Benefits

• Auto-insights → less analysis drift — Executives and managers receive metric digests with drivers, contributors, and outliers in the tools they already use, keeping attention on what moved and why.
• Ask in plain English → governed answers — Business users ask questions and get responses grounded in your semantic model and row-level security, reducing misinterpretation and shadow metrics.
• From “what happened” to “what next” — Built-in forecasting and anomaly detection convert rear-view dashboards into forward-looking guidance with clear confidence and assumptions.
• Fewer swivel-chair hops — NL→SQL assistants draft queries and explain logic, so analysts spend time validating signal instead of rewriting boilerplate.
• Executive-grade narratives — Insight summaries cite the metric definition and filters you approved, producing consistent briefings for monthly reviews, boards, and go-to-market meetings.
• Scale with governance — Private/derived metrics, semantic policies, and audit trails keep a single source of truth intact as self-service usage expands.

How It Works

1. Assess
   We inventory your analytic estate (warehouse or lakehouse, BI models, semantic layer, metric store) and prioritize 3–5 decisions to accelerate—such as weekly pipeline, churn risk, margin variance, or supply health. Together we:
   • Baseline KPIs — Decision latency, insight adoption, analyst hours on routine requests, forecast error (MAPE/WAPE), and ad-hoc backlog.
   • Map governance — PII handling, row-level and column-level security, data residency, and retention policies.
   • Identify friction — Where definitions drift, where queries are brittle, and which teams wait longest for answers.

   Output: a scope brief, baseline metrics, risk register, and a four-week pilot plan with clear success thresholds and a go/no-go gate.

2. Implement
   We wire an “insights fabric” that reads from governed models and writes narratives your teams can trust:
   • Semantic grounding — Connect to your metric layer so natural-language questions compile into correct, explainable SQL/DAX. Domain terms (e.g., fiscal year starts, “performance”, active customer rules) are encoded to reduce ambiguity.
   • NL→SQL assistance — Enable assistants that draft, complete, and fix queries directly where data lives. Analysts review diffs, adjust filters, and promote approved queries to shared assets.
   • Auto-insights & subscriptions — Stand up digest feeds that push trends, drivers, contributors, and outliers to email/Slack with deep links back to governed views.
   • Forecasts & anomalies — Use warehouse-native time-series models for transparent forecasts you can retrain, version, and explain. We log assumptions, seasonality handling, and known events.
   • Evaluation & quality — Curate “golden sets” of questions and expected answers, then monitor faithfulness, grounding, and query correctness. Non-conforming outputs are flagged for review.
   • Controls — Enforce private/derived metric rules; apply feature policies for any embedded/native apps; require previews before executing generated SQL against production.

   We pilot in a limited domain (e.g., revenue analytics for one region or product line) so your teams validate outcomes without disrupting current reporting.

3. Optimize
   Weekly tuning steadily improves signal-to-noise. We refine prompts and semantic instructions, tighten cohorts and filters, A/B test alert thresholds, and expand coverage only after adoption holds. We also align releases with your platform roadmap to reduce latency and improve quality without added sprawl. When KPIs remain on target, we widen access and add new decisions, channels, and personas.
   • Coverage growth — Add decisions, metrics, and segments with clear rollback; promote only after passing evaluation and shadow tests.
   • Policy & safety — Update redaction rules, rotate secrets, and re-validate approval thresholds as the audience expands.
   • Change control — Version prompts, playbooks, and metric definitions; ship with release notes and KPI deltas.

Case Snapshot

Anonymized example: A growth-stage B2B SaaS team consolidated KPIs into a governed model and launched weekly auto-insight digests for leaders and AE managers. Within six weeks, executives received narratives for revenue, pipeline, and product adoption with “why” explanations; managers got segment-level alerts and drill-downs. Analyst hours on routine slide packs dropped, ad-hoc questions moved to NL→SQL, and forecasting confidence improved as seasonal effects and promotions were encoded into retrainable models. Results varied by business unit depending on data quality and definition maturity.

Risk Reversal

Start with a 4-week pilot; continue only if KPIs are met. Day-0 baseline, day-14 checkpoint, day-28 readout. If jointly agreed targets (decision latency, analyst hours saved, insight adoption, forecast error) aren’t reached, you can stop without a long-term commitment. The program stays focused on measured impact over demos.

FAQ

What You Get

• Insights fabric that plugs into your BI/warehouse and semantic layer (governed NL→SQL + narrative generation).
• Auto-insight subscriptions (drivers, contributors, anomalies) delivered to email/Slack with links to governed views.
• Forecasting & anomaly detection using warehouse-native time-series models with reproducible SQL.
• Analyst accelerators that draft and explain SQL/Python where data lives, with approval workflows.
• Governance pack: semantic instructions, private/derived metric rules, execution logs, and audit dashboards.
• Pilot readout: KPI deltas, adoption metrics, risk register, and a 90-day roadmap tied to your platform releases.

Get a Pilot Plan

Book a 30-minute scoping call. We’ll identify 3–5 decisions to accelerate, confirm systems and guardrails, and deliver a fixed-scope 4-week pilot with KPI targets, evaluation sets, and a go/no-go gate.

Schedule a call