Quote · The MongoDB Podcast
From Data to Decisions: Powering gen/Agentic AI with Capgemini & MongoDB
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What Effective AI Partnerships Look Like — and the MongoDB–Capgemini Model
At 16:15 · chapter starts 9:30
Apurva pushes into what a truly effective technology partnership looks like in the GenAI era, and both guests make the same core point: AI must be thought of as a system, not a model. [1] — Vinay Makkaji "No single vendor can build a complete enterprise AI system. The most effective architectures combine LLMs, RAG pipelines, hyperscaler servi…" 10:20 Vinay uses the example of a connected fleet solution built jointly with Google and MongoDB to illustrate how what used to require ten separate enterprise integrations can now be consolidated into a unified solution — though it still requires multiple specialized partners. Five components are named explicitly: the LLM, the RAG architecture, the trained models, hyperscaler-native services, and MongoDB's vectorization layer. Farid extends this by pointing out what MongoDB alone cannot supply: industry-specific domain knowledge, transformation governance, and the ability to span people and processes across a large organization. That is exactly what Capgemini brings. [2] — Farid Mohammad "The companies that succeed in this AI transformation, they consider this AI as a system rather than just a model." 13:30 The joint value proposition, Farid argues, is enabling customers to move from experimentation to enterprise-scale AI faster while reducing risk. Vinay closes with a philosophical observation: the future isn't AI replacing IT, but a joint venture between IT organizations and AI — co-evolving together.
No single vendor can build a complete enterprise AI system. The most effective architectures combine LLMs, RAG pipelines, hyperscaler services, and specialized data platforms like MongoDB — each playing a distinct role in delivering unified, production-ready solutions.
Agentic AI is evolving autonomously — but it still needs human oversight. Vinay Makkaji's analogy: it's in cruise control mode. The future isn't AI replacing IT organizations, but AI and IT co-evolving as a joint venture, building better outcomes together.
Unexpected equipment failure in oil and gas costs $250,000 per hour, and 80% of companies face at least one such failure per year according to Baker Hughes. A tri-party reference architecture using MongoDB's vector search, AWS Bedrock, and Capgemini's domain knowledge now predicts failures before they happen.
