Diet adherence evidence and reporting workspace

For

Dietitians, clinic teams and health coaches guiding clients through a prescribed diet

Solves

Clients log food inconsistently, plans ignore restrictions, and progress evidence is scattered across several rented apps.

Delivers

Reviewer-approved diet adherence report linked to the client's plan

Built in

about 6 weeks of creation time, MVP in 7 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 manual diet-review effort while keeping every recommendation traceable.

1. Build personalized meal plans from preferences and goals.
2. Record meals through a simple client logging interface.
3. Track calories and macronutrients in real time.
4. Suggest recipes that fit the prescribed diet.
5. Offer AI food suggestions for balanced choices.
6. Scan food items to capture details quickly.
7. Calculate daily calorie targets from intake data.
8. Respect allergies, diets and foods the client never wants again.
9. Merge ingredients into one grouped shopping list.
10. Monitor progress with analytics and feedback.
11. Connect to a nutrition database of common foods.
12. Sync with fitness and health apps.
13. Forecast intake trends and flag risk patterns.
14. Visualize intake and adherence interactively.
15. Let reviewers customize dashboards for key indicators.
16. Deliver automated summaries and insights.
17. Capture repetitive review actions as reusable macros.
18. Support scripting for complex review sequences.
19. Run workflows across connected programs.
20. Schedule macros at set times or events.
21. Provide a library of pre-made macros for common tasks.

Everything these tools do, in one app

• Personalized meal plans Creates meal plans tailored to the user's preferences and goals.Found in AI keto coach, PlanEat AI
• Food logging Lets users record what they eat through a simple interface.Found in Capy Diet
• Nutrition tracking Tracks macronutrients and calories in real time.Found in AI keto coach
• Recipe suggestions Offers recipe ideas that fit the user's diet.Found in AI keto coach
• AI food suggestions Provides personalized food suggestions to encourage balanced choices.Found in Capy Diet
• AI food scanner Simplifies capturing food details to improve tracking convenience.Found in Capy Diet
• Calorie target calculation Calculates daily calorie targets based on user-provided information.Found in PlanEat AI
• Dietary restriction respect Chooses recipes that respect diets, allergies, and foods the user never wants again.Found in PlanEat AI
• Grouped shopping list Merges all ingredients into a single, grouped shopping list to simplify grocery shopping.Found in PlanEat AI
• Progress monitoring Monitors progress with detailed analytics and feedback.Found in AI keto coach
• Nutrition database integration Connects to a nutrition database for a wide range of common foods.Found in Capy Diet
• Health app integration Syncs with common fitness and health apps for seamless data syncing.Found in AI keto coach
• Predictive analytics Provides advanced predictive analytics for trend forecasting and risk assessment.Found in PrevessAI App
• Data visualization Offers interactive data visualization tools to simplify complex information.Found in PrevessAI App
• Customizable dashboards Allows users to customize dashboards to monitor key performance indicators.Found in PrevessAI App
• Automated reporting Delivers timely summaries and insights automatically.Found in PrevessAI App
• Macro recorder Captures repetitive actions to create macros.Found in MacroMate
• Scripting support Supports scripting to create complex automation sequences.Found in MacroMate
• Cross-application compatibility Enables workflows across multiple programs.Found in MacroMate
• Scheduling Runs macros at specified times or events.Found in MacroMate
• Pre-made macro library Provides a built-in library of pre-made macros for common tasks.Found in MacroMate

What goes in, what comes out

The workflow

1. In
   Start with

   Client intake, dietary restrictions, logged meals, nutrition database entries and health-app data

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect client intake

4. 3

   Dietary restrictions

5. 4

   Logged meals

6. 5

   Nutrition database entries and health-app data

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved diet adherence report linked to the client's plan

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. One prescribed diet template and one nutrition database; final clinical judgment and allergy checks remain with the dietitian. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Client intake and restrictions, Editable plan and log review, Client progress and delivery. Use a client list with adherence status, a central review canvas for plans, logs and flagged items, and a right-hand panel for restrictions, targets and comments. Let users compare planned versus logged intake side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant meal or day. Make the task-specific outcome reviewer-approved diet adherence report linked to the client's plan visible beside its evidence, review state and value baseline.

Accounts and administration

Client ownership, plan versions, reviewer 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 intake forms, authorized health-app exports and permitted nutrition sources. Cloud storage, health-app sync and report export destinations. Start with file exchange and validate destination specifications before promising direct clinical system integration. 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: build personalized meal plans from preferences and goals; record meals through a simple client logging interface. 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 dietitians, clinic teams and health coaches guiding clients through a prescribed diet use it to solve "clients log food inconsistently, plans ignore restrictions, and progress evidence is scattered across several rented apps"?
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: Reviewed client-weeks per dietitian hour and plan corrections after review.
4. Measure, then decide. Track reviewed client-weeks per dietitian hour and plan 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 prescribed diet template and one nutrition database; final clinical judgment and allergy checks remain with the dietitian. Implement one approved input format, a bounded representative case set and the first two task modules: build personalized meal plans from preferences and goals; record meals through a simple client logging interface. Support the third module with operator review: track calories and macronutrients in real time. 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 reviewer-approved diet adherence report linked to the client's plan. Retain the explicit scope boundary: One prescribed diet template and one nutrition database; final clinical judgment and allergy checks remain with the dietitian.

What the build depends on. Client intake and log upload, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity clinical use requires specialist dietitian QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One prescribed diet template and one nutrition database; final clinical judgment and allergy checks remain with the dietitian.

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 client cohort. Offer a monthly review allowance after repeat demand. Quote complex clinical integrations or multi-site rollouts separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved diet adherence report linked to the client's plan. 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 diet-review effort while keeping every recommendation traceable. Demonstrate a concrete reviewer-approved diet adherence report linked to the client's plan using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Dietitians, clinic teams and health coaches professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant clinical or practitioner events.

Lead magnet

A reviewed sample reviewer-approved diet adherence report linked to the client's plan from a small authorized input set, with a transparent calculation of reviewed client-weeks per dietitian hour and plan corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five dietitians, clinic teams and health coaches guiding clients through a prescribed diet and inspect a recent example of clients logging food inconsistently, plans ignoring restrictions, and progress evidence scattered across several rented apps.
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 reviewed client-weeks per dietitian hour and plan 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: Reviewed client-weeks per dietitian hour and plan 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

Reviewed client-weeks per dietitian hour and plan 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 reviewer-approved diet adherence report linked to the client's plan. 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 diet templates, restriction rules and review examples, together with reliable delivery for a narrow clinical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for dietitians, clinic teams and health coaches guiding clients through a prescribed diet. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AI keto coach, MacroMate, Capy Diet, PlanEat AI and PrevessAI App, plus spreadsheets and paper logs. Compare this product with the buyer's present method on reviewed client-weeks per dietitian hour and plan 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

Model calls, nutrition database access, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved diet adherence report linked to the client's plan. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve client privacy, source attribution, allergy accuracy and usage permissions. Dietitians approve substantive plan changes and clinical scope. One prescribed diet template and one nutrition database; final clinical judgment and allergy checks remain with the dietitian. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.