AI Workflow Automation
Automations with judgement — workflows where an AI step reads, classifies and decides before the process continues.
Traditional automation moves data between systems. It cannot read an email and decide whether it is a complaint, an order or a supplier invoice. That judgement step is where automation projects have historically stopped and a human has been inserted. Putting a language model at exactly that point extends automation into work that was previously out of reach.
Who this is for
Teams whose automations already handle the mechanical steps but still stop at a human decision. Companies processing unstructured input — email, documents, form text, transcripts — that needs interpreting before it can be routed.
Problems we solve
- Rules that cannot cope. Keyword matching that fails on how people actually write.
- Human bottlenecks. One person triaging a queue while the rest of the process waits.
- Inconsistent classification. The same item categorised differently depending on who handled it.
- No confidence signal. AI decisions treated as certain when the model was guessing.
- Unbounded AI cost inside a loop. A model called per row across a large batch with no ceiling.
What we build
- Classification and routing steps embedded directly in existing workflows
- Extraction of structured fields from unstructured text, with schema validation
- Summarisation stages that condense long input before the next step
- Confidence thresholds that route uncertain cases to a human queue
- Retrieval steps that ground decisions in your own policies and documentation
- Drafting stages that prepare a reply for human approval rather than sending it
How we work
We put AI only where judgement is genuinely required and keep deterministic logic everywhere else — it is cheaper, faster and easier to debug. Every AI step returns a confidence signal, and low-confidence outputs go to a person rather than through. We start with the model proposing and a human approving, then measure agreement before removing the gate on any category.
Technologies we use
n8n for orchestration with native AI agent nodes, OpenAI, Claude, Gemini or Ollama for the reasoning steps, Qdrant or pgvector where decisions need grounding, and Redis for caching repeated classifications. Structured output with JSON schema validation so downstream steps receive typed data.
Business benefits
- Automation reaches processes that rule-based tools could never cover
- Queues clear continuously instead of waiting for a triage shift
- Classification stays consistent across the whole volume
- Humans review exceptions rather than reading everything
Where it pays off
- Shared inboxes sorted, prioritised and routed automatically
- Support tickets categorised and assigned on arrival
- Supplier documents identified and posted to the right ledger
- Form submissions and survey text classified at volume
- Call transcripts turned into structured CRM updates
Common questions
How accurate is it?
It depends on the task, and we measure rather than estimate. We build a labelled test set from your real data, report accuracy per category before launch, and set the confidence threshold so the error rate is one you have agreed to.
What happens when it is unsure?
It escalates. Low-confidence items route to a human queue with the model's suggestion attached, which is faster to review than deciding from scratch.
Does every step need AI?
No, and using it everywhere is a common mistake. Most of a workflow should be deterministic; AI belongs at the two or three points that genuinely need interpretation.
Have an automation that stops at a human decision? That decision point is usually the one worth automating.