Right-sized architecture
Your workflow, risks, users, and constraints determine whether an AI agent should be built and what shape it should take.
Secure AI Agents
Secure AI agents for sensitive workflows
We begin by understanding the workflow you want to improve, the access it would require, and the failure modes your team cannot accept. Then we design and build the smallest secure agent that solves the real problem.
Problems we help with
We work with organizations where an AI agent can create real harm to users, operations, or trust. If any of these match your situation, we can help you reason through the risk.
Your AI agent touches sensitive data
Customer records, financial transactions, health information, or confidential business data — where unauthorized access by generated code is not an acceptable risk.
Regulators or auditors require evidence
Compliance teams need more than a policy document. They need a demonstrable, inspectable boundary — something they can show to an auditor and point to in source code.
A security failure would be costly
Operational disruption, liability, reputational damage, or loss of user trust — where the consequence of getting it wrong is severe enough that "move fast and fix it later" is not a strategy.
How we work
We need to understand your users, workflow, data, constraints, and definition of success before we recommend technology.
LLM vendor neutral
Jo agents work with any LLM — Claude, GPT-4, Mistral, Llama, or your own fine-tuned model. We have no commercial relationship with any LLM vendor, and we keep it that way.
Full transparency
The agents we build are open to inspection. Capability boundaries are visible in the source code and type signatures. Security reviewers can read exactly what is permitted — no black boxes.
Your data stays yours
We support in-house systems and cloud deployments. In both cases, data safety is the priority: access is explicit, bounded, and designed around your security requirements.
Customer success first
Success means the work is safe, useful, and maintainable in production. We build for the long run and remain available as your system evolves.
Deep technical integrity
The security model has known limits — resource exhaustion, implementation bugs, prompt misuse within granted scope. We explain these upfront and combine complementary measures to defend in depth.
How an engagement works
A focused path from discovery to production handover, with risk and security decisions made visible before the system goes live.
Phase 1 1
Map the workflow
We work with your team to map the workflow, identify where restricted data or critical systems are involved, and name the access that must be controlled.
Outcome
A shared view of the workflow, what the agent may do, what it must never do, and what success looks like.
Phase 2 2
Implement the agent
We implement the agent, its approved access boundaries, and the checks needed for review around the agreed workflow and risk profile.
Outcome
A controlled pilot your team can evaluate with real users while sensitive data remains protected.
Phase 3 3
Launch with evidence
We evaluate the system, help launch it safely, and support continued monitoring and improvement after deployment.
Outcome
Source code, documentation, runbooks, and a clear explanation your team can stand behind.
Ongoing production support
After launch, we remain available for security reviews, incident response planning, performance tuning, feature updates as the business evolves.
Have a hard workflow to automate?
Start with what you want the agent to do, what access it might need, and what would make it unsafe. We will work from there.
Tell us your problem