Source-linked code assistant and admin console

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 engineering teams and platform owners who need AI coding help inside their own infrastructure use it to solve "coding AI is rented across several tools, so code, prompts and review evidence sit outside the buyer's control and workflow"?
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 suggestions per developer hour and corrections after merge.
4. Measure, then decide. Track accepted suggestions per developer hour and corrections after merge; 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 approved model checkpoint set and one repository scope; final code review and merge decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: index the repository and retrieve relevant files; generate code suggestions with source links. Support the third module with operator review: run agent-style multi-step coding tasks. 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 code suggestions with named-owner approval. Retain the explicit scope boundary: One approved model checkpoint set and one repository scope; final code review and merge decisions remain with the engineering team.

What the build depends on. Repository upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model checkpoint set and one repository scope; final code review and merge decisions remain with the engineering team.

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repo or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked code suggestions with named-owner approval. 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 tool sprawl while keeping code and review evidence inside the buyer's own environment. Demonstrate a concrete source-linked code suggestions with named-owner approval using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and platform owners who need AI coding help inside their own infrastructure 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 code suggestions with named-owner approval from a small authorized input set, with a transparent calculation of accepted suggestions per developer hour and corrections after merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and platform owners who need AI coding help inside their own infrastructure and inspect a recent example of coding AI rented across several tools, so code, prompts and review evidence sit outside the buyer's control and workflow.
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 suggestions per developer hour and corrections after merge, 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 suggestions per developer hour and corrections after merge. 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 suggestions per developer hour and corrections after merge; 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 code suggestions with named-owner approval. 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 checkpoints, policy rules and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams and platform owners who need AI coding help inside their own infrastructure. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Qwen3-Coder, Mistral Code and MiMo-V2-Flash, plus generic coding assistants and in-house scripts. Compare this product with the buyer's present method on accepted suggestions per developer hour and corrections after merge. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, repository indexing, 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 code suggestions with named-owner approval. Track cost per accepted output, including correction work, unsuccessful cases and support.