We build AI around how organizations actually work.

Prashant Bhuyan started Accrete in 2017 to solve the context problem, with a specific focus on capturing and scaling tacit knowledge — which he views as the most important problem in AI. As Einstein put it, everyone’s a genius, but teach a fish to climb a tree and it will fail. Autonomy without expert judgment is an existential liability for organizations applying agents to consequential work, which is why reasoning has to be grounded in context — and the most valuable, hardest-to-capture context is tacit institutional knowledge that’s constantly changing.

Over 15 years, Prashant evolved from building high-frequency trading systems to Knowledge Engines. The binding constraint organizations face in turning reasoning models into productive agentic labor isn’t reasoning — it’s context, and the problem runs far deeper than plumbing siloed data. Knowledge Engines solve the context problem: context recorders that learn through observation and natural interaction, transforming fragmented systems, siloed data, and tacit knowledge into a continuously learning intelligence layer for trusted autonomous work.

As knowledge compounds, Knowledge Engines enable organizations to overcome barriers to employing agentic labor at scale — including skyrocketing token costs, hallucinations, cybersecurity, and sovereignty. Accrete proved the value of Knowledge Engines in the world’s most sensitive infrastructure in defense and intelligence, and is now bringing Knowledge Engines to enterprises. Intelligence is becoming abundant; expert judgment is scarce. Every moment an organization doesn’t record its context, it’s trading compounded superintelligence in the future.