A working space for agents, automation, evaluation methods, trust layers, compliance research, and product prototypes. Some experiments stay internal; the strongest become public Burhuc products.
Experimental workspace · Built in Luxembourg · Product tracks marked by maturity and evidence
Current public tracks from the lab. Each card is product-specific; AI Labs itself remains broader than any one framework.
Tests a frozen agent-system configuration against a versioned suite. Results describe behaviour observed in that run and do not certify future behaviour, legal compliance, or production safety.
Converts observed failure patterns into controlled remediation candidates. TRAIN results are practice evidence; improvement remains specific to a protected re-evaluation of the frozen system, and broader generalisation is unproven.
Represents the policy-mediated control boundary for selected protected effects: tool calls, memory access, credentials, payments, and outbound data. Production effectiveness requires deployment-specific evidence.
Burhuc Labs follows a simple loop: build, test, measure, improve, and decide. The strongest experiments become internal tools or public products.
Fast experiments are allowed. Public claims are not. Each lab track must earn confidence through tests, evidence, deployment gates, and operational review.
Explore agents, automation, research workflows, trust layers, and decision tooling.
Use realistic operations, runtime endpoints, deployment checks, and user-facing surfaces.
Evidence matters: logs, traces, runbooks, smoke checks, scores, and reviewable artifacts.
Failures are logged, weak ideas are removed, and selected projects move toward release.
CertentIQ records observed behavioural weaknesses. CertentiTrain uses those observations to create controlled remediation candidates for protected re-evaluation.