Evidence-backed prediction and insight workspace

Reduce the time from raw data to a reviewed decision while keeping the evidence attached. For marketing and operations teams that turn business data into forecasts and decisions, convert permitted business data, campaign and CRM records, text feedback and planning assumptions into reviewer-approved forecasts, scores and recommended actions linked to their evidence. The benefit is a testable hypothesis, measured through reviewed decisions per analyst hour and forecast error against actuals; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted business data, campaign and CRM records, text feedback and planning assumptions, then follow this sequence: 1. Connect permitted data sources and map fields. 2. Process large volumes from multiple sources. 3. Build no-code predictive models for future trends and outcomes. 4. Forecast sales, demand and other business metrics. 5. Score leads to prioritize sales and marketing effort. 6. Classify text feedback into categories. 7. Identify churn risk and suggested retention actions. 8. Produce clear insights and recommended actions from raw data. Resolve uncertain cases with qualified reviewers, approve reviewer-approved forecasts, scores and recommended actions linked to their evidence, and measure reviewed decisions per analyst hour and forecast error against actuals against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved data schema and a fixed metric dictionary; final forecast sign-off and budget decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve data rights, source attribution, metric definitions and usage permissions. Named owners approve substantive changes and external actions. One approved data schema and a fixed metric dictionary; final forecast sign-off and budget decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One approved data schema and a fixed metric dictionary; final forecast sign-off and budget decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect permitted data sources and map fields; process large volumes from multiple sources. Support the third module with operator review: build no-code predictive models for future trends and outcomes. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned CRM, campaign, web analytics and support exports. Cloud data storage, spreadsheet import/export and BI destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Data connection and mapping, Model and forecast workspace, Insight review and delivery. Use a thumbnail gallery for projects, a large central analysis canvas, and a right-hand panel for sources, assumptions and comments. Let users compare forecast versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or recommendation. Make the task-specific outcome reviewer-approved forecasts, scores and recommended actions linked to their evidence visible beside its evidence, review state and value baseline.