AI-assisted Workflows
An LLM integration embedded in operational workflows, reducing manual processing — with evaluation and guardrails in place from day one.
In production
A team was spending hours a day on repetitive processing work — reading, sorting, extracting and transcribing information that followed predictable patterns. The work was important, but it did not need to be manual.
We integrated LLM-based processing into the workflow where it earned its keep: not a chat window bolted on for the demo, but automation embedded in the actual flow of work, grounded in the business’s own data and rules.
At a glance
How we approached it
01
One use case, not a roadmap
We started with the single task that cost the most time per week, and proved the approach there before touching anything else.
02
Grounded in their data
The model works against the business’s own structured and unstructured data — with the right model chosen for each job, not the biggest one available.
03
Guardrails from day one
Evaluation sets, access control and human review points were built in before rollout. AI that acts needs to be verified — we treat that as part of the system, not an add-on.
04
Monitor and improve
The system reports on its own performance, so the team can see where it is reliable, where it needs review, and where to extend it next.
What changed
The manual processing work moved from a daily burden to a supervised exception — automated where it is reliable, reviewed where it matters, and measured so the team knows exactly where it stands.
Tell us what you're trying to build.
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