We did not pick a market and look for a problem. We went and asked the people who do compliance work in regulated industries what actually goes wrong, and scoped what we build against the answer. This page is who we are, how we got here, and what is still unproven.
Compliance work in regulated industries is knowledge work done under time pressure with a penalty attached. The expertise that prevents a finding sits in a small number of experienced heads, and it does not scale, transfer, or get checked. That is a problem worth solving with software that can show its reasoning.
Two things changed at once: models became good enough to read a document set and reason about it, and the governance expectation caught up — an answer you cannot trace to a provision is worth nothing in a regulated process. Systems that can cite their source are now buildable and now required.
Our team has been working on state-of-the-art process and system design for compliance AI. We have expert and board advisors out of this industry who act as a vector into the system — the working knowledge of how the process actually runs, shaping what gets built rather than reviewing it afterwards. That is what we are building towards.
Investors and accelerators read the team before the product. This is deliberately short and deliberately specific.
Co-Founder & CTO
At Aeronive Labs, I bridge product strategy and engineering as Co-Founder and CTO to build AI-native compliance systems for regulated industries. Previously, I developed core algorithms for automated classification pipelines, a blockchain-based supply chain platform, and a multi-agent AI orchestrator for the RBI. That experience now drives our mission to eliminate compliance bottlenecks through autonomous AI systems.
Co-Founder & COO
As Co-founder & COO of Aeronive Labs, I lead operations, IT, and compliance, building the infrastructure and systems that let our AI products scale securely and reliably. My focus is turning ambitious technology into an enterprise-ready, compliant business, owning everything from IT infrastructure and security to operational execution and regulatory strategy.
Short, dated, and factual. A timeline that reads as a story rather than a record is a warning sign.
We began by sitting with compliance, risk, and audit teams in regulated industries and asking what actually goes wrong between a question being asked and an answer that holds up under scrutiny. What we build was scoped from those conversations rather than from a market map.
Incorporated to build compliance-native AI for regulated work, starting with the problem the interviews had already defined rather than a platform looking for one.
The governed core is coming together as one system: retrieval that resolves every answer to a cited source, policy compiled into runtime controls, and audit evidence generated by the system as it runs. Every deployment sits on it.
The governed core was built to carry more than one use case. First engagements are being scoped now against the regulated sectors the platform is architected for, each one against a single high-value use case.
Recorded conversations with compliance, risk, and audit practitioners in regulated industries. What we build was scoped from those, not from a market map.
One governed core in active development — grounded retrieval, policy compiled into runtime controls, and evidence generated as the system runs. Every deployment is assembled from it.
No published benchmark results, no named reference customers. When we have measured numbers, they go in Research with the method attached.
The fastest way to reach the people building this is email. It goes to us, not to a queue.
The one we get asked most is what happens when the system is wrong. We have an answer, and it is architectural rather than reassuring.