BigQuery AI & Gemini Services

BigQuery is no longer just a warehouse. Since 2024 Google has rebuilt it around Gemini: natural-language SQL in BigQuery Studio, data canvas for visual exploration, generative AI functions that run over every row of a table, vector search for retrieval-augmented generation, and — as of April 2026 — a generally available Data Engineering Agent that builds and maintains Dataform pipelines from a prompt. Most teams we meet have turned a few of these features on and are unsure which ones to trust with production data. That is the gap this service closes.

Gemini in BigQuery Enablement

We roll out Gemini in BigQuery deliberately rather than project-wide on day one. That means enabling BigQuery Studio and data canvas for the analysts who will benefit most, connecting SQL generation to your governed datasets so suggested queries reference real tables and column descriptions, and writing the usage guidance your team actually follows: when to accept generated SQL, when to rewrite it, and how to check bytes billed before running a generated query over a multi-terabyte table. We also configure the IAM roles Gemini features require so enablement does not quietly widen data access.

Data Engineering Agent Adoption

The BigQuery Data Engineering Agent (GA April 2026) turns a natural-language description into a Dataform pipeline: source tables, SQLX transformations, assertions, and a schedule. It is genuinely useful for well-understood ingestion and staging work, and it is not ready to be left alone on anything with complex business logic, late-arriving data, or regulatory constraints. Our engagements establish the review loop — every agent-generated SQLX change lands in a Dataform workspace, is reviewed by a senior engineer against a checklist, and is promoted through dev and staging like any other code. You get the speed where the agent earns it and human accountability where it does not.

Generative AI Over Warehouse Data

AI.GENERATE, AI.GENERATE_TABLE, ML.GENERATE_TEXT and ML.GENERATE_EMBEDDING let you call Gemini models from SQL over millions of rows: classifying support tickets, extracting structured fields from free-text notes, summarising call transcripts, or producing embeddings for semantic search. We design these jobs so they are cheap and repeatable — batching rows, caching results in materialised tables so the model is not re-invoked on unchanged data, setting per-project quotas and budget alerts, and using ML.GENERATE_TEXT's flatten_json_output and schema options so downstream queries never have to parse model output by hand.

Vector Search and RAG

Retrieval-augmented generation only works when the retrieval layer is grounded in data you trust. We build embedding pipelines with ML.GENERATE_EMBEDDING, create vector indexes (IVF or TreeAH, chosen by dataset size and recall requirements) and write the VECTOR_SEARCH queries that feed your chatbot, agent, or internal knowledge tool. Because the corpus lives in BigQuery, row-level security, column masking and audit logging apply to retrieval exactly as they do to reporting — a property most bolt-on vector databases cannot offer.

Responsible Rollout

Every AI capability we deploy ships with the controls a security review will ask for:

• Service accounts for agents and remote models scoped to the datasets they need, never project-wide editor roles
• Cloud Audit Logs and INFORMATION_SCHEMA.JOBS dashboards showing who invoked which model, over how many rows, at what cost
• Monthly cost budgets for LLM functions with alerting at 50, 80 and 100 percent
• Documented review gates for agent-generated pipelines and SQL
• Data-classification tags that keep sensitive columns out of prompts and embeddings

Who This Is For

• Data teams under pressure to "do something with AI" who want results that survive an audit
• Organisations with a mature BigQuery estate that have enabled Gemini features but have no usage policy
• Product teams building chatbots or agents that must answer from governed company data

Start With an AI Readiness Assessment

Our two-week AI readiness assessment inventories your BigQuery estate, measures data quality and governance coverage on the datasets AI features would touch, benchmarks the cost of the LLM functions you plan to use, and delivers a prioritised roadmap with an honest view of what to automate now and what to keep in human hands. Contact us to schedule one.

Back to services