Personal health evidence and reporting workspace

For

Individuals combining wearable, log and lab data who want plain-language answers and plans

Solves

Health data sits in separate apps and wearables, so people cannot see patterns or get clear answers about their own records.

Delivers

User-reviewed health summaries linked to source records

Built in

about 6 weeks of creation time, MVP in 7 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 effort of turning scattered health data into reviewed, plain-language answers and plans.

1. Sync sleep, activity, heart rate and recovery data from connected wearables.
2. Combine apps, wearables and labs into one health profile.
3. Answer free-form questions about the user's own data.
4. Detect correlations across different health data types.
5. Log meals, including from meal photos.
6. Upload bloodwork and connect markers to lifestyle factors.
7. Send proactive updates on what changed and possible next steps.
8. Suggest simple personal experiments on habits.
9. Log mood, energy, supplements and habits.
10. Track symptoms and medications for correlation.
11. Draft personalized wellness plans and adherence steps.
12. Provide practitioner-style guidance with source references.
13. Generate tailored workout plans from user input and progress.
14. Track workouts and analyze performance.
15. Visualize progress through charts and reports.
16. Run community challenges for shared motivation.
17. Read meal photos and notes together with text.
18. Run on limited hardware or edge devices.

Everything these tools do, in one app

• Wearable data sync Automatically pulls sleep, activity, heart rate, and recovery data from connected wearables or health platforms.Found in Illume Labs, Kim Personal Health Assistant, Insightfull and 1 more
• Unified health profile Combines data from multiple apps, wearables, and labs into one place.Found in Illume Labs, TrueWellness, Kim Personal Health Assistant and 1 more
• Natural-language health questions Lets users ask free-form questions about their own health data and get answers.Found in Illume Labs, Kim Personal Health Assistant, Insightfull
• Cross-source pattern detection Finds correlations and patterns across different types of health data.Found in Illume Labs, Kim Personal Health Assistant, Insightfull
• Food and meal logging Records what you eat, including via meal photos, to track nutrition.Found in Illume Labs, Kim Personal Health Assistant, Insightfull
• Lab result upload Lets users upload bloodwork or lab results and connect those markers to lifestyle factors.Found in Illume Labs, TrueWellness
• Proactive insights Sends updates about what changed, what it might mean, and suggested next steps.Found in Illume Labs, TrueWellness
• Personal experiments Suggests simple tests to try on yourself to see how habits affect how you feel.Found in Kim Personal Health Assistant
• Context logging Adds subjective information like mood, energy, supplements, and habits to sensor data.Found in Kim Personal Health Assistant, Insightfull
• Symptom and medication logging Tracks symptoms and medications to help identify correlations.Found in Insightfull
• Automated wellness plans Creates personalized health plans and adherence protocols to turn insights into action.Found in TrueWellness
• Practitioner-style guidance Uses AI trained on experienced practitioners to give guidance at scale.Found in TrueWellness
• Personalized workout plans Generates tailored exercise plans based on user input and progress.Found in Gym Hero
• Exercise tracking and analysis Tracks workouts in detail and automatically analyzes performance.Found in Gym Hero
• Progress visualization Shows progress through charts and reports.Found in Gym Hero
• Community challenges Provides community support and motivation through shared challenges.Found in Gym Hero
• Multimodal image-text understanding Understands and generates content based on both images and text.Found in SmolVLM2
• Lightweight model deployment Runs efficiently on machines with limited hardware or edge devices.Found in SmolVLM2

What goes in, what comes out

The workflow

1. In
   Start with

   Connected wearable data, meal and habit logs, lab results and symptom notes

2. 1

   Confirm the user's problem and scope

3. 2

   Collect connected wearable data

4. 3

   Meal and habit logs

5. 4

   Lab results and symptom notes

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   User-reviewed health summaries linked to source records

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 medical interpretation and treatment decisions remain with qualified clinicians. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data connections and profile, Editable health summary, Plan and progress view. Use a dashboard of connected sources, a large central summary canvas, and a right-hand panel for source records, flags and comments. Let users compare periods side by side. Display draft, changes requested and approved states. Provide a shareable clinician link with comments anchored to the relevant record. Make the task-specific outcome user-reviewed health summaries linked to source records visible beside its evidence, review state and value baseline.

Accounts and administration

Account ownership, source connections, record versions, sharing permissions, approval states, retention 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

User-owned wearable accounts, lab portals and permitted health sources. Cloud record storage, health-file import/export and clinician sharing destinations. Start with file exchange and validate destination specifications before promising direct sharing. 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

   7 days

   One buyer segment, one recurring use case; first modules: sync sleep, activity, heart rate and recovery data from connected wearables; combine apps, wearables and labs into one health profile. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

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

4. 4

   Full product

   3 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 individuals combining wearable, log and lab data who want plain-language answers and plans use it to solve "health data sits in separate apps and wearables, so people cannot see patterns or get clear answers about their own records"?
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 summaries per user month and corrections after review.
4. Measure, then decide. Track accepted summaries per user month and corrections after review; 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 wearable platform and one lab upload format; final medical interpretation remains with qualified clinicians. Implement one approved input format, a bounded representative case set and the first two task modules: sync sleep, activity, heart rate and recovery data from connected wearables; combine apps, wearables and labs into one health profile. Support the third module with operator review: answer free-form questions about the user's own data. Include source references, corrections, basic account 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 user-reviewed health summaries linked to source records. Retain the explicit scope boundary: One wearable platform and one lab upload format; final medical interpretation remains with qualified clinicians.

What the build depends on. Record upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity health review requires qualified clinical QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One wearable platform and one lab upload format; final medical interpretation remains with qualified clinicians.

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 health data package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded user-reviewed health summaries linked to 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 the effort of turning scattered health data into reviewed, plain-language answers and plans. Demonstrate a concrete user-reviewed health summaries linked to source records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Individuals combining wearable, log and lab data professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample user-reviewed health summaries linked to source records from a small authorized input set, with a transparent calculation of accepted summaries per user month and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five individuals combining wearable, log and lab data and inspect a recent example of health data sits in separate apps and wearables, so people cannot see patterns or get clear answers about their own records.
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 summaries per user month and corrections after review, 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 summaries per user month and corrections after review. 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 summaries per user month and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs user-reviewed health summaries linked to 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 summary styles, source mappings and review examples, together with reliable delivery for a narrow health niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for individuals combining wearable, log and lab data. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Illume Labs, TrueWellness, SmolVLM2, Kim Personal Health Assistant, Insightfull and Gym Hero. Compare this product with the buyer's present method on accepted summaries per user month and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data sync and processing, storage, reviewer hours, user revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of user-reviewed health summaries linked to source records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user privacy, source attribution, data accuracy and usage permissions. Users approve substantive changes and sharing scope. One wearable platform and one lab upload format; final medical interpretation remains with qualified clinicians. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.