Preface

From early GPT experiments to the DeepSeek moment, this ERP giant shipped “Enterprise GPT” in just six months—doubling internal efficiency and kick-starting new product lines. Here’s how they built it on YMatrix.

Why AI for Enterprise Software?

Since GPT took off—and with DeepSeek pushing the frontier—enterprise software vendors have raced to add “intelligence” to their stacks. But a model alone doesn’t make an enterprise product. For real-world use, AI must sit on a reliable, elastic, and up-to-date data engine. Without it, you get neat demos that stall before customer rollout.

In short: LLMs + an AI-ready database = the foundation of enterprise AI.

That database must deliver:

• Vector search for retrieval-augmented generation (RAG)
• Fast, frequent updates because enterprise knowledge changes daily
• High-throughput queries & steady latency at peak usage
• Low integration cost & quick time-to-value on existing architecture

How Should ERP Vendors Choose a Database for AI?

From a large-vendor perspective, the database behind AI needs to satisfy four pragmatic requirements:

1. Real AI capability: Native, scalable vector indexing & search so models can pull fresh, relevant context.

2. High-velocity updates: Documentation, tickets, code, and policies change often—updates must be cheap and near-real-time.

3. Responsive at scale: Even if AI isn’t the highest-QPS service, peak hours happen; p95 must remain predictable.

4. Fast rollout, low cost: Budgets are cautious; vendors need to layer AI onto what they already run—not rebuild the plane in flight.

Case Study — Six Months to “Enterprise GPT”

Result first: The vendor launched an internal “Enterprise GPT” in ~6 months, validated it with heavy internal usage, and has multiple commercial customers signed for phased rollouts.

What shipped

• Enterprise Search, supercharged: Employees can ask natural-language questions across text, images, and videos, with grounded answers (RAG).
• Smart Process Assistant: Learns from search behavior and domain “hot terms,” recommending relevant assets and next actions to finish tasks faster.
• Directory & Graph Helper: A lightweight org-knowledge graph lets users find people, teams, and related resources with a single prompt—speeding cross-department work.
• AI Agents multiplying:
• -Expense Control Copilot (policy Q&A, anomaly hints)
• -Sales Coach (call note synthesis, objection handling snippets, account briefs)

Why YMatrix under the hood?

The vendor already ran core products on YMatrix—an HTAP hyper-converged database—so they could plug AI in without swapping their data layer:

• Reuse the existing stack: YMatrix already powered their products; adding AI meant building on the same database rather than stitching together new services.
• Built-in vectors: YMatrix includes native vector storage & retrieval for RAG—no external vector store to manage.
• Multimodal retrieval: Unified storage and search across text / images / videos in one system.
• Distributed by design: Horizontal scale-out for compute and storage, keeping sub-second to seconds-level responses under concurrent load.
• Real-time updates: Vectors and source data update in-place; AI sees the latest knowledge without ETL ping-pong.

Bottom line: The team didn’t just “add a chatbot.” They stood up an enterprise-grade AI layer on top of a single, converged database—shortening the path from prototype to production.

Want your own “Enterprise GPT”? See how YMatrix helps you unify data, vectors, and analytics—so AI can finally work at enterprise scale.

→ Let’s talk about your roadmap.