What stood in the way
Sales and finance teams worked in parallel in Zoho CRM and Zoho Books. Queries crossed domains, and generic chatbots hallucinated and leaked data across roles.
Case studyBFSI
Multi-agent AI unifying Finance and CRM workflows
A governed multi-agent LangGraph system resolves cross-domain queries across CRM and Finance without hallucinating or leaking data across roles.
- Client
- Enterprise Sales & Finance Teams
- Practice
- AI Bees
- Services
- Agentic Architecture, Memory & Retrieval, Reliability & Governance
- Region
- USA
40%Faster task execution
45-60 minSaved per user / day
80%Faster bulk operations
40%Fewer handling errors
Our solution
What TeamBees built
AI Bees built a multi-agent system with episodic and semantic memory scoped per user, hallucinated tool calls intercepted by schema validation, and evals, mutation scoring and planted-bug checks run pre-release.
- Agentic Architecture
- Memory & Retrieval
- Reliability & Governance
- Evaluation & Quality
How we delivered
The delivery path
- 01
Router agent
Classifies intent.
- 02
Domain agent
Routes to CRM, RAG, or Finance specialist agents.
- 03
RBAC filter
Hard reject on any role mismatch.
- 04
Typed JSON output
A fallback broadens low-confidence queries.
The results
What changed for the client
Sales and finance teams now resolve cross-domain queries through one governed agent system, with faster execution and far fewer handling errors.
Technology stack
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