Flagship Solution

Make AI coding work for your repository.

Pandora evaluates and optimizes complete AI coding systems against real engineering tasks, architecture and standards, creating the evidence needed to move from AI assistance toward trusted autonomy.

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01Repository analysisTasks, history and standards02EvaluationExams from real work03Optimization and tuningConfigurations compete, models are tuned04Trusted executionDelegated on evidence

01The autonomy gap

AI can write code.
Can it meet your standards?

Generic benchmarks tell you how models perform on common tasks. They do not tell you whether your full coding setup can deliver reliable work in your repositories, within your architecture and conventions.

Generic benchmarksYour engineering organization
Isolated, synthetic tasksReal features, fixes and refactors
Public, open-source codePrivate repositories with history and context
Generic notions of correctnessYour architecture, conventions and standards
A single model and promptComplete systems: agents, tools, context, configuration
Leaderboard scoresEvidence your engineering leadership can act on

02Repository-specific evaluation

Your repository becomes the exam.

Pandora turns real engineering tasks, approved reference implementations and repository standards into repeatable evaluations of complete AI coding systems.

  1. 01

    Real tasks

    Features, bug fixes and refactors grounded in your codebase.

  2. 02

    Approved reference

    Human-approved solutions establish a trusted reference.

  3. 03

    Run the system

    Evaluate the model, agent, tools and configuration together.

  4. 04

    Scores and insights

    Measure correctness, alignment, quality and efficiency.

03The complete system

The model is only one variable.

The same model can produce very different results depending on its agent, tools, instructions and repository context. Pandora evaluates the complete setup, not a model in isolation.

  1. ModelOne variable
  2. Coding agent
  3. Context
  4. Plugins & tools
  5. Skills & configuration
  6. Repository
AI Engineering System

Pandora measures the system, not only the model behind it.

04Continuous optimization

Improve the system. Prove the difference.

Change the model, agent, prompt, tools or context. Run each configuration against the same repository-specific exam and compare quality, reliability, speed and cost before choosing what to deploy.

Variables under test

  • Models
  • Agents
  • Prompts
  • Skills
  • Tools
  • Context
  • Providers
01Configure02Run03Score04Compare05Promote
Same exam.
Same standard.

05Repository-specific intelligence

From verified work to your own intelligence.

Pandora’s longer-term loop extends evaluation and optimization into data capture and model adaptation. Verified coding executions can become repository-specific training data for tuning open models, with each new version evaluated against the same standards.

  1. 01Proof of concept Repository exam Real engineering tasks and approved reference implementations.
  2. 02Proof of concept Evaluate Measure complete coding systems against repository standards.
  3. 03Proof of concept Optimize Improve models, agents, prompts, tools and context.
  4. 04In development Proxy Capture supported agent-model interactions through an integration layer.
  5. 05In development Capture Select successful, validated coding executions.
  6. 06In development Training dataset Prepare curated examples grounded in your repository.
  7. 07In development Fine-tune Adapt compatible open models to your engineering context.
  8. 08 Your intelligence Repository-adapted intelligence, deployable on infrastructure you control.
Re-evaluate · same exam
  • Proof of concept
  • In development

The model-adaptation workflow is an evolving capability. Pandora’s immediate focus is repository-specific evaluation, with optimization and tuning expanding over time.

06From assistance to autonomy

Autonomy is earned, one measured step at a time.

  1. 01

    Human-led

    Engineers own implementation; AI helps with selected tasks.

  2. 02

    AI-assisted

    AI completes more work while engineers validate outcomes.

  3. 03

    Trusted autonomy

    Qualified systems handle defined tasks within agreed quality gates and oversight.

Pandora provides the evidence to decide when more work can safely be delegated.

07Business value

Know what works. Improve what doesn’t.

  • 01

    Trust

    Make delegation decisions using evidence from your own repositories.

  • 02

    Quality

    Evaluate generated code against your architecture and conventions.

  • 03

    Efficiency

    Find the best quality, speed and cost trade-offs for your work.

  • 04

    Model independence

    Compare providers and configurations on the same tasks.

  • 05

    Proprietary intelligence

    Build reusable knowledge and training data from validated work.

  • 06

    Continuous improvement

    Re-evaluate every meaningful change before promoting it.

08Try Pandora

Explore Pandora yourself.

Open the interactive demo workspace to explore repository exams, evaluation scores, optimization and the model-adaptation vision. No installation required.

Try PandoraTechnical Pandora
trypandora.ioDemo workspace AI Control Center The control room for repository-native AI agents. AI Inspector94%repository evaluationAI Optimizer41%configuration efficiencyAI Spy TunerCustommodel adaptation Connect repoSetupBuild examAI InspectorOptimize configAI OptimizerFine-tune modelAI Spy Tuner Open trypandora.io

Pilot program

Prove AI coding on your codebase.

Start with a focused pilot: evaluate an AI coding setup against your real repository tasks and establish a measurable baseline for improvement.

Discuss a PilotTry Pandora