Workflow & LLM integration development
LLM capabilities integrated into existing workflows and products — document processing, triage, drafting, summarization — with human-review controls built in.
What we build
AI put to work inside a specific internal process — reading documents, extracting data, triaging inboxes, drafting responses, updating systems — with a human approving anything consequential. We automate the repetitive middle of the workflow and keep people on the decisions. Choose this when the goal is 'make this recurring job take minutes instead of days'.
What you get
- The integrated workflow's source code in a Git repository
- An approval queue where a named reviewer approves or rejects each item before it is written back
- An audit log recording who approved what and when
- A per-run accuracy and throughput report
How the work unfolds
- Map the workflow to automate
- Build the LLM pipeline with human approval steps
- Handle failures and retries gracefully
- Measure accuracy and throughput
- Optimise prompt and caching costs
What shapes the price
Before you see a number, our scoping conversation asks:
- How many recurring jobs are we automating — one workflow end to end, or several? Each workflow is its own pipeline, approval steps and failure handling.
- Will this run at a volume where the AI usage bill matters — thousands of items a month? Prompt and caching optimisation pays for itself at volume and is wasted below it.
How an engagement starts
This work builds on Discovery workshop, so scope, boundaries and constraints are agreed before anything is built.
Teams often combine it with: