AI agent that lets business users query BigQuery in plain English using Gemini in BigQuery and Vertex AI Agent Builder. No SQL required. Real-time dashboards explained in natural language — insights delivered directly to decision-makers.
We implement a production-grade natural language analytics agent on Google Cloud — connecting Gemini in BigQuery to your existing data warehouse and surfacing it through Looker Studio dashboards, Slack, or a custom web interface. Business users ask questions in plain English and receive accurate, sourced answers within seconds, without any SQL knowledge.
Data teams field 50+ ad-hoc query requests per week, pulling analysts away from strategic work and creating multi-day backlogs for business stakeholders.
Analysts spend 60% of their time on data preparation and query writing rather than generating insights — a massive misallocation of expensive talent.
Business leaders who need data to make decisions can't self-serve insights without SQL knowledge, creating a bottleneck and dependency on technical staff.
Gemini in BigQuery translates plain-English business questions into optimised SQL queries — no SQL knowledge required from the end user.
The agent surfaces key trends, outliers, and patterns automatically, presenting findings in plain language alongside the underlying data.
BigQuery ML models continuously monitor data streams and trigger natural-language alerts when anomalies or threshold breaches are detected.
Recurring reports are automatically narrated by Gemini — turning raw dashboards into plain-English summaries delivered to stakeholders on schedule.
The Vertex AI agent reasons across multiple BigQuery datasets, joining data sources intelligently to answer complex cross-functional questions.
Gemini in BigQuery uses a fine-tuned large language model that understands your specific schema — table names, column definitions, and relationships. When a user asks a question in plain English, Gemini generates the corresponding SQL, executes it against your BigQuery dataset, and returns both the results and a plain-language explanation. The model improves over time as it learns your data vocabulary.
All data remains within your Google Cloud project — no data leaves your environment. Gemini in BigQuery operates within your existing IAM permissions, so users can only query tables they are already authorised to access. Vertex AI Agent Builder applies the same access controls, and all queries are logged in Cloud Audit Logs for compliance.
The agent supports structured and semi-structured data in BigQuery, including standard tables, partitioned tables, views, and BigQuery ML models. It works across any data domain — sales, finance, operations, marketing, and more. Documents and unstructured data can be incorporated via BigQuery's Object Table feature combined with Gemini multimodal analysis.
For typical analytical queries, end-to-end response time (natural language question to results with explanation) is between 3 and 15 seconds, depending on query complexity and dataset size. For simple aggregations and lookups, responses are typically under 5 seconds. Complex multi-dataset joins may take 15–30 seconds. Scheduled report narration runs asynchronously with no latency constraint.
Let your business users query data in plain English while your data team focuses on strategic work. We implement production-ready in 3 weeks.