Service
AI Workflow Automation
We automate business processes end to end, using ordinary deterministic logic for the structured parts and applying model judgement only where the input is genuinely ambiguous.
What is AI workflow automation?
AI workflow automation is the automation of multi-step business processes where at least one step requires interpretation that rules cannot express — reading an unstructured email, classifying a document, extracting fields from an invoice that has no fixed format. The structured steps remain deterministic code; the model handles only the ambiguous step.
This distinction matters commercially. Deterministic steps are cheaper, faster and fully testable. Using a model for work that a rule could do adds cost and variance for no benefit. Good automation design is largely about drawing that line correctly.
Traditional automation platforms handle processes where every step is structured. They stop at the email that needs reading, the PDF with no fixed layout, or the request that has to be triaged before it can be routed. That is where processes fall back to people.
We automate the whole path. The orchestration is deterministic and observable: every run has a state, a history and a retry path. The model is used at specific decision points, with its output validated against a schema before anything downstream acts on it. Consequential actions route through approval.
Business problems
What this usually solves.
The situations clients describe when they start this conversation.
A process that stops at an inbox
Automation handles everything until a message has to be read and understood, and then a person takes over.
Manual triage and routing
Requests arrive in a shared mailbox and are sorted by hand, adding hours of delay before work begins.
Documents keyed by hand
Invoices, certificates and forms arrive in inconsistent formats and are transcribed into systems manually.
Rule sets that have become unmaintainable
Hundreds of conditions accumulated over years, and nobody is confident changing any of them.
Capabilities
What we bring to it.
Process mapping
The actual path a case takes, including exceptions and rework, documented before anything is automated.
Deterministic orchestration
Durable, observable workflow execution with state, retries, timeouts and compensating actions.
Document understanding
Classification and field extraction from unstructured documents, validated against expected schemas.
Intelligent routing
Triage and prioritisation of inbound requests based on content rather than sender or subject rules.
Approval workflows
Human checkpoints with the evidence presented, the proposed action shown, and the decision recorded.
Audit and reporting
Every run traceable end to end: inputs, decisions, model outputs, actions taken and by whom.
Deliverables
What we build
- Inbound request triage, classification and routing
- Invoice and document processing into finance systems
- Compliance document collection, validation and chasing
- Onboarding workflows spanning several systems
- Quote and proposal preparation from structured inputs
- Scheduled reporting and exception detection with escalation
Stack
Technology approach
Deterministic by default. Every model call has a defined input, a validated output schema and a fallback path when confidence is low.
| Layer | What we use |
|---|---|
| Orchestration | Durable workflow execution with state persistence and replay |
| Triggers | Webhooks, mailbox listeners, schedules, database change events |
| Judgement | Model calls at defined decision points with schema-validated output |
| Actions | Idempotent writes into ERP, CRM, finance and document systems |
| Oversight | Approval queues with evidence, override and full decision history |
| Reporting | Run-level telemetry, exception dashboards and volume reporting |
Process
How we deliver.
- 01
Discover
Understand business requirements and existing systems.
- 02
Architect
Design product, cloud, integration and data architecture.
- 03
Engineer
Build production-grade software.
- 04
Launch
Deploy, integrate and validate.
- 05
Scale
Optimize, monitor and evolve.
Case studies
Relevant work
FAQ
AI Workflow Automation — questions we are asked
How is this different from tools like Zapier or Power Automate?
Those platforms are effective for structured triggers and simple actions. They struggle when a step needs interpretation, when volumes grow, when error handling must be sophisticated, or when the logic needs version control and testing. We build custom automation where those limits are being reached.
Which processes are worth automating first?
High-volume, rule-heavy processes with a clear definition of a correct outcome. Low-volume processes with many exceptions rarely repay the effort. We usually recommend starting with one process, measuring it, and expanding from proven ground.
What happens when the model is uncertain?
The case is routed to a person with the extracted data and the source document side by side. Low-confidence output is never written silently. Those reviewed cases also become evaluation data that improves the system.
Can this work with our existing automation platform?
Yes. A common pattern is to keep the existing platform for the structured orchestration and call a custom service for the steps that need interpretation, rather than replacing what already works.
Planning ai workflow automation work?
Tell us about the systems involved and the constraints. We will come back with an architecture and a delivery sequence.