoning, so an auditor or regulator can be shown why a given outcome happened.
Typical stack
Chosen per project, not by habit. If your team already runs something that works, we use it.
- Extraction
- vision-capable language models
- OCR pre-processing
- schema-constrained structured output
- Orchestration
- queue-backed pipelines
- Temporal
- n8n
- native integrations with your existing stack
- Integration
- REST and GraphQL APIs
- webhooks
- SFTP and email ingestion
- direct database writes
- Quality
- per-field confidence scoring
- golden-set regression tests
- human review queues
How an automation project runs
We measure the current process before changing it. Without a baseline — how long a case takes now, how often it is wrong now — there is no way to prove the automation helped, and no way to price it honestly.
- 01
Baseline
We sample real cases, measure current handling time and error rate, and identify which steps are genuinely worth automating. Some are not, and we will say so.
- 02
Pilot on live data
The automation runs alongside your team on real cases without acting, so accuracy can be compared against human output before anything is trusted.
- 03
Cut over gradually
High-confidence cases go automatic first, everything else stays with people. The threshold moves as the evidence accumulates, not on a launch date.
- 04
Operate
Monitoring on accuracy drift, volume and cost, with alerts when the exception rate moves. Models and layouts change; the system needs watching.
Common questions
How accurate is it?
It depends entirely on the document and the field, which is why we measure on your data during the pilot rather than quoting a number here. The more useful design point is that the system knows when it is unsure and escalates, so the accuracy that matters is accuracy on the cases it chose to handle.
Do we still need people on this process?
Yes, on the exceptions and on spot-checking. Most clients redeploy capacity rather than remove it — the same team handling several times the volume, spending their time on the hard cases instead of re-keying the easy ones.
What happens when a document format changes?
Far less than with rule-based extraction, which is the main reason to use a model here — a new invoice layout usually needs no change at all. Accuracy monitoring flags drift if a change does degrade results.
Can this run on-premise?
Yes. Where data cannot leave your environment we use open-weight models deployed in your own infrastructure. There is a capability trade-off against frontier models and we will be direct about where it will and will not matter for your use case.
How do we justify this to compliance?
With the audit trail and the measured baseline. Every decision is logged with inputs, confidence and reasoning, and the pilot produces a documented accuracy comparison against your current human process — which is usually a stronger evidence base than the manual process ever had.
Start with a scoping call.
Thirty minutes, no obligation. If we are not the right fit we will tell you on the call rather than after a proposal.
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