Applied AI
AI Process Automation
Back-office workflows automated end to end (intake, classification, extraction, decision, hand-off), with exceptions routed to people.
Most companies have a handful of processes that quietly consume hundreds of hours a month: reading email attachments into a system, triaging tickets, reconciling records, chasing approvals. These are now automatable, because language models handle the messy, unstructured middle that rules engines never could. We map the process, automate the deterministic parts properly, use models only where judgement is genuinely needed, and route exceptions to a human with everything they need to decide in seconds.
What's included
- Document & email intake
- Ticket triage & routing
- System-to-system sync
- Exception handling
What you get out of it
- Hours per week returned to the team, measured against a baseline we capture up front
- Consistent handling, so the same input produces the same decision every time
- Exceptions surfaced with context instead of failing silently
Automate the process, not just the prompt
The valuable part is rarely the model call. It is the reliable plumbing around it: idempotent writes so a retry doesn't duplicate a record, a queue that survives a restart, an audit log that satisfies your auditors, and a clear rule for what a human still has to look at. We build automations the way we build payment systems, because when they run unattended, correctness is the whole product.
trigger: inbox/invoices · 34 new messagesclassify: invoice(29) · statement(3) · spam(2)extract: supplier, total, due_date, line_itemsvalidate: 27 clean · 2 amount mismatchpost: erp.create_bill() x27 (idempotent)route: 2 exceptions -> #ap-review with difflog: 34 events written to audit trail
Where automation pays off fastest
Document & email intake
Attachments and forms read, validated, and written into your ERP, CRM, or database, with confidence thresholds deciding what still needs a human glance.
Triage & routing
Tickets, leads, and requests classified, prioritised, enriched with context, and routed to the right queue or owner, with the reasoning attached so people trust it.
System-to-system sync
Reliable integrations between tools that were never meant to talk, with reconciliation jobs that catch drift rather than assuming the happy path always held.
Drafted communication
Replies, follow-ups, and summaries drafted from real context and queued for a one-click send, keeping a person in control of anything a customer will read.
Scheduled & event-driven jobs
Durable schedulers and queues with retries, dead-letter handling, and alerting, so an automation that fails at 3am is visible at 8am instead of discovered in a month.
Technologies we work with
- Python
- TypeScript
- Temporal
- Celery
- Postgres
- Redis
- AWS
- Docker
- n8n
Frequently asked questions
We look for volume, repetition, and a clear definition of done. A process handled dozens of times a week with an unambiguous outcome pays back fastest; a rare judgement call usually should not be automated at all.
It stops and asks. Every workflow has an explicit exception path that hands a human the input, the partial result, and the reason it paused, which is far better than a confident wrong answer.
Not always. For simple, low-volume flows those are the right answer and we will say so. We build custom when the process needs real error handling, auditability, domain logic, or scale that a visual builder starts fighting you on.
Usually yes. Where there is no API we integrate at the database, file, or email layer. Where a vendor UI is genuinely the only surface, browser automation is a last resort we keep tightly scoped and monitored.
Next step
Need ai process automation?
Tell us what you're trying to build. We'll give you an honest read on the approach, the effort, and what it costs to run.