Evidence-backed marketing analysis and content workspace

For

Marketing and growth teams that analyze campaign data and produce written content

Solves

Campaign data sits in several tools while written content is drafted elsewhere, so analysis, evidence and published copy drift apart.

Delivers

Reviewer-approved analysis and content linked to their evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce the gap between measured campaign data and published marketing copy.

1. Connect analytics, advertising and warehouse sources.
2. Ask questions in natural language and return answers from live data.
3. Visualize results as charts and dashboards.
4. Detect significant metric changes and alert owners.
5. Forecast likely positive or negative campaign impact.
6. Automate recurring data processing with reusable flows.
7. Generate written content such as articles, blogs and campaign copy.
8. Adjust tone and style to match brand rules.
9. Check grammar and spelling before release.
10. Run batch writing tasks across several briefs.
11. Search connected sources with ranked results and filters.
12. Preview source content before opening it.
13. Keep search history for revisiting earlier queries.
14. Support team editing, feedback and approval.
15. Export content and dashboards in common formats.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before publication.
18. Export a versioned reviewer-approved analysis and content linked to their evidence with source references and unresolved questions.

Everything these tools do, in one app

• AI content generation Automatically generates written content such as articles, blogs, and marketing materials.Found in Avian, ZylerAI
• Tone and style customization Allows users to adjust the tone and style of generated text to match different writing needs.Found in Avian, ZylerAI
• Grammar and spell checking Checks and corrects grammar and spelling to ensure polished output.Found in Avian, ZylerAI
• Collaboration tools Enables team-based editing, feedback, and collaborative workflows.Found in Avian, Uplyt Copilot
• Export options Provides options to export content in popular document formats.Found in Avian
• Multi-source data integration Connects with multiple data sources such as Google Analytics, Facebook Ads, and Google Ads to consolidate data.Found in Avian ChatGPT Plugin, Findly, Uplyt Copilot and 2 more
• Interactive data analysis Allows users to ask questions and receive immediate answers based on live data.Found in Avian ChatGPT Plugin, Fabi.ai Analyst Agent
• Quick setup Offers a streamlined setup process to get started rapidly.Found in Avian ChatGPT Plugin
• Data visualization Enables instant visualization of data to aid interpretation.Found in Avian ChatGPT Plugin, Fabi.ai Analyst Agent
• Privacy and security Ensures sensitive information remains protected during data handling.Found in Avian ChatGPT Plugin
• Smart search algorithms Prioritizes relevant results using advanced algorithms.Found in Findly
• Customizable filters Allows users to refine search outputs with customizable filters.Found in Findly
• Content preview Provides quick preview of content before opening full sources.Found in Findly
• Search history tracking Keeps a history of previous searches for easy revisiting.Found in Findly
• Real-time alerts Detects significant changes early and notifies users for quick response.Found in Uplyt Copilot
• AI-driven predictions Forecasts potential positive or negative impacts before they occur.Found in Uplyt Copilot
• Automated data analysis Automates complex data processing tasks to reduce manual workload.Found in Fabi.ai Analyst Agent, Roadway, Tabula
• Natural language query Allows users to interact with data using natural language queries.Found in Fabi.ai Analyst Agent
• Customizable dashboards Enables creation of customizable dashboards to visualize key metrics.Found in Fabi.ai Analyst Agent, Roadway, Tabula
• Data warehouse integration Connects directly to existing data warehouses to leverage company data infrastructure.Found in Roadway
• Automated growth analysis Delivers insights and recommended actions to optimize marketing campaigns.Found in Roadway
• Batch processing Handles multiple writing tasks simultaneously.Found in ZylerAI
• No-code interface Provides a no-code interface suitable for non-technical users.Found in Tabula
• Reusable data processing flows Automates routine analytics tasks with reusable flows.Found in Tabula

What goes in, what comes out

The workflow

1. In
   Start with

   Connected analytics, advertising and warehouse sources plus brand style rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected analytics

4. 3

   Advertising and warehouse sources plus brand style rules

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewer-approved analysis and content 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. Read-only source access and named reviewer accounts; final claims, forecasts and publication decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source connections and brand rules, Analysis and content canvas, Review and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for sources, filters, charts 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 claim or chart. Make the task-specific outcome reviewer-approved analysis and content linked to their evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source credentials, brand rules, client comments, approval states, usage allowances, revision limits, export 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

Client-owned analytics, advertising and warehouse accounts plus brand style guides. Cloud storage, document export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized read-only connections. 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 analytics, advertising and warehouse sources; ask questions in natural language and return answers from live data. 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 growth teams that analyze campaign data and produce written content use it to solve "campaign data sits in several tools while written content is drafted elsewhere, so analysis, evidence and published copy drift apart"?
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 content pieces per analyst hour and corrections after publication.
4. Measure, then decide. Track accepted content pieces per analyst hour and corrections after publication; 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: Read-only source access and named reviewer accounts; final claims, forecasts and publication decisions remain human. Implement one approved source set, a bounded representative case set and the first two task modules: connect analytics, advertising and warehouse sources; ask questions in natural language and return answers from live data. 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 sources and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved analysis and content linked to their evidence. Retain the explicit scope boundary: Read-only source access and named reviewer accounts; final claims, forecasts and publication decisions remain human.

What the build depends on. Source connection and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity reporting requires specialist analytics QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Read-only source access and named reviewer accounts; final claims, forecasts and publication 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 source and content package. Offer a monthly production allowance after repeat demand. Quote complex warehouse or multi-brand work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved analysis and content linked to their evidence. 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 gap between measured campaign data and published marketing copy. Demonstrate a concrete reviewer-approved analysis and content linked to their evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and growth teams 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 analysis and content linked to their evidence from a small authorized input set, with a transparent calculation of accepted content pieces per analyst hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five marketing and growth teams that analyze campaign data and produce written content and inspect a recent example of campaign data sitting in several tools while written content is drafted elsewhere.
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 content pieces per analyst hour and corrections after publication, 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 content pieces per analyst hour and corrections after publication. 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 content pieces per analyst hour and corrections after publication; 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 analysis and content 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 brand rules, source mappings and review examples, together with reliable delivery for a narrow marketing niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and growth teams that analyze campaign data and produce written content. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Avian, Avian ChatGPT Plugin, Findly, Uplyt Copilot, Fabi.ai Analyst Agent, Roadway, ZylerAI and Tabula, plus spreadsheets and separate writing tools. Compare this product with the buyer's present method on accepted content pieces per analyst hour and corrections after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, 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 reviewer-approved analysis and content linked to their evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand voice, source attribution, claim accuracy and usage permissions. Named reviewers approve substantive claims and publication scope. Read-only source access and named reviewer accounts; final claims, forecasts and publication decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.