07 / ENTERPRISE RESEARCH & AI TEAMS

Your research. A smarter next test.

Finetune Studio is the model-improvement part of Djini’s enterprise vision: use research and device-test results to identify gaps, create training material, and evaluate a stronger next model.

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FINETUNE STUDIO
  1. 01
    RESEARCH + DEVICE LABValidated results and test traces
  2. 02
    FINETUNE STUDIOIdentify a gap → prepare training data
  3. 03
    CANDIDATE MODELTrain a proposed update
  4. 04
    EVALUATIONHeld-out tests → adoption decision

SECURITY MODEL IMPROVEMENTFinetune StudioValidated test dataEnterprise roadmap

You ask Djini

“Find where the security model failed on our evaluation tasks. Use validated research and device outcomes to improve it, then prove the update on held-out tests.”

What your team gets

A model-improvement loop with identified gaps, curated training material, and evaluation results that inform whether to use the new model.

Access & scope

Enterprise roadmap. Automated research loops and the specialised fine-tuned model are described as upcoming in the supplied roadmap.

THE WORK BEHIND THE OUTCOME

Keep the learning from the work you do.

A useful test produces more than a finding. It also shows which approaches worked, where the model struggled, and what the device actually did. Finetune Studio brings those outcomes into a loop for improving the testing capability.

  1. 01

    Collect validated outcomes

    Bring together research results and device-test evidence. Separate reproduced outcomes from incomplete leads before considering them as training material.

  2. 02

    Identify the skill gaps

    Evaluate the model on defined security tasks. Use failures and weak areas to choose what the next training cycle should address.

  3. 03

    Prepare and train

    Develop targeted examples from the retained work and run the agreed training process within the enterprise model setup.

  4. 04

    Evaluate before reuse

    Compare the candidate model on held-out tasks and regression checks. Use the result to decide whether the next testing cycle should adopt it.

FINETUNE STUDIO

A learning loop built on test evidence.

Illustrative Finetune Studio roadmap workflow. This is not a screenshot or demonstration of a generally available training interface.

WHAT YOUR TEAM TAKES AWAY

Retained research value

Use validated outcomes as material for improving the testing capability.

Targeted model development

Train around identified weaknesses instead of assuming every update is better.

An evaluation record

Keep the evidence behind the decision to adopt or reject a candidate model.

Custom model fine-tuning service

Build your team's knowledge into a model you own.

Work with us to curate internal security reports into a dataset and fine-tune a compatible base model for local deployment. Your organisation owns the dataset and resulting fine-tuned weights.

Explore the service →

A few good questions

Before you go deeper.

Is the automated learning loop available in every plan?

No. The supplied product roadmap marks automated research loops and the specialised fine-tuned model as upcoming. This page presents the enterprise direction and an evaluation discussion, not a self-service feature entitlement.

Can we use our own provider now?

Yes. Model choice through supported providers and BYOK is separate from Finetune Studio. AI SAST can use an OpenAI-compatible endpoint, including OpenRouter or a reachable local endpoint.

Does one successful test prove a model improved?

No. Evaluate against a defined set of held-out tasks and check for regressions. The workflow should distinguish an individual success from consistent improvement across the intended workload.

PUT DJINI TO WORK

Build the next test from what you learned.

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Finetune Studio and automated learning loops: enterprise roadmap discussion.

THIS WORKFLOW, IN YOUR INDUSTRY