Evidence-backed insight and reporting workspace

For

Marketing and operations teams that must turn scattered data into reviewed, shareable decisions

Solves

Insights live in several rented tools, so dashboards, recommendations, reports and outreach are rebuilt by hand and cannot be traced to evidence.

Delivers

Reviewed, source-linked findings, recommendations and reports

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce manual reporting and tool switching while keeping every finding traceable to its source.

1. Connect permitted data sources and databases.
2. Build customizable dashboards for chosen metrics.
3. Monitor selected metrics in near real time.
4. Detect trends and emerging themes.
5. Generate prioritized, source-linked recommendations.
6. Run automated analysis over connected data.
7. Organize separate data instances per team or client.
8. Generate and schedule reports automatically.
9. Export and share reports with stakeholders.
10. Support team collaboration and comments on findings.
11. Route routine messages and approvals through workflow automation.
12. Send approved messages across SMS, email and voice.
13. Keep sensitive communications under secure messaging rules.
14. Produce personalized playbooks from goals and product context.
15. Offer an AI advisor for follow-up questions on reviewed data.
16. Capture user feedback and corrections to improve the workspace.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed finding set with source references and unresolved questions.

Everything these tools do, in one app

• Customizable dashboards Lets users tailor visual displays to monitor the metrics and insights that matter most to them.Found in Ask Viable, Profit Leap, InsightQ
• Data source integration Connects to external platforms and databases to pull in data for analysis.Found in Spok, Ask Viable, Telow and 1 more
• Actionable recommendations Provides specific, prioritized suggestions for actions users can take based on the analysis.Found in SHIFTLY, Profit Leap, Telow
• Automated analysis Uses AI to process and interpret data without manual effort, surfacing key findings.Found in Ask Viable, Profit Leap, InsightQ
• Real-time monitoring Tracks data or activity as it happens, giving users immediate visibility into current status.Found in Spok, Profit Leap, InsightQ
• Trend identification Detects patterns and emerging themes in data to help users spot opportunities or issues.Found in Ask Viable, InsightQ
• Report export and sharing Allows users to generate reports and distribute findings to stakeholders.Found in Ask Viable, InsightQ
• Automated report generation Creates and schedules reports automatically, reducing manual reporting work.Found in InsightQ
• Secure messaging Protects sensitive communications with compliance support for privacy and regulatory needs.Found in Spok
• Workflow automation Automates routine processes and message routing to improve efficiency.Found in Spok
• Multi-channel communication Supports messaging across channels like SMS, email, and voice for flexible outreach.Found in Spok
• Personalized playbooks Generates tailored growth strategies and experiments based on the user's product and goals.Found in SHIFTLY
• Collaboration tools Enables team members to work together on data projects and share insights.Found in InsightQ
• AI business advisor Provides instant, personalized business insights and recommendations from an AI advisor.Found in Profit Leap
• Data organization Allows users to create distinct instances to organize and analyze different sets of data.Found in Telow
• User feedback updates Incorporates user feedback to continuously improve and expand functionality.Found in Telow

What goes in, what comes out

The workflow

1. In
   Start with

   Connected data sources, team goals and communication constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected data sources

4. 3

   Team goals and communication constraints

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed, source-linked findings, recommendations and reports

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. Final interpretation, external messaging and publication remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data connections and scope, Analysis and recommendation review, Report and delivery. Use a thumbnail gallery for workspaces, a large central analysis canvas, and a right-hand panel for sources, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant finding. Make the task-specific outcome reviewed, source-linked findings, recommendations and reports visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, data instance boundaries, source credentials, client comments, approval states, usage allowances, report schedules, delivery 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

Buyer-owned databases, analytics platforms, messaging channels and reporting destinations. Start with file exchange and validate destination specifications before promising direct publishing or sending. 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

   5 days

   One buyer segment, one recurring use case; first modules: connect permitted data sources and databases; build customizable dashboards for chosen metrics. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 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 must turn scattered data into reviewed, shareable decisions use it to solve "insights live in several rented tools, so dashboards, recommendations, reports and outreach are rebuilt by hand and cannot be traced to evidence"?
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: Accepted findings per analyst hour and corrections after report approval.
4. Measure, then decide. Track accepted findings per analyst hour and corrections after report approval; 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 team, a bounded set of connected sources and one reporting cadence; final interpretation and external messaging remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect permitted data sources and databases; build customizable dashboards for chosen metrics. Support the remaining modules with operator review. 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 reviewed, source-linked findings, recommendations and reports. Retain the explicit scope boundary: One team, a bounded set of connected sources and one reporting cadence; final interpretation and external messaging remain human.

What the build depends on. Source connection and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity reporting requires qualified analyst review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One team, a bounded set of connected sources and one reporting cadence; final interpretation and external messaging 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 reporting scope. Offer a monthly production allowance after repeat demand. Quote complex integrations or regulated messaging separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed finding 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 manual reporting and tool switching while keeping every finding traceable to its source. Demonstrate a concrete reviewed finding set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and operations professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample finding set from a small authorized input set, with a transparent calculation of accepted findings per analyst hour and corrections after report approval and no promised savings.

The first 30 days

1. Week 1: interview five marketing and operations teams and inspect a recent example of insights living in several rented tools.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted findings per analyst hour and corrections after report approval, 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: Accepted findings per analyst hour and corrections after report approval. 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

Accepted findings per analyst hour and corrections after report approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked findings, recommendations and reports. 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 dashboards, source mappings and review examples, together with reliable delivery for a narrow reporting niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and operations teams. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Spok, SHIFTLY, Ask Viable, Profit Leap, Telow and InsightQ, plus spreadsheets and internal BI teams. Compare this product with the buyer's present method on accepted findings per analyst hour and corrections after report approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data processing, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked findings, recommendations and reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data permissions, source attribution, message consent and regulatory constraints. Named owners approve substantive findings and external messages. One team, a bounded set of connected sources and one reporting cadence; final interpretation and external messaging remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.