AI agents in production, not in demos
An AI agent is not a chatbot. We design systems that understand objectives, execute actions against real tools and know when to escalate to a human.
The problem we solve.
An AI agent is not a chatbot. It is a system that understands a goal, plans the steps to achieve it, executes actions against real tools (CRM, ERP, APIs) and knows when to escalate to a human.
We design and deploy agents with LLMs (Claude, GPT, Gemini, open-source models) for real use cases: customer service, internal support, administrative process automation and copilots for sales teams.
What is included
- Agent architecture design (tools, memory, planning)
- Integration with your systems (CRM, ERP, helpdesk, APIs)
- Automated quality and regression evals
- Guardrails, human escalation and observability
- Progressive deployment (shadow mode → production)
- Continuous improvement plan based on real feedback
Our process for AI Agents.
Use case
We validate the case is agent-friendly. Not every problem is solved by an LLM.
Prototype
In 2-4 weeks we have a functional agent working with your real tools.
Evals
We build an evaluation dataset that measures quality before and after every change.
Deployment
Shadow mode first, progressive ramp-up later, with observability and rollback ready.
Cases where an agent fits
Agents shine when the problem requires reasoning over structured information and the action to take is clear: classifying tickets, drafting replies, filling in forms, querying APIs and aggregating information, or assisting a human with contextual suggestions.
Where we don't recommend agents: high-risk unauditable decisions, flows where a hallucination has serious legal or reputational impact, or processes where the ROI doesn't justify the investment in evals and observability. The question is not "can an agent do it?" but "should an agent do it?".