Human-Written Coding Data for AI Models

Models learn best from code that is correct, original, and explained

Scraped code is noisy and often duplicated, and synthetic data repeats the mistakes of the model that produced it. Coding models improve fastest with data that is:

• Correct: Solutions that are run and tested, not just plausible.

• Original: Written from scratch by humans, not copied from public repos or generated by AI.

• Explained: Clear reasoning showing why the code works, not only the final answer.

• Judged by engineers: Reviews and ratings from people who write production code.

What We Deliver

1. Coding Tasks & Tested Solutions

Original problems with working reference solutions and test cases, from single functions to multi-file features. For years we have written original programming problems and solutions across dozens of languages and frameworks.

2. Step-by-Step Reasoning Traces

Written walkthroughs of how an engineer understands a problem, plans an approach, and verifies the result. Our engineers already explain code step by step in 1:1 mentoring and technical teaching.

3. Code Review & Critique

Line-level reviews of flawed code covering bugs, security issues, performance problems, and readability, with corrected versions. This is work our team does daily in client code reviews and in rescuing broken apps.

4. Debugging Tasks

Broken code with a failing behavior, a documented root cause, and the fix. These come from real debugging and app-rescue work.

5. Preference Ratings (RLHF)

Side-by-side comparisons of model responses, rated on correctness, efficiency, security, readability, and instruction-following. Each rating includes a written reason.

Coverage

Sample Batch

Before any commitment, we write a small sample batch matched to your task type, languages, and format, so you can judge quality on your own criteria.

Example record (preference rating):

{
  "task_id": "pref_0142",
  "language": "python",
  "prompt": "Implement a thread-safe LRU cache with TTL expiry.",
  "response_a": "...",
  "response_b": "...",
  "preferred": "b",
  "scores": {
    "correctness": { "a": 2, "b": 5 },
    "efficiency": { "a": 3, "b": 4 },
    "security": { "a": 4, "b": 4 },
    "readability": { "a": 4, "b": 4 },
    "instruction_following": { "a": 3, "b": 5 }
  },
  "rationale": "Response A reads and evicts without holding the lock, causing a race under concurrent access. Response B guards all mutations and handles TTL on read.",
  "reviewed": true
}

How It Works

1. Scope

We agree on task types, languages, difficulty, output format, and guidelines with your team.

2. Sample

We deliver a sample batch. You review it, we refine the guidelines together.

3. Pilot

A 2-week pilot at small volume, so you can check quality and throughput before scaling.

4. Scale

A dedicated team continues on an agreed weekly schedule, delivered in your format or inside your platform.

How We Keep Quality High

• Matched engineers: Each task goes to an engineer experienced in that language and domain.

• Run and tested: Solutions are executed against test cases before delivery.

• Second review: A senior engineer checks every item for correctness and guideline fit.

• Written from scratch: Contributors follow strict originality rules — no copying from public code or AI tools.

• Feedback loop: Your reviewer comments go straight back into the guidelines and to the engineers.

Who Does the Work

Our in-house engineering team, supported by our network of vetted developers. These are the same people who deliver client projects, debug production apps, and teach programming. They are working engineers, not general annotators.

Engagement Models

Per-Task

A fixed price per accepted task. Best for well-defined, repeatable datasets.

Dedicated Team

Reserved engineers billed hourly or monthly, working in your tools and guidelines. Best for ongoing or changing needs.

Supply Partner for Data Platforms

Specialized coding capacity for human-data companies. Our engineers work inside your platform and rubrics, with our team lead reviewing work first.

Human Coding Data vs. Alternatives

Confidentiality & Ownership

• An NDA with every client, and confidentiality terms for every contributor

• All delivered data is assigned to the client

• Work in your platform or an access-restricted workspace

• Client tasks, prompts, and model outputs are never reused across projects

FAQ

Who is this for?

AI labs, companies fine-tuning coding models, AI coding tool startups, and human-data platforms that need coding specialists.

Who creates the data?

Engineers from our in-house team and vetted developer network, matched to each task's language and domain. A senior engineer reviews every item.

How do you avoid AI-generated or copied data?

Contributors write every task from scratch under strict originality rules, and every item goes through senior review before delivery.

Can you work inside our annotation platform?

Yes. We can work in your tools or deliver files in your required format (for example JSONL or CSV).

Who owns the data?

You do. All delivered data is assigned to the client.

How do we start?

Request a sample batch. Once you approve it, we run a 2-week pilot and then scale.

Start With a Sample Batch

Tell us your task types and languages. We'll write a matching sample so you can judge the quality yourself.

Book a Scoping Call