Built from the inside of
the problem.
We are building CevantAI because we know what institutional financial workflows actually demand. We have seen, first-hand, what happens when the analysis misses what matters.
Five months solo build: 1,757 reasoning chains, 97 modules, three productised applications, live Neo4j knowledge graph, enterprise FDE engagement in active proposal review. Previously: UBS Director, 13+ years in institutional capital markets. Oxford AI Programme · Alan Turing Institute Ethics in AI.
Owns commercial operations, GTM, marketing and partnerships end-to-end. Dual remit. Already running the outreach engine. Previously: ex-IB (T&L sector coverage), analytics consulting.
Those programmes embed applied AI inside Top-50 sponsor portfolio companies. CevantAI ships adversarial diligence to the mid-market investment teams underneath them: different buyer, different ticket, same underlying inference layer.
Verified proof points
Every figure we publish is sourced to the actual data it came from. The real finding is more compelling than an inflated one, because it is true and verifiable.
Embedded infrastructure
Your data stays inside your environment at every stage. No public network exposure. No shared model training. Auto-upgrades with frontier models.
Capability, not dependency
Every engagement is designed to build permanent capability inside your organization. We are explicit about what we commit to, and we hand it over.