Co-built an agentic OS that unifies enterprise knowledge and automates research, drafting, and sales prep.
- AI strategy
- Defined
- Evaluation framework
- In production
- Agent capabilities
- Research, briefing, drafting, retrieval
What we shipped
A partnership with the client CTO and Chief AI Officer to build an agentic operating system.
Companies run on a growing ecosystem of tools. The side effect is knowledge fragmentation. Critical information scatters across email, chat, documents, and internal apps.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Partnered with CTO and CAIO to define high-impact agent use cases and the unified data architecture.
- AI Pilot
Built knowledge integration plus initial agents for research, drafting, and meeting prep with an evaluation harness.
- Production launch
Shipped agent-based workflow automation across research, document creation, and sales preparation.
- Scale and operate
Expanded to meeting assistants, knowledge graphs, report generation, pipeline insights, and autonomous agents.
Business, technical, and governance outcomes.
- Unified enterprise knowledge retrieval.
- Production agents across research and drafting.
- Scalable evaluation and monitoring framework.
- Foundation for autonomous business agents.
- Python
- LLMs
- LangGraph
- Vector Database
- PostgreSQL
- Kubernetes
Dedicated AI evaluation and monitoring framework benchmarks agents across real business scenarios for safe iteration.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.