AI LABS · AGENT STUDIO
We build agents enterprises run in production.
Agentic engineering is our practice of designing, building and hardening AI agents for the enterprise — agents grounded in your systems that take actions, stay inside policy and justify their cost. Production software with a model at the core.
An enterprise agent is a system built around a model. It reasons over your data, calls your tools, and acts — under identity, permissions and audit, with its token economics measured. We engineer that whole system: the grounding, the actions, the guardrails, the evals, and the way it runs in production.
Bring an idea — we'll scope it and build it.
A finished specification is not required. Bring a problem or an idea; we scope it with you in the discovery workshop, then build and run the agent at enterprise grade. The examples below are starting points.
Describe it here. We scope the rest with you.
A customer support agent, grounded in your knowledge base, that can answer questions — built on Gemini Enterprise, ADK, LangGraph or CrewAI, governed end to end.
We build on the platform that fits your stack.
Framework-agnostic by design. We lead with Gemini Enterprise on Google Cloud, and build on the right framework for the team and the problem.
From a workshop to an agent in production.
A staged engagement. Timelines are typical for each phase and confirmed in discovery.
Discovery workshop
~2 daysWe sit with your team to map the use case, the systems and data it touches, and the success criteria, then decide together what to build.
Proof of concept
~2 weeksA working agent against your real data and systems, end to end on the chosen platform, evaluated against the success criteria we set in discovery.
Deployment — alpha / beta
2–3 monthsWe harden, integrate and govern it, then roll it out to a controlled set of users — measured and observable in production from the first day.
Refinement
~6 monthsWith the agent live, we tune its behaviour and economics, close the gaps the data reveals, and expand scope as results support it.
Have an agent in mind?
Tell us the use case and the systems it touches. We will show you how we would build it and run it economically.