Agentic processes and automation in production
We start at use-case discovery and finish with agents running in your production systems. The evaluation comes before the deployment.
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Where agents pay off
Repetitive judgement at volume
Work that is too varied for a rule engine and too repetitive to keep a good person on. Research, triage, qualification, routing.
Systems that do not talk to each other
An agent can read one system, decide, and write to another. That is often cheaper than the integration project nobody wants to fund.
Work that stops at the edge of a document
Anything where a person reads unstructured text, applies context, and produces a short structured answer.
How we scope an agent programme
Discovery
We map the workflow step by step and pick the one step where an agent changes the outcome. Usually not the step you expected.
Evaluation harness
We write the test set before the agent exists. Without it there is no way to tell an improvement from a lucky run.
Guardrails
Limits on what the agent may touch, an escalation path to a person, and a log of every action it took.
Rollout
Shadow mode first, then a share of live volume, then the rest. Each stage has a number that has to hold before the next one starts.
Showcase: sales and go-to-market automation
GTM automation for a technology company
We built the sales and go-to-market automations for a company in the tech industry. Agents research accounts, qualify them against the criteria the team actually uses, and route what is left to a human.
What we need from you
Access to the systems the workflow already runs on, one person who can decide, and examples of the work done well. That is the input for the evaluation set.
Talk to us about an agent programme
Describe the workflow. We will tell you whether an agent is the right answer.
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