Evidence-backed marketing data query and reporting workspace

For

Marketing and analytics teams answering recurring questions across several data sources

Solves

Business data sits in separate databases, marketing platforms and files, so recurring questions need manual exports, hand-written queries and repeated report rebuilding.

Delivers

Reviewed answers, charts and reports linked to their queries and source records

Built in

about 4 weeks of creation time, MVP in 4 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 manual query and report assembly while keeping every number traceable to its source.

1. Accept plain-language questions about business data.
2. Connect to authorized databases, marketing platforms and file sources.
3. Generate editable SQL for each question.
4. Show query results immediately in a spreadsheet-style grid.
5. Build charts and dashboards from prompts or manual configuration.
6. Clean and transform pulled data before analysis.
7. Join records across multiple sources into one report.
8. Save and reuse query templates for recurring requests.
9. Pull marketing performance metrics from connected platform accounts.
10. Collect data from permitted websites, files and APIs through browser automation.
11. Expose the SQL and processing steps behind each result.
12. Generate written summaries and commentary in a chosen style from reviewed numbers.
13. Guide generated text with customizable input prompts.
14. Push approved data segments into connected marketing and growth tools.
15. Export and share outputs to other workflows.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewed answer, chart or report with source references and unresolved questions.

Everything these tools do, in one app

• Natural language querying Allows users to ask questions in plain language and get answers without writing code or SQL.Found in Airbook AI, BlazeSQL, Eliott - Ask anything about your data
• Data source connections Connects to various databases, marketing platforms, and tools to pull in data automatically.Found in Airbook AI, BlazeSQL, Eliott - Ask anything about your data and 1 more
• SQL query generation Automatically writes SQL queries based on user input, which can be edited for control.Found in Airbook AI, BlazeSQL
• Visualization and dashboards Creates charts and dashboards from data, either through prompts or manual configuration.Found in Airbook AI, BlazeSQL
• Data cleaning and transformation Automatically cleans and transforms data to make it ready for analysis.Found in Airbook AI, Sheet0
• Multi-source data joins Combines data from different sources into comprehensive reports.Found in Airbook AI
• Workflow activation Pushes data segments into growth and marketing tools for operational use.Found in Airbook AI
• Customizable query templates Saves and reuses query templates for recurring data requests.Found in BlazeSQL
• Real-time query results Displays query results immediately within the platform.Found in BlazeSQL
• Marketing platform integration Connects specifically to marketing platforms like GA4, Meta Ads, and Google Ads.Found in Eliott - Ask anything about your data
• Instant marketing insights Provides quick access to marketing performance metrics and insights.Found in Eliott - Ask anything about your data
• Cloud browser automation Automatically collects data from websites, files, and APIs in parallel.Found in Sheet0
• Auditable processing steps Allows inspection of underlying SQL or processing steps for transparency and troubleshooting.Found in Sheet0
• Spreadsheet interface Provides a familiar Excel-like grid for viewing and interacting with data.Found in Sheet0
• One-click share and export Enables easy sharing and exporting of data outputs to other workflows.Found in Sheet0
• Creative writing generation Generates text-based content such as stories, articles, and brainstorming ideas.Found in Daydream
• Customizable input prompts Allows users to guide content creation with specific prompts.Found in Daydream
• Multiple writing styles Supports various writing styles and tones for generated content.Found in Daydream

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized database connections, marketing platform accounts, spreadsheets and file sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect authorized database connections

4. 3

   Marketing platform accounts

5. 4

   Spreadsheets and file sources

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed answers, charts and reports linked to their queries and source records

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate queries, charts and written summaries 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 by default; metric definitions and final numbers remain analyst-owned. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source connections and permissions, Query and evidence workspace, Report and delivery. Use a thumbnail gallery for saved questions and reports, a large central canvas for query, result grid and chart, and a right-hand panel for sources, definitions, filters and comments. Let users compare query versions and result sets side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or number. Make the task-specific outcome reviewed answers, charts and reports linked to their queries and source records visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, source credentials, metric definitions, saved templates, client comments, approval states, usage allowances, query 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

Authorized databases, marketing platform accounts, spreadsheets and permitted file sources. Cloud storage, BI and reporting destinations, and marketing execution tools. 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

   4 days

   One buyer segment, one recurring use case; first modules: accept plain-language questions about business data; connect to authorized databases, marketing platforms and file sources. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   10 days

   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 analytics teams answering recurring questions across several data sources use it to solve "business data sits in separate databases, marketing platforms and files, so recurring questions need manual exports, hand-written queries and repeated report rebuilding"?
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 answers per analyst hour and corrections after report delivery.
4. Measure, then decide. Track accepted answers per analyst hour and corrections after report delivery; 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 by default; metric definitions and final numbers remain analyst-owned. Implement one approved source type, a bounded representative question set and the first two task modules: accept plain-language questions about business data; connect to authorized databases, marketing platforms and file sources. Support the remaining modules with operator review: generate editable SQL, show results in a grid, build charts, clean and join data, save templates, expose processing steps, generate written summaries, push approved segments, export and share. 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 question volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed answers, charts and reports linked to their queries and source records. Retain the explicit scope boundary: Read-only source access by default; metric definitions and final numbers remain analyst-owned.

What the build depends on. Source connection and preview, asynchronous query jobs, editable version history, reviewer access and tested export formats. High-fidelity reporting requires analyst QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Read-only source access by default; metric definitions and final numbers remain analyst-owned.

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 package. Offer a monthly query and reporting allowance after repeat demand. Quote complex multi-source or high-volume work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed answer, chart or report linked to its query and source records. 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 query and report assembly while keeping every number traceable to its source. Demonstrate a concrete reviewed answer, chart or report linked to its query and source records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample answer, chart or report linked to its query and source records from a small authorized input set, with a transparent calculation of accepted answers per analyst hour and corrections after report delivery and no promised savings.

The first 30 days

1. Week 1: interview five marketing and analytics teams answering recurring questions across several data sources and inspect a recent example of business data sitting in separate databases, marketing platforms and files, so recurring questions need manual exports, hand-written queries and repeated report rebuilding.
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 accepted answers per analyst hour and corrections after report delivery, 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 answers per analyst hour and corrections after report delivery. 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 answers per analyst hour and corrections after report delivery; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed answers, charts and reports linked to their queries and source records. 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, query templates and review examples, together with reliable delivery for a narrow analytics niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and analytics teams answering recurring questions across several data sources. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Airbook AI, BlazeSQL, Eliott - Ask anything about your data, Sheet0 and Daydream, plus manual exports and hand-written SQL. Compare this product with the buyer's present method on accepted answers per analyst hour and corrections after report delivery. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Query compute, browser automation runs, 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 answers, charts and reports linked to their queries and source records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, metric definitions, query accuracy and usage permissions. Analysts approve substantive changes and publication scope. Read-only source access by default; metric definitions and final numbers remain analyst-owned. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.