Agentic AI & Automation
Language models, agents and workflow automation, placed where they remove a step or sharpen a decision.
The easy part of AI is attaching it to an interface. The hard part is making it work inside a process that already runs, without breaking it. So we start from the opposite question: where exactly does your team lose time to repetitive work, and where is a decision being made on incomplete information? What answers those two questions deserves a model. Everything else deserves ordinary code.
Language models and retrieval
Grounding models in your own sources through retrieval-augmented generation, so an answer comes from your documents rather than from what the model memorised — and so every answer can show where it came from.
Agent and tool orchestration
Agents that call real tools and APIs inside explicit boundaries: what they may do unattended, what needs human approval, and where the agent stops and hands back to a person.
Workflow automation
Automating the repetitive steps between systems — extract, classify, route, notify — with an audit trail showing what happened and why, because automation you cannot review is automation you cannot trust.
Evaluation and monitoring
Evaluation sets that measure output quality before and after release, plus monitoring for drift, cost and latency. A model nobody measures degrades quietly.
If you have a repetitive process eating your team's time, or knowledge spread across documents nobody can search properly, start by describing the process as it runs today and we will come back with a view of what is worth automating and what is not.
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