Product development + bootcamp
BodhiContextAI develops the Context OS for enterprise agents and runs practitioner-led bootcamps for people building production AI.
Connect → govern → deliver.AI programs stall for two recurring reasons: teams cannot move prototypes into production, and agents cannot reach dependable business context. BodhiContextAI tackles both.
Connect live systems, ontology, context graphs, policies, and tools once—then deliver governed context to every permitted agent.
Learn through a production-ready agentic system—with architecture, evaluation, safety, observability, and deployment included.
02 / The shared principle
The platform must define where context begins, how meaning is assembled, and who owns each decision boundary. That is what turns a collection of retrieval tools into dependable enterprise infrastructure.
Why we built BodhiContextAI →The graph resolves relationships. The Context OS also handles live state, retrieval routes, permissions, provenance, tools, and feedback.
Ontology gives shared meaning; SQL and events preserve exact truth; documents add expertise; policy decides what each persona can use.
Data teams own source quality, domain teams own meaning, platform teams own runtime controls, and AI teams own agent behavior and evaluation.
Every cohort starts with a real production outcome, works backward to the skills required, and ends with a graded track record your team can inspect.
Capstones and pilots run against real workflows, not isolated demos.
Why it mattersThe capstone is the production system your team keeps, not a prototype of one that gets shelved after the cohort ends.Every module is scored against a rubric, not a completion checkbox.
Why it mattersReadiness is measured against a rubric your team can audit—not a certificate that says "completed."Mentors have shipped production systems for Tier 1 and Tier 2 enterprise customers, not just taught theory.
Our facultyEvery instructor has hit and fixed the failure modes they teach.Meet the team →Your engineers leave able to extend, evaluate, and operate what they ship.
Why it mattersThe skill gap closes inside your own team, not inside a vendor you have to keep paying to operate what they built.Not everything is semantic search. The context layer routes each task to the retrieval path it needs, and carries source, freshness, and policy with every answer.
Combine relational, keyword, semantic, and graph retrieval instead of forcing every question through one index.
How it worksExact values go through SQL, relationships through graph traversal, and concepts through semantic search—each question gets the retrieval path built for it.Every response can carry source, freshness, and decision provenance.
How it worksEvery response carries its source, freshness, and the decision trail behind it, so an answer can be checked, not just trusted.One governed layer supports many agents, channels, and use cases.
How it worksBuilt once and reused across every agent, channel, and use case—not rebuilt from scratch for each new project.Persona and policy decide what each agent may see and do, not a shared context blob.
How it worksPersona and policy decide what each agent can see and act on—scoped and revocable, never a shared context blob.Two decades of enterprise delivery, real Tier 1 and Tier 2 production deployments, and hands-on AI quality expertise—brought to your context and your team.
Two decades building enterprise applications in manufacturing, telecom, and retail; now AI-first, cloud-native platforms and production agent fleets.
BITS Pilani. AI agents, robotics, hyperautomation, and context engineering, built in Python.
Agentic AI, LLM and RAG evaluation, AI reliability, and observability on AWS.