Model comparison and selection workspace

For

Product and engineering teams choosing between AI models or options for a defined task

Solves

Model choices are made from scattered benchmarks, vendor claims and ad-hoc trials that are hard to reproduce or defend.

Delivers

Reviewed selection report linked to raw evidence

Built in

about 5 weeks of creation time, MVP in 6 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 selection time while producing a documented, reviewable comparison.

1. Compare outputs from multiple AI models side by side.
2. Collect community votes and structured feedback on candidate outputs.
3. Define custom evaluation criteria and weights.
4. Run test cases in a spreadsheet-like grid and score them.
5. Access a range of AI models from one workspace.
6. Generate written content for test cases and drafts.
7. Display results in real time as they update.
8. Share polls, comparisons and apps by link.
9. Test prompts against models in an interactive playground.
10. Expose an API for programmatic runs and integration.
11. Support team collaboration and shared review.
12. Analyze uploaded files for context-aware answers.
13. Capture AI assistance from a browser extension on any webpage.
14. Adjust output tone and style to a defined voice.
15. Check grammar and readability of generated text.
16. Suggest content plans and ideas for test cases.
17. Show leaderboards and benchmarks for candidate models.
18. Report cost metrics such as cost per 1k tokens.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed selection report linked to raw evidence with source references and unresolved questions.

Everything these tools do, in one app

• Side-by-side model comparison Allows users to compare outputs or performance of multiple AI models simultaneously.Found in MIOSN, Langtail 1.0, Aymo AI and 1 more
• Community voting and feedback Enables gathering opinions from a community to aid decision-making.Found in thisorthis.ai
• Customizable evaluation criteria Lets users define their own metrics or priorities for assessment.Found in MIOSN
• Spreadsheet-like testing interface Provides a grid format to create, run, and score test cases easily.Found in Langtail 1.0
• Multi-model access Offers access to a wide range of AI models in one place.Found in Aymo AI, LLM Stats
• AI content generation Generates written content such as articles and social media posts.Found in Contentable.ai
• Real-time results display Shows results immediately as they are updated.Found in thisorthis.ai
• Shareable polls and apps Allows creating and sharing polls or AI apps via links or social media.Found in thisorthis.ai, Langtail 1.0
• Interactive playground Provides an environment to test prompts against models.Found in LLM Stats
• API access Enables programmatic access to models for automation and integration.Found in LLM Stats
• Team collaboration Facilitates sharing and collaboration among team members.Found in Aymo AI
• File analysis Accepts various file types for context-aware answers.Found in Aymo AI
• Chrome extension Integrates AI capabilities into any webpage via a browser extension.Found in Aymo AI
• Customizable tone and style Adjusts output to match specific brand voices or writing styles.Found in Contentable.ai
• Grammar and readability checks Enhances text quality by checking grammar and readability.Found in Contentable.ai
• Content planning and suggestions Assists with brainstorming and planning content ideas.Found in Contentable.ai
• Leaderboards and benchmarks Provides community-driven rankings and performance benchmarks.Found in LLM Stats
• Cost metrics Shows cost information such as cost per 1k tokens for budgeting.Found in LLM Stats

What goes in, what comes out

The workflow

1. In
   Start with

   Task cases, evaluation criteria, model access, cost constraints and permitted source files

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect task cases

4. 3

   Evaluation criteria

5. 4

   Model access and cost constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed selection report linked to raw 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. One defined task family and approved model list; final selection and production decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Task and criteria setup, Side-by-side comparison grid, Selection report and delivery. Use a thumbnail gallery for evaluation projects, a large central comparison grid, and a right-hand panel for criteria, cost metrics and comments. Let users compare model outputs side by side. Display draft, changes requested and approved states. Provide a shareable comparison link with comments anchored to the relevant test case. Make the task-specific outcome reviewed selection report linked to raw evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, model versions, test case versions, community votes, approval states, usage allowances, run 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

Buyer-owned task cases, authorized model endpoints and permitted research sources. Cloud storage, design-file import/export and publishing 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: compare outputs from multiple AI models side by side; collect community votes and structured feedback on candidate outputs. 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 product and engineering teams choosing between AI models or options for a defined task use it to solve "model choices are made from scattered benchmarks, vendor claims and ad-hoc trials that are hard to reproduce or defend"?
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 selection decisions per evaluation hour and rework after model choice.
4. Measure, then decide. Track accepted selection decisions per evaluation hour and rework after model choice; 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 defined task family and approved model list; final selection and production decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: compare outputs from multiple AI models side by side; collect community votes and structured feedback on candidate outputs. Support the remaining modules with operator review: define custom evaluation criteria and weights; run test cases in a spreadsheet-like grid and score them. 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 selection report linked to raw evidence. Retain the explicit scope boundary: One defined task family and approved model list; final selection and production decisions remain human.

What the build depends on. Asset upload and preview, asynchronous model runs, editable version history, reviewer access and tested export formats. High-fidelity evaluation requires specialist model QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One defined task family and approved model list; final selection and production 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 evaluation package. Offer a monthly evaluation allowance after repeat demand. Quote complex integrations or specialist model access separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed selection report linked to raw 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 selection time while producing a documented, reviewable comparison. Demonstrate a concrete reviewed selection report linked to raw evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and engineering teams choosing between AI models or options for a defined task professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed selection report linked to raw evidence from a small authorized input set, with a transparent calculation of accepted selection decisions per evaluation hour and rework after model choice and no promised savings.

The first 30 days

1. Week 1: interview five product and engineering teams choosing between AI models or options for a defined task and inspect a recent example of model choices made from scattered benchmarks, vendor claims and ad-hoc trials that are hard to reproduce or defend.
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 selection decisions per evaluation hour and rework after model choice, 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 selection decisions per evaluation hour and rework after model choice. 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 selection decisions per evaluation hour and rework after model choice; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed selection report linked to raw 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 task cases, evaluation criteria and reviewer corrections, together with reliable delivery for a narrow selection niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and engineering teams choosing between AI models or options for a defined task. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

thisorthis.ai, MIOSN, Langtail 1.0, Aymo AI, Contentable.ai and LLM Stats, plus spreadsheets and ad-hoc trials. Compare this product with the buyer's present method on accepted selection decisions per evaluation hour and rework after model choice. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference attempts, 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 selection report linked to raw evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and publication scope. One defined task family and approved model list; final selection and production decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.