Source-linked model workspace and admin console

For

Product and platform teams building AI features who need a language model they can run, inspect and administer themselves

Solves

Teams rent several model APIs and data tools, cannot see how outputs are produced, and cannot tune the workflow to their own review and data rules.

Delivers

Source-linked drafts, code, analyses and reports

Built in

about 4 weeks of creation time, MVP in 5 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 number of rented tools and keep model use inside one owned, auditable workspace.

1. Generate and understand natural language text.
2. Write code and follow detailed instructions.
3. Handle multi-step reasoning and problem-solving tasks.
4. Run goal-oriented agentic steps under named-owner approval.
5. Process long documents and extended interactions in a large context window.
6. Offer open-source licensing for use, modification and redistribution.
7. Provide multiple model variants for speed, cost and capability.
8. Use mixture-of-experts routing for efficient computation.
9. Clean, transform and process supplied data automatically.
10. Build customizable charts and dashboards.
11. Support a collaborative workspace for simultaneous project work.
12. Run predictive analytics for trend detection.
13. Connect databases, cloud storage and third-party applications.
14. Provide interactive dashboards with real-time insights.
15. Generate reports in multiple formats.
16. Offer a drag-and-drop builder for non-specialists.
17. Process and understand images alongside text.
18. Expose API access for deployment and scaling.
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 source-linked output set with source references and unresolved questions.

Everything these tools do, in one app

• Text generation and understanding Generates and comprehends natural language text for diverse tasks.Found in GLM-4.5, GPT-4.1 in the API, OpenAI Open Models and 1 more
• Coding and instruction following Writes code and follows detailed instructions accurately.Found in GLM-4.5, GPT-4.1 in the API, LongCat-2.0
• Reasoning tasks Handles complex reasoning and problem-solving tasks.Found in GLM-4.5, OpenAI Open Models
• Agentic functions Performs goal-oriented tasks autonomously.Found in GLM-4.5, OpenAI Open Models, LongCat-2.0
• Large context window Processes very long documents and extended interactions.Found in GLM-4.5, GPT-4.1 in the API, LongCat-2.0
• Open-source licensing Allows free use, modification, and redistribution under open licenses.Found in GLM-4.5, OpenAI Open Models, LongCat-2.0
• Multiple model variants Offers different model sizes to balance speed, cost, and capability.Found in GLM-4.5, GPT-4.1 in the API, OpenAI Open Models
• Mixture-of-experts architecture Activates subsets of parameters for efficient computation.Found in LongCat-2.0, Qwen 1.5 MoE
• Automated data processing Cleans, transforms, and processes data automatically.Found in DBRX, QWQ-Max
• Data visualization Creates customizable charts and dashboards for insights.Found in DBRX, QWQ-Max
• Collaborative workspace Enables team members to work on projects simultaneously.Found in DBRX
• Predictive analytics Uses built-in AI models for trend detection and predictions.Found in DBRX
• Third-party integrations Connects with databases, cloud storage, and other applications.Found in DBRX, QWQ-Max, LongCat-2.0
• Interactive dashboards Provides real-time insights through interactive interfaces.Found in QWQ-Max
• Advanced reporting Generates reports in multiple formats.Found in QWQ-Max
• Drag-and-drop interface Simplifies usage with a user-friendly drag-and-drop design.Found in QWQ-Max
• Vision and image understanding Processes and understands images alongside text.Found in GPT-4.1 in the API
• API integration Supports deployment and scalability through APIs.Found in GPT-4.1 in the API, OpenAI Open Models

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, code, datasets and images

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Code

5. 4

   Datasets and images

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Source-linked drafts, code, analyses and 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 stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Model variant, context limits and routing are configuration choices; final code, data and report checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workspace and model selection, Source-linked assistant, Data and dashboard builder, Admin console. Use a project list, a large central assistant and editor canvas, and a right-hand panel for sources, model variant, constraints and comments. Let users compare model variants and versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant source. Make the task-specific outcome source-linked drafts, code, analyses and reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, model variant selection, source 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

Customer-owned repositories, databases, cloud storage and permitted document sources. Start with file exchange and validate destination specifications before promising direct deployment. 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

   5 days

   One buyer segment, one recurring use case; first modules: generate and understand natural language text; write code and follow detailed instructions; handle multi-step reasoning and problem-solving tasks. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 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 product and platform teams building AI features who need a language model they can run, inspect and administer themselves use it to solve "teams rent several model APIs and data tools, cannot see how outputs are produced, and cannot tune the workflow to their own review and data rules"?
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 outputs per reviewer hour and corrections after approval.
4. Measure, then decide. Track accepted outputs per reviewer hour and corrections after approval; 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 model variant set, one approved input format and one bounded representative case set. Implement the first three task modules: generate and understand natural language text; write code and follow detailed instructions; handle multi-step reasoning and problem-solving tasks. Support agentic steps and data processing with operator review. 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 source-linked drafts, code, analyses and reports. Retain the explicit scope boundary: One model variant set, one approved input format and one bounded representative case set.

What the build depends on. Source upload and preview, asynchronous model jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist development QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One model variant set, one approved input format and one bounded representative case set.

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 project package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist data work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked drafts, code, analyses and 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 the number of rented tools and keep model use inside one owned, auditable workspace. Demonstrate a concrete source-linked drafts, code, analyses and reports set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams building AI features professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked drafts, code, analyses and reports set from a small authorized input set, with a transparent calculation of accepted outputs per reviewer hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams building AI features and inspect a recent example of rented model APIs and data tools, cannot see how outputs are produced, and cannot tune the workflow to their own review and data rules.
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 outputs per reviewer hour and corrections after approval, 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 outputs per reviewer hour and corrections after approval. 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 outputs per reviewer hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked drafts, code, analyses and 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 prompts, model configurations, review examples and data connectors, together with reliable delivery for a narrow development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and platform teams building AI features. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

GLM-4.5, DBRX, GPT-4.1 in the API, QWQ-Max, OpenAI Open Models, LongCat-2.0 and Qwen 1.5 MoE. Compare this product with the buyer's present method on accepted outputs per reviewer hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, data 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 source-linked drafts, code, analyses and reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, code provenance, data permissions and usage rights. Named owners approve substantive changes and deployment scope. One model variant set, one approved input format and one bounded representative case set. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.