Attention heatmap design decision workbench

For

Marketing and product design teams reviewing landing pages, ads and email layouts

Solves

Design and campaign decisions rely on opinion because predicted attention data, design variants and reporting sit in separate rented tools.

Delivers

Reviewed attention findings, design variants and stakeholder reports

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

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

Reduce review cycles and make attention evidence usable in one owned workspace.

1. Generate attention heatmaps from uploaded screenshots.
2. Produce new UI designs and HTML code from heatmap analysis.
3. Forecast engagement trends and suggest send times.
4. Track recipient interactions in real time.
5. Mark areas of interest such as buttons or headlines.
6. Compare design iterations on predicted attention.
7. Toggle analysis layers over design files.
8. Generate reports with customizable metrics.
9. Export reports for stakeholder presentations.
10. Explain design suggestions with generated rationale.
11. Set improvement goals that guide design changes.
12. Accept uploaded heatmap images and matched screenshots without integration.
13. Import and export data through email marketing platforms.
14. Generate multiple design variations within seconds.
15. Compare the reviewed result with the recorded baseline and value assumptions.
16. Capture corrections and named-owner approval before consequential use.

Everything these tools do, in one app

• Attention heatmap generation Creates visual heatmaps showing where users are likely to focus or click.Found in Heatbot.io, Heatbot.io 2.0, Attention Insight FIGMA Plugin
• AI-powered design generation Produces new UI designs and HTML code based on heatmap analysis.Found in Heatbot.io
• Predictive analytics Forecasts engagement trends and optimizes send times.Found in Heatbot.io 2.0
• Real-time tracking Tracks recipient interactions in real time to adjust strategies promptly.Found in Heatbot.io 2.0
• Area of interest marking Allows marking specific areas like buttons or headlines to get percentage attention metrics.Found in Attention Insight FIGMA Plugin
• Design iteration comparison Enables easy comparison between design iterations based on predicted user attention data.Found in Attention Insight FIGMA Plugin
• Toggleable analysis layers Integrates analyses as toggleable layers within FIGMA for seamless design review.Found in Attention Insight FIGMA Plugin
• Automated reporting Generates reports with customizable metrics for detailed analysis.Found in Heatbot.io 2.0
• Report export Supports quick export of detailed reports summarizing attention data for stakeholder presentations.Found in Attention Insight FIGMA Plugin
• Data-driven rationale Provides explanations for design suggestions through generated reports.Found in Heatbot.io
• Customizable improvement goals Users can set or select specific goals to guide the AI in tailoring design changes.Found in Heatbot.io
• No integration required Works with popular heatmap tools by simply uploading heatmap images and matching website screenshots.Found in Heatbot.io
• Email platform integration Integrates with popular email marketing platforms for seamless data import and export.Found in Heatbot.io 2.0
• Rapid iteration Allows users to generate multiple design variations within seconds, speeding up the design process.Found in Heatbot.io

What goes in, what comes out

The workflow

1. In
   Start with

   Uploaded screenshots, heatmap images, campaign goals and platform exports

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect uploaded screenshots

4. 3

   Heatmap images

5. 4

   Campaign goals and platform exports

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed attention findings, design variants and stakeholder 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 three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Attention predictions are directional estimates, not measured user behavior; final design and campaign decisions remain with the marketing owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Asset and goal intake, Editable analysis workspace, Client proof and delivery. Use a thumbnail gallery for projects, a large central canvas for heatmap overlays and variant comparison, and a right-hand panel for goals, marked areas and comments. Let users compare design iterations side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome reviewed attention findings, design variants and stakeholder reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision 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

Client-owned screenshots, heatmap images, campaign goals and permitted platform exports. Cloud asset storage, design-file import/export and email marketing 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: generate attention heatmaps from uploaded screenshots; mark areas of interest such as buttons or headlines. 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 product design teams reviewing landing pages, ads and email layouts use it to solve "design and campaign decisions rely on opinion because predicted attention data, design variants and reporting sit in separate rented tools"?
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 design decisions per review hour and corrections after campaign launch.
4. Measure, then decide. Track accepted design decisions per review hour and corrections after campaign launch; 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 fixed set of page types and one email platform; final design and campaign decisions remain with the marketing owner. Implement one approved input format, a bounded representative case set and the first two task modules: generate attention heatmaps from uploaded screenshots; mark areas of interest such as buttons or headlines. Support the third module with operator review: compare design iterations on predicted attention. 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 attention findings, design variants and stakeholder reports. Retain the explicit scope boundary: One fixed set of page types and one email platform; final design and campaign decisions remain with the marketing owner.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed set of page types and one email platform; final design and campaign decisions remain with the marketing owner.

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 asset package. Offer a monthly production allowance after repeat demand. Quote complex multi-brand or specialist campaign work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed attention findings, design variants and stakeholder reports 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 review cycles and make attention evidence usable in one owned workspace. Demonstrate a concrete reviewed attention findings, design variants and stakeholder reports set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample reviewed attention findings, design variants and stakeholder reports set from a small authorized input set, with a transparent calculation of accepted design decisions per review hour and corrections after campaign launch and no promised savings.

The first 30 days

1. Week 1: interview five marketing and product design teams reviewing landing pages, ads and email layouts and inspect a recent example of design and campaign decisions relying on opinion because predicted attention data, design variants and reporting sit in separate rented tools.
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 design decisions per review hour and corrections after campaign launch, 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 design decisions per review hour and corrections after campaign launch. 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 design decisions per review hour and corrections after campaign launch; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed attention findings, design variants and stakeholder 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 page patterns, campaign constraints 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 product design teams reviewing landing pages, ads and email layouts. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Heatbot.io, Heatbot.io 2.0 and Attention Insight FIGMA Plugin. Compare this product with the buyer's present method on accepted design decisions per review hour and corrections after campaign launch. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, image processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed attention findings, design variants and stakeholder reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve brand guidelines, source attribution, asset permissions and usage rights. Marketing owners approve substantive design and campaign changes and publication scope. One fixed set of page types and one email platform; final design and campaign decisions remain with the marketing owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.