Regulated firms invest heavily in AI and cloud, then watch the value stall behind guardrails, reviews, and architecture signoffs. Too often the initiative is abandoned because the foundations were never laid. Neogeno builds those foundations: we make your enterprise legible to AI, compress the path from idea to production, and keep every step defensible to your regulators.
In a regulated enterprise, the hard part of AI and cloud was never the AI model or cloud technology. It is everything around it. Every initiative has to clear a thicket of guardrails, security reviews, and architecture signoffs before it can touch production, and each gate adds weeks or months between a good idea and a live result.
So teams do the rational thing and rush. They stand up agents and copilots quickly to show progress, but skip the foundations underneath. Then the value never arrives, because the AI is working blind and doesn't meet the compliance and resilience standards of the firm. It has no reliable picture of the interconnected systems it is meant to act on, and in a regulated firm, acting blind without adhering to rules is not an option.
That is why so many enterprise AI programmes quietly stall and get written off. Not because the technology failed, but because it was deployed onto foundations that were never laid. Neogeno lays those foundations. We make your enterprise legible to AI, compress the path from idea to production, and keep every step defensible to the people who have to sign it off.
How we do it. Two products and an open-source toolkit, built on one principle: lay the foundations first, then move fast without losing the audit trail. Who is behind it, and what each product does, follows below.
Neogeno is an independent advisory practice founded by a cloud architect and enterprise AI specialist with nearly three decades of experience across global financial institutions, hyperscalers, and his own ventures. That experience covers both sides of the knowledge layer: the platforms that hold the data and documentation, and the analyst tooling that turns it into decisions.
The Enterprise Intelligence thesis came directly from practice. As a Principal Solutions Architect at AWS, and before that leading cloud platforms at HSBC across Asia Pacific, architecting front-office systems at Credit Suisse, and advising JP Morgan on technology modernisation, the same pattern repeated: firms spending millions on AI initiatives built on documentation that nobody trusted. Knowledge walking out the door with every departure. Auditors asked to accept manual reports compiled in a panic.
Before financial services, our founder spent a decade running his own technology company in Hong Kong, building products, managing operations, handling everything from accounting to marketing. That experience shapes how Neogeno operates: lean, direct, and focused on outcomes rather than billable hours.
That track record is the foundation under everything below: the products, the open-source toolkit, and the advisory practice that puts them to work for firms who need this now, not when the tooling matures.
Lay the foundations first, then move fast without losing the audit trail. The Automatic Documentation Pipeline gives AI agents and auditors a true picture of your systems. Neogeno Analyst Studio compresses the journey from idea to production for research and data teams.
Most enterprise documentation is years out of date and never captured how one system depends on another, so an AI agent pointed at it acts on fiction. The Automatic Documentation Pipeline fixes that at the source. It reads your code, schemas, and system dependencies and extracts them into OKF, an open, vendor-neutral format (created by Google): a cross-linked graph of plain markdown that lives in version control, is regenerated on every commit, and serves agents and auditors from one source of truth.
One source, two views. Because the knowledge layer is generated from the code and stored beside it, it stays current by construction. Agents get a graph they can traverse; the second line of defense, including security and enterprise architects, gets a document in your house format from the same extraction. Render to anything, swap the format if the standard moves, never rebuild the pipeline.
A data-science-lab-in-a-box for research teams. The slow part of a new idea is never the analysis. It is standing up a compliant environment, getting data access approved, and surviving an architecture review. Neogeno Analyst Studio collapses that, so an idea moves from question to shareable result in days, not quarters.
The ADP is not a black box. It runs on two open-source tools we built and maintain in the open. Chronicle reads your systems into the knowledge layer; Terravision renders the result for the people who need to see it. Both are in active use across global engineering teams.
From a rapid assessment to a full Enterprise Intelligence programme. Every engagement is scoped against your regulatory context, your agent ambitions, and the state of the systems you need made legible to AI agents.
Four phases, applied to every agentic engagement. Governance and evals are phase-one concerns, not afterthoughts, and scope widens one proven workflow at a time.
Articles and walkthroughs on making systems legible to agents, the open knowledge layer, Terraform tooling, and the Enterprise Intelligence thesis.