Intelligence Infrastructure for Labs

FROM INTELLIGENCE TO CAPABILITY

"We build precision-engineered data that targets exactly where models fail—grounding AI in real execution and self-correction."

ACTION: READ_MULTI_FILE_DEPENDENCY -> OBS: SUCCESSSTATUS: SYNTHESIZING_RECURSIVE_DEBUGGING_TRACESAGENT_TRACE: PLAN -> EXECUTE -> FAIL -> SELF_CORRECTUPLOADED: 12,500_AGENT_TOOL_CALL_TRAJECTORIESPII_SCRUBBER: ACTIVE [CLEAN_RATE: 100%]SME_AUDIT: AGENT_REFLECTION_LOGS_V2_COMPLETEDREWARD_MODEL_SIGNAL: GROUND_TRUTH_FIDELITY_100%COMPILER_FEEDBACK: GROUNDED_IN_RUNTIME_EXECUTION
ACTION: READ_MULTI_FILE_DEPENDENCY -> OBS: SUCCESSSTATUS: SYNTHESIZING_RECURSIVE_DEBUGGING_TRACESAGENT_TRACE: PLAN -> EXECUTE -> FAIL -> SELF_CORRECTUPLOADED: 12,500_AGENT_TOOL_CALL_TRAJECTORIESPII_SCRUBBER: ACTIVE [CLEAN_RATE: 100%]SME_AUDIT: AGENT_REFLECTION_LOGS_V2_COMPLETEDREWARD_MODEL_SIGNAL: GROUND_TRUTH_FIDELITY_100%COMPILER_FEEDBACK: GROUNDED_IN_RUNTIME_EXECUTION
ACTION: READ_MULTI_FILE_DEPENDENCY -> OBS: SUCCESSSTATUS: SYNTHESIZING_RECURSIVE_DEBUGGING_TRACESAGENT_TRACE: PLAN -> EXECUTE -> FAIL -> SELF_CORRECTUPLOADED: 12,500_AGENT_TOOL_CALL_TRAJECTORIESPII_SCRUBBER: ACTIVE [CLEAN_RATE: 100%]SME_AUDIT: AGENT_REFLECTION_LOGS_V2_COMPLETEDREWARD_MODEL_SIGNAL: GROUND_TRUTH_FIDELITY_100%COMPILER_FEEDBACK: GROUNDED_IN_RUNTIME_EXECUTION
ACTION: READ_MULTI_FILE_DEPENDENCY -> OBS: SUCCESSSTATUS: SYNTHESIZING_RECURSIVE_DEBUGGING_TRACESAGENT_TRACE: PLAN -> EXECUTE -> FAIL -> SELF_CORRECTUPLOADED: 12,500_AGENT_TOOL_CALL_TRAJECTORIESPII_SCRUBBER: ACTIVE [CLEAN_RATE: 100%]SME_AUDIT: AGENT_REFLECTION_LOGS_V2_COMPLETEDREWARD_MODEL_SIGNAL: GROUND_TRUTH_FIDELITY_100%COMPILER_FEEDBACK: GROUNDED_IN_RUNTIME_EXECUTION

CORE VERTICALS

We focus on high-impact data slices, targeting exactly where model reasoning collapses.

Code Intelligence

Train models to behave like real software engineers.

  • Execution-grounded debugging traces
  • Multi-file & large codebase reasoning
  • Tool-using agents (compiler, CLI, debugger)
  • Secure coding & vulnerability intelligence
  • Refactoring & adversarial edge cases

Outcome

Models that debug reliably and reason across systems.

Agentic Systems

Train models to act like reliable autonomous agents.

  • Full task trajectories (end-to-end execution)
  • Execution-grounded feedback systems
  • Reflection & self-correction loops
  • Multi-agent coordination workflows
  • Environment interaction & tool-use data

Outcome

Systems that use tools correctly and recover from failure.

PRECISION
TRAJECTORIES

We provide execution-grounded data that captures the entire lifecycle of a task. Switch between our core verticals to see how we structure multi-step reasoning.

Code Intelligence

"From bug report to verified fix."

Agentic Systems

"Planning, execution, and recovery."

execution_grounded_debugging.jsonl
{
  "task_id": "debug_trace_001",
  "category": "execution_grounded_debugging",
  "language": "python",
  "context": {
    "repo": "internal/microservice-auth",
    "file": "auth/token_validator.py"
  },
  "problem": {
    "description": "Token validation intermittently fails for valid JWTs in production.",
    "symptom": "Users randomly get logged out despite valid tokens.",
    "logs": [
      "ValueError: Signature verification failed",
      "TokenExpiredError: Signature has expired"
    ]
  },
  "execution_trace": [
    {
      "step": 1,
      "action": "Inspect token decoding logic",
      "observation": "Clock skew not handled"
    },
    {
      "step": 2,
      "action": "Compare server time vs token expiry",
      "observation": "5-10 sec drift detected"
    },
    {
      "step": 3,
      "action": "Test with leeway parameter",
      "observation": "Validation succeeds"
    }
  ],
  "root_cause": "Token validation fails due to missing clock skew tolerance.",
  "fix": {
    "code_patch": "jwt.decode(token, key, algorithms=['HS256'], leeway=10)"
  },
  "learning_signal": {
    "bug_type": "time_synchronization",
    "skill": ["debugging", "distributed_systems"]
  }
},
  "execution_trace": [
    {
      "step": 1,
      "action": "Inspect decoding logic",
      "observation": "Clock skew not handled"
    },
    {
      "step": 2,
      "action": "Add 10s leeway",
      "observation": "Validation succeeds"
    }
  ],
  "fix": "jwt.decode(token, key, leeway=10)"
}

THE PRECISION PIPELINE

We don’t sell volume. We deliver the measurable improvements required to turn models into reliable autonomous systems.

Target

Capability Gap Identification

We analyze exactly where your model breaks—whether it's multi-file reasoning, tool-use recovery, or adversarial code scenarios.

Engineer

Precision Data Synthesis

Instead of bulk scraping, we engineer high-impact data slices that target identified weaknesses with surgical accuracy.

Verify

Execution-Grounded Validation

Every data point is grounded in real runtime behavior. We verify code execution and agent trajectories in live sandboxed environments.

Refine

Iterative Feedback Loops

We provide RLHF and reward modeling signals based on failure scenarios, enabling the model to improve via reflection and self-correction.

Outcome: Models that don’t just generate—they troubleshoot, self-correct, and master multi-agent workflows.

DATA INTEGRITY

Engineering the trust required for autonomous systems to operate in production environments.

Runtime-Grounded Verification

Unlike scraped datasets, our code traces are grounded in runtime behavior, ensuring 100% execution accuracy.

Surgical Data Hygiene

Every precision slice is deduplicated and balanced for specific capability gaps, preventing model collapse.

Sanitized Code Provenance

Rigorous PII scrubbing and enterprise-grade security filtering for all multi-file reasoning datasets.

Adversarial Integrity

We focus on the failure scenarios big labs miss, providing the edge cases necessary for real-world reliability.

JOIN THE INTELLECT

Help us build the precision data that turns models into engineers and systems into agents.

Don't see your specialty? Send your GitHub/Portfolio toinfo@finemodel.ai

REQUEST A
DATA SAMPLE

Ready to benchmark our reasoning chains? Contact our data strategy team for catalog access, volume pricing, or custom dataset synthesis.

Custom Synthesis

Targeted data generation for niche technical domains.

Global Compliance

GDPR, SOC2, and IP-cleared datasets for global labs.

Our HQ

Built for the next generation of AI capability, our lab focuses on creating high-quality data that enables models to reason, code, and perform at an engineering level.

Location

Maharashtra, India / Remote Global

Business Hours

Mon — Fri: 9am - 6pm (GMT+5:30)

Registered As

FINE MODEL AI LLP

FINE MODEL AI HQ