Evidence-backed prediction and insight workspace

For

Marketing and operations teams that turn business data into forecasts and decisions

Solves

Predictions, dashboards and recommendations sit in separate rented tools, so teams cannot trace an insight back to its data, assumptions and reviewer.

Delivers

Reviewer-approved forecasts, scores and recommended actions linked to their evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce the time from raw data to a reviewed decision while keeping the evidence attached.

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.
9. Refresh analytics in real time for up-to-date views.
10. Visualize trends and hidden patterns.
11. Assemble customizable dashboards per team.
12. Generate predictions, plans and recommendations with generative AI.
13. Accept voice commands for insight requests.
14. Design and automate recurring workflows without deep technical skill.
15. Personalize views and interactions per user.
16. Run omnichannel workflows across email, web and messaging.
17. Publish a shareable web app for the approved insights.
18. Compare the reviewed result with the recorded baseline and value assumptions.
19. Capture corrections and named-owner approval before consequential use.
20. Export a versioned reviewer-approved forecast and action set with source references and unresolved questions.

Everything these tools do, in one app

• Predictive modeling Builds models to forecast future trends and outcomes from data.Found in Analytics Model, Akkio, Maya AI
• Data processing Handles large volumes of data from multiple sources.Found in Analytics Model, Maya AI
• Actionable insights Converts raw data into clear insights for decision-making.Found in Analytics Model, Akkio, Maya AI
• No-code interface Allows users to build and deploy models without writing code.Found in Akkio, Maya AI
• Real-time analytics Processes data in real time to provide up-to-date insights.Found in Analytics Model, Maya AI
• Customizable dashboards Provides dashboards that can be tailored to user needs.Found in Analytics Model
• Data source integration Connects to various data sources for seamless data aggregation.Found in Analytics Model, Akkio
• Trend visualization Visualizes trends and uncovers hidden patterns in data.Found in Analytics Model
• Augmented lead scoring Scores leads to help prioritize sales and marketing efforts.Found in Akkio
• Forecasting Predicts future business metrics such as sales or demand.Found in Akkio
• Text classification Categorizes text data automatically for analysis.Found in Akkio
• Churn reduction Identifies and helps reduce customer churn.Found in Akkio
• Instant web app creation Quickly builds and shares web applications that use AI insights.Found in Akkio
• Voice-activated support Allows users to generate insights using voice commands.Found in Maya AI
• Generative AI Uses generative AI to produce predictions, plans, and recommendations.Found in Maya AI
• Workflow automation Designs and automates workflows without extensive technical expertise.Found in Maya AI
• Personalization Tailors interactions and processes to individual user needs.Found in Maya AI
• Omnichannel workflows Manages processes across multiple channels for consistent engagement.Found in Maya AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted business data, campaign and CRM records, text feedback and planning assumptions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted business data

4. 3

   Campaign and CRM records

5. 4

   Text feedback and planning assumptions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved forecasts, scores and recommended actions linked to their evidence

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, data-source versions, client comments, approval states, usage allowances, refresh limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: connect permitted data sources and map fields; process large volumes from multiple sources. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will marketing and operations teams that turn business data into forecasts and decisions use it to solve "predictions, dashboards and recommendations sit in separate rented tools, so teams cannot trace an insight back to its data, assumptions and reviewer"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Reviewed decisions per analyst hour and forecast error against actuals.
4. Measure, then decide. Track reviewed decisions per analyst hour and forecast error against actuals; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved forecasts, scores and recommended actions linked to their evidence. Retain the explicit scope boundary: One approved data schema and a fixed metric dictionary; final forecast sign-off and budget decisions remain human.

What the build depends on. Data upload and preview, asynchronous model jobs, editable version history, reviewer access and tested export formats. High-fidelity forecasting requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved data schema and a fixed metric dictionary; final forecast sign-off and budget decisions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined data package. Offer a monthly production allowance after repeat demand. Quote complex multi-source or specialist modeling separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved forecast and action set. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce the time from raw data to a reviewed decision while keeping the evidence attached. Demonstrate a concrete reviewer-approved forecast and action set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and operations teams that turn business data into forecasts and decisions professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved forecast and action set from a small authorized input set, with a transparent calculation of reviewed decisions per analyst hour and forecast error against actuals and no promised savings.

The first 30 days

1. Week 1: interview five marketing and operations teams that turn business data into forecasts and decisions and inspect a recent example of predictions, dashboards and recommendations sit in separate rented tools, so teams cannot trace an insight back to its data, assumptions and reviewer.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure reviewed decisions per analyst hour and forecast error against actuals, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Reviewed decisions per analyst hour and forecast error against actuals. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Reviewed decisions per analyst hour and forecast error against actuals; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved forecasts, scores and recommended actions linked to their evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved metric definitions, model configurations and review examples, together with reliable delivery for a narrow marketing and operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and operations teams that turn business data into forecasts and decisions. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Analytics Model, Akkio and Maya AI, plus spreadsheets and internal BI teams. Compare this product with the buyer's present method on reviewed decisions per analyst hour and forecast error against actuals. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model runs, data processing, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved forecasts, scores and recommended actions linked to their evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.