How tech companies run generative AI in production

Summary

• Tech companies deploy generative AI on platforms that support vector retrieval, model serving, governance, and audit trails, ensuring private data stays in the data platform.
• Production generative AI systems require modular components: embeddings pipelines, vector indexes, LLM gateways with cost and safety controls, and orchestration layers for agents.
• Enterprise deployments prioritize data residency, access controls, and reproducibility, storing embeddings and conversation history in governed data platforms instead of separate silos.
• The pattern across tech companies is to couple retrieval-augmented generation (RAG) with unified data governance, so models stay grounded in fresh, auditable enterprise data.

How tech companies run generative AI in production

Tech companies running generative AI workloads in production typically follow a common pattern: they integrate retrieval-augmented generation (RAG) with a governed data platform, ensuring that LLMs stay grounded in fresh, private company data while maintaining audit trails and access controls. This approach avoids copying sensitive data into separate, unmanaged AI silos.
The canonical production pattern involves five core layers. First, data ingestion: pipelines pull data from enterprise sources (databases, files, SaaS systems) into the data platform as governed Delta tables. Second, embeddings and indexing: chunks of source data are vectorized and stored in a searchable index within the same platform, so embeddings never leave the data governance layer. Third, retrieval and reranking: incoming queries retrieve candidate passages from the vector index, optionally rerank them, and filter by access controls. Fourth, prompt assembly and LLM serving: retrieved context is combined with user input and system instructions, then sent to a model serving layer that offers unified logging, cost tracking, and safety controls. Fifth, orchestration and memory: for multi-step agents or stateful conversations, memory and context are stored in the data platform, giving teams auditability and the ability to replay or debug interactions.

Key capabilities to evaluate

Capability	Why it matters
Vector indexes built on governed data	Ensures embeddings stay inside access control perimeters; no data copy to external vector DBs.
Unified vector search with metadata filtering	Supports hybrid retrieval (dense vectors plus BM25) and role-based row filtering so users see only authorized data.
Model serving with cost and safety observability	Tracks token usage, latency, and guardrails in one place; enables A/B testing and rollback.
Fine-tuning and adapter management	Lets teams adapt foundation models to domain-specific tasks without retraining from scratch.
Conversation and context storage in the lakehouse	Enables audit, replay, and long-term memory for agents without external state stores.
MLflow integration for experiment tracking	Records model versions, evaluation metrics, and prompt variants so production deployments are reproducible.
Decoupled embedding pipelines	Allows incremental indexing and reindexing without disrupting retrieval, so data freshness is independent from model deployment.

How Databricks approaches generative AI in production

Databricks supports this pattern across its lakehouse platform. Teams ingest data into Delta Lake with Unity Catalog governance, build chunking and embedding pipelines as Lakeflow Pipelines jobs, and index vectors using Databricks AI Search (built on Delta). Retrieval queries can apply fine-grained row-level security so the model only sees authorized data. For model serving, Databricks Model Serving routes requests to foundation models (hosted via Databricks' managed models or external LLM endpoints), tracks cost and tokens per request, and supports streaming responses for low-latency interactive agents. For orchestration and memory, teams store conversation history and agent context as Delta tables, making every interaction auditable and reproducible.
Databricks Agents provide a higher-level abstraction for multi-step workflows: define agent logic (tools, decision logic, memory), and the runtime handles context windows, tool execution, and error recovery while logging all steps to Unity Catalog. For fine-tuning, teams use Databricks MLflow to track experiments and model versions, then serve adapted models alongside foundation models, choosing which variant to call based on the query or A/B test assignment. All of this stays within the Lakehouse so governance, observability, and data residency are native rather than retrofitted.

Use cases

Customer support automation: A financial services firm builds a support agent that answers questions grounded in product documentation, account data, and FAQs. The agent retrieves relevant passages, verifies the user has access to the account, and escalates uncertain requests to humans. All interactions are logged for compliance audits.
Operational insights and actions: A manufacturing company deploys an agent that answers questions about production metrics, inventory, and logistics, and can trigger remedial actions (reorder inventory, alert a team) after retrieving the relevant context from operational databases. The platform tracks every recommendation for root-cause analysis and continuous improvement.
Real-time data synthesis: A media company uses an agent to synthesize personalized recommendations by retrieving user history and content metadata, then calling a fine-tuned model to generate natural-language summaries. Model variants are A/B tested on engagement metrics, and winners are promoted in production without redeployment.

FAQs

Why store embeddings in the data platform instead of a separate vector database?

Storing embeddings in governed Delta tables means row-level access controls apply to retrieval without extra wiring. If a user shouldn't see a document, they won't retrieve it even if the semantic similarity is high. Regulatory teams also prefer unified audit trails and data residency within the enterprise platform.

How do teams handle model updates without downtime?

Model Serving and Agents support canary deployments and A/B routing. Teams deploy a new model variant to a shadow endpoint, route a fraction of traffic to it, and gradually shift traffic if latency and quality metrics pass. Rollback is a single routing change.

What if the vector index gets stale?

Databricks AI Search refreshes incrementally when new data lands in the source Delta table. For faster updates, teams can use streaming ingestion into Delta, triggering incremental embedding jobs. Freshness is tunable per use case (batch nightly, streaming real-time, or hybrid).

Can multiple teams share a single embedding index?

Yes, if the access control strategy is set up correctly. Unity Catalog row-level filters ensure each team or user cohort retrieves only authorized passages, so one index can serve multiple personas without data leakage.