Analysts spend 2 to 4 hours on every ad-hoc request: formalizing the task, writing SQL, building a dashboard, and summarizing insights. In large companies, the queue of such tasks backs up the data team's backlog for weeks. We built a BI Copilot that cuts this time to 30–60 seconds and delivers the answer immediately, without analyst involvement. Under the hood — RAG over metric metadata, Text-to-SQL for query generation, and LLM-based result interpretation. The system handles up to 100 queries per day without performance degradation, with p99 latency under 2 seconds. Our team has 7+ years in AI/ML and over 40 deployments across finance, retail, and telecom. BI Copilot understands business context and answers natural language questions: "What was the conversion rate in Q1?", "Why did revenue drop yesterday?", "Which metrics are anomalous right now?"
How Semantic Metric Search Works
A typical dictionary is an Excel table with names and descriptions. Our semantic catalog based on Retrieval-Augmented Generation understands synonyms and context: the question "how much did we earn yesterday" automatically maps to the metric monthly_revenue with a date filter. Each metric contains a SQL template, unit, tags, and owner. Here's how relevant metrics are found via LLM:
def find_relevant_metrics(self, question: str) -> list[MetricDefinition]: catalog_summary = "\n".join([ f"- {name}: {m.description} (tags: {', '.join(m.tags)})" for name, m in self.metrics.items() ]) response = self.llm.messages.create( model="claude-3-5-sonnet-latest", max_tokens=300, messages=[{ "role": "user", "content": f"""Given this metrics catalog: {catalog_summary} Question: {question} Return only the metric names that are relevant, comma-separated. No explanation.""" }] ) metric_names = [m.strip() for m in response.content[0].text.split(',')] return [self.metrics[n] for n in metric_names if n in self.metrics] Why Few-Shot Text-to-SQL Is More Accurate
Text-to-SQL is a critical component. We use few-shot prompting with example queries for the current database schema. In our tests, this approach achieves up to 15% higher accuracy than baseline LLM generation without examples. Each SQL template in the catalog already contains correct grammar, so the Copilot only substitutes parameters (dates, filters). This delivers p99 latency under 2 seconds and reduces hallucination risk. Insight generation is based on numeric results, not model assumptions. BI Copilot processes queries 120 times faster than the traditional approach: 30–60 seconds vs. 2–4 hours. Additionally, answer accuracy reaches 95% thanks to two-level verification—three times higher than standard LLM solutions without RAG.
This saves up to 2 million rubles per year on analytics for a mid-sized business—an 80% reduction in BI analytics costs. Contact us to evaluate your project and get a consultation. Request a free pilot on three metrics within 2 days—you'll see results before deciding on deployment.
What Proactive Anomaly Alerts Provide
The Copilot works not only in Q&A mode but also in push mode. You set thresholds, like "if churn_rate exceeds 5%—notify." The system checks metrics daily and on trigger sends a message explaining the anomaly, its impact, and suggesting actions. This enables response to issues before they escalate into crises.
Comparison: Traditional vs. BI Copilot
| Parameter | Traditional Approach | BI Copilot |
|---|---|---|
| Time per question | 2–4 hours | 30–60 seconds |
| Analyst involvement | Manual SQL writing | Automatic generation |
| Metric coverage | Limited to dashboards | All available metrics |
| Proactivity | Manual alerts only | Automatic anomaly detection |
Integration with Your BI Systems
| System | Connection Method | Metrics |
|---|---|---|
| Tableau | REST API + hyper extract | Published datasources |
| Power BI | Datasets API + DAX | Reports, dashboards |
| Metabase | API + card queries | Questions, dashboards |
| Looker | LookML API | Explores, looks |
| Redash | Query API | Saved queries |
| Custom SQL | Direct connection | Any table/view |
Implementation Process and Timeline
We deploy Copilot iteratively, delivering value fast:
- Data audit—review available sources, select 5–10 key metrics.
- Cataloging—describe metrics, create SQL templates, configure semantic search.
- Connection—integrate with BI system or direct DB access.
- Alert setup—agree thresholds for proactive notifications.
- Team training—workshop on question formulation and answer interpretation.
- Handover—documentation, access, one month of support.
Timeline: 3 to 6 weeks depending on metric count and integration complexity.
What's Included
- Full documentation for all metrics and SQL templates.
- Secure connection to your data warehouse (no data transferred to third parties).
- Training for up to 10 analysts on using the Copilot.
- 1 month of technical support after launch.
- Uptime guarantee with SLA 99.9%.
Get a consultation and assess the feasibility of BI Copilot in your company.