Context Platforms for Enterprise GenAI

Many enterprise GenAI projects start with a familiar prototype: one LLM, one vector database, a few documents, and a demo that answers questions. The demo can look convincing. It can also hide the hard part.

The hard part is not the model. It is the context.

In manufacturing, engineering, and enterprise operations, useful answers depend on information scattered across CAD/CAM/CAE tools, BOM systems, PLM, ERP, MES, CRM, warehouses, lakehouses, PDFs, change records, and file exports. A chatbot cannot answer a production question with confidence if it does not know which system owns the truth, which revision is current, which documents the user can see, or which evidence supports the response.

This is why I think the next layer for enterprise GenAI is not another standalone POC. It is a context platform.

From RAG Demo to Context Platform

Basic RAG proves that retrieval can help an LLM. Enterprise RAG has to prove more:

• Which source system owns the answer?
• Which revision, lifecycle state, and permission rule applies?
• Which citation proves the claim?
• Which parts of the answer came from structured data, documents, drawings, or graph relationships?
• Which failure cases did the team test?
• Which actions can the AI suggest, draft, route, or execute?

A context platform manages those questions as product infrastructure. It connects to source systems, keeps metadata with the content, combines search methods, assembles evidence, evaluates answer quality, and defines workflow boundaries.

The platform does not replace the warehouse, PLM, or ERP. It gives AI systems a governed way to use them.

The Pieces That Matter

A useful context platform needs more than embeddings. I would design it around eight pieces.

Source connectors. The platform needs reliable access to warehouses, PLM, CAD vaults, ERP, MES, CRM, file exports, and document stores.

Ingestion pipelines. APIs, CDC, connectors, and batch exports should preserve source identity and update time. Context gets stale fast when teams treat ingestion as a one-time import.

Document and object parsing. PDFs, CAD derivatives, BOM tables, change orders, logs, and specs need different parsers. A drawing note, a BOM row, and a PLM lifecycle state should not become the same anonymous chunk of text.

Metadata. Part number, revision, owner, lifecycle state, permission, source URL, extraction time, and source system need to travel with the content.

Hybrid retrieval. SQL, keyword search, vector search, and graph traversal answer different kinds of questions. A part-number lookup should not depend on semantic similarity. A change-impact question may need the product graph.

Retrieval orchestration. The system should decide when to query SQL, when to filter by metadata, when to search documents, and when to follow relationships across parts, suppliers, and change records.

Citations and evidence packages. The answer should show the evidence. In regulated or engineering-heavy work, a good response is not enough. Reviewers need to inspect the source.

Evaluation and telemetry. Teams should measure retrieval quality, answer correctness, citation correctness, latency, cost, and policy violations. A POC without evaluation only measures how persuasive the demo feels.

CAD, BOM, and PLM Are Context Sources

CAD/BOM/PLM data deserves special treatment because it carries the engineering truth behind the product.

CAD contains geometry, features, drawings, annotations, and derived files. BOM data describes product structure and quantity. PLM adds lifecycle state, release status, effectivity, change orders, ownership, and permissions. MES, QMS, ERP, and service systems add what happened after design moved into production and support.

If the AI treats all of this as plain text, it loses the details that make the answer trustworthy. A PDF drawing may support one claim. A released PLM record may support another. A supplier substitution may need ERP data and PLM approval status. The system has to preserve those distinctions.

For engineering workflows, I would expect an AI answer to include an evidence package: the relevant drawing, the part revision, the BOM relationship, the PLM state, and any change record that affected the answer. That gives a reviewer something concrete to approve or challenge.

Better POCs Build Reusable Assets

Enterprise teams often run disconnected POCs. Each one creates its own corpus, prompt, scoring method, and demo path. That wastes learning.

A stronger POC portfolio should reuse platform assets. Good candidates include:

1. CAD/BOM engineering knowledge RAG for drawings, BOMs, specs, and change records.
2. PLM change-impact assistant for product structure, lifecycle state, change orders, and permissions.
3. Existing-database vector search for bounded RAG over Postgres, SQL Server, Oracle, or MySQL.
4. Warehouse-native AI SQL over Snowflake, BigQuery, Databricks, or Microsoft Fabric.
5. CAM knowledge assistant for machining strategy, tooling, process plans, and cited process knowledge.

Each POC should leave behind something reusable: corpus schema, metadata schema, citation format, gold questions, failure taxonomy, evaluation metrics, permission tests, and telemetry. The next POC should start from those assets instead of rebuilding the same foundation.

Governance Belongs Inside the System

AI governance cannot sit in a slide deck while the system runs somewhere else. For RAG and agent systems, governance has to live in retrieval, context assembly, citation, permissions, logs, and action boundaries.

I would separate AI actions into levels:

• Level 1: Read. The AI retrieves and cites evidence.
• Level 2: Analyze. The AI compares, summarizes, and explains.
• Level 3: Draft. The AI prepares an ECR, review packet, or ticket draft.
• Level 4: Route. The AI sends work into an approval flow.
• Level 5: Execute. The AI changes enterprise records or triggers operational actions.

Most early enterprise POCs should stay between Level 1 and Level 3. Teams can learn a lot there without giving the system authority it has not earned.

The evaluation set should include known-answer cases, known-evidence cases, adversarial cases, ambiguous questions, no-answer questions, permission tests, stale revisions, restricted documents, obsolete drawings, and missing lifecycle states. These cases reveal whether the system can refuse, cite, and ask for clarification instead of producing a smooth wrong answer.

A Practical Starting Point

If I had to start with one project, I would choose CAD/BOM engineering knowledge RAG.

The initial corpus can stay small: a few hundred drawings or specs, several BOMs, change summaries, part metadata, and revision metadata. The goal is not to impress people with scope. The goal is to prove that the system can retrieve the right evidence, respect permissions, cite sources, and expose failures.

The project plan can move in five steps:

1. Define the use case, source owners, data sensitivity, and POC charter.
2. Build the corpus, metadata model, and baseline retrieval.
3. Create gold questions and a scoring rubric.
4. Add hybrid retrieval, citations, and permission filters.
5. Review audit logs, cost telemetry, red-team cases, and the decision package.

At the end, the team should not ask only whether the demo looked good. They should ask whether the system answered from the right source, cited the right evidence, handled permissions, stayed within latency and cost limits, and helped domain experts decide whether to harden, pivot, or stop.

Enterprise GenAI will not mature through model upgrades alone. It needs source authority, evaluation, and workflow boundaries. A context platform gives teams a way to build those capabilities once, then reuse them across the next set of AI applications.