Reliability & security in production
Evals, guardrails, observability and security: AI systems that don't drift, don't break silently, and are far harder to hijack.
The difference between a demo and a production system is what you put around the model. I set up evaluations that measure quality before every release, guardrails that block drift, and observability to see what the agent is actually doing. And I test your AI the way someone with bad intentions would, before it happens to you for real: prompt injection, data leaks, an agent stepping outside its scope.
Key facts
red-teaming
I attack your AI before others do
injection
the #1 flaw in the OWASP LLM top 10, tested and contained
access
your data compartmentalized, not exposed by the RAG
What I build
Evaluations and guardrails
Test suites that measure the real quality of your answers on every version, plus filters and output validation that keep the agent in its lane, even on the unexpected.
Evals · Guardrails · Regression testing
Red-teaming and security audit
I test your AI like an attacker: prompt injection, attempted data leaks, an agent pushed outside its scope. I deliver a clear report of the flaws found, ranked by severity, and fix the most critical ones with you. Based on the OWASP top 10 for LLM applications.
Prompt injection · Red-teaming · OWASP LLM
Access control and permissions
The number one problem with an enterprise RAG is that it answers from documents the user wasn't allowed to see. I lock down access: each person only sees their own scope, and your agents and tool connections (MCP) hold only the permissions strictly needed.
Access control · RAG · MCP permissions
The promise
AI that doesn't go off the rails, and far harder to hijack.
Before / after
AI you can hijack with a single sentence
Prompt injections tested, traced and contained
A RAG that answers from documents off-limits to the user
Every answer limited to the person's authorized scope
AI that hallucinates or drifts silently
Drift measured, blocked and traced
A black box in production
A system that's observed, alerted, under control
The stack
Evals
LangSmith
Traces
Guardrails
Observability
Logfire
Monitoring
OWASP LLM
Promptfoo
Red-teaming
Straight answers
What are evals?
Test suites that measure the real quality of your AI's answers on business cases. Before every release, you know whether the new version answers better or worse. Without evals, you're flying blind.
How do you stop an AI from hallucinating?
You never prevent it 100%, you contain it: sourced answers, output validation, refusing out-of-scope questions, and continuously measuring the error rate. The goal is a known, bounded risk, not a magic promise.
Can you make an AI reliable if you didn't build it?
Yes, it's common. I take over what exists, put evals, guardrails and observability around it, and move it from demo to production. Often cutting the bill along the way.
How do I know what my AI is doing in production?
Traces, logs and alerts on every answer: you see what the system does, what it costs, and you're warned when quality drifts, before your users notice.
What is prompt injection, in plain terms?
It's when someone slips a hidden instruction into a message, a document or a web page to divert your AI from its mission: making it ignore its rules, reveal data or trigger an unintended action. It's the most widespread flaw in LLM-based applications. You never eliminate it 100%, you test it, contain it and monitor it.
Can my RAG show an employee documents they aren't allowed to see?
Yes, and it's the most common cause of leaks on an enterprise RAG. By default, the engine searches everything it was given. I wire in your existing access rights so each person only gets answers from what they're authorized to view, and I verify it with tests.
Contact
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