AI-Native Company
A company where AI agents work beside your people, with clear rules.
Agents with an owner, rules and a track record
Most companies start with scattered experiments: a few people using AI on their own, with no list of what is running and nobody in charge of it. The aim is a small number of well-run agents, each with an owner, rules and a track record, before adding more. We begin with a short assessment of where your company stands: its processes and systems, who owns which data, and the AI already in use.
Then we start small: one process, one agent and a clear decision to continue, adjust or stop. Every agent has an owner, a defined set of permissions and a log of what it did. A person approves anything that cannot be undone, such as payments, outgoing email or deleting records. Answers come from your own documents and systems and respect who is allowed to see what, and someone is named to keep that knowledge correct.
We test quality on your own examples before launch, test again whenever the underlying AI changes, and feed the problems found in use back into the system. Within the agreed scope, we build the capability inside your company with training, named champions, documentation and a handover, so your team can run and change its agents without depending on Capitao.Consulting. Where data is processed is chosen deliberately and written down, with private or local options when data cannot leave your premises. We map which obligations may apply to each system and confirm them with your legal advisers.
What we deliver
- Assessment of where the company stands today (processes, systems, data owners, AI already in use) and candidate processes ranked by value and risk
- A first agent on one narrow process that can be checked, with a written decision at the end to continue, adjust or stop
- For every agent, a named owner, minimum permissions, a log of its actions and human approval before anything irreversible
- A written register of agents, tools and the systems they touch, with a level of control that matches the risk of each
- Search over your documents and systems that respects who may see what, with an owner for each source and a way to correct it
- Connections to the tools you already use, one small connector per system with only the access it needs, through MCP where it fits
- Tests on your own examples before launch, repeated when the underlying AI changes, with logs and alerts in place before go-live
- Where data is processed, chosen and written down, with private or local options when data cannot leave your premises
- Training, named champions, documentation and a handover so your team can run and change the agents
TechnologyFor the technically curious
Agent orchestration
LangGraph · Microsoft Agent Framework · Temporal
Interoperability
Model Context Protocol (MCP) · Agent2Agent (A2A)
Knowledge & access
Qdrant · pgvector · OpenFGA
Evaluation & tracing
Inspect AI · Langfuse · OpenTelemetry