Our services
Expertise for your next AI challenge.
Explore the support your project needs, from training data and agent evaluation to safety testing and specialized workflows.
What We Do
Services & Solutions
Foundation Model Training
Human-generated data pipelines for pre-training, fine-tuning, and RLHF — designed to your model's exact specifications at every stage of development.
- 01Model fine-tuning support
- 02RLHF design & execution
- 03RLVR verification & iteration
- 04Benchmarking & stress testing
Task design & delivery
Starting pointTarget capabilities, model gaps, and a sample specification.
- Expert demonstrations and preference pairs
- Reference answers with scoring criteria
- Versioned datasets and review records
Quality approachDefine the rubric first; calibrate a pilot before expanding the dataset.
Agent Optimization & Evaluation
Structured evaluation and human-in-the-loop refinement that help autonomous agents move from prototype to production-grade reliability.
- 01Agent behavior eval & adversarial testing
- 02Prompt design & process optimization
- 03Explainability & reliability assurance
Task design & delivery
Starting pointA target workflow, tool interfaces, and completion requirements.
- Task briefs and reproducible workspaces
- Execution traces and evaluation harnesses
- Failure analysis and improvement priorities
Quality approachInspect both the submitted artifact and the steps used to produce it.
AI Safety & Red Teaming
Adversarial testing, bias auditing, and safety evaluation by trained specialists — identifying model vulnerabilities before they reach users.
- 01Adversarial prompt testing
- 02Bias auditing & mitigation
- 03Safety benchmark design
- 04Red team coordination
Task design & delivery
Starting pointIntended users, operating boundaries, and known failure modes.
- Adversarial scenarios and risk taxonomy
- Annotated failures and reviewer decisions
- Safety evaluation and regression sets
Quality approachSpecify severity and expected behavior so findings can be reviewed consistently.
Vertical AI Solutions
Domain-expert annotation and evaluation for healthcare, legal, finance, and other specialized industries where generic training data falls short.
- 01Medical imaging & diagnosis assist
- 02Financial risk management
- 03E-commerce recommendations & NLP
- 04Embodied AI & robotics
- 05Simulation data pipeline
Task design & delivery
Starting pointDomain materials, a decision to support, and expert review criteria.
- Domain task and annotation specifications
- Evidence-linked reference responses
- Capability-level evaluation reports
Quality approachMatch reviewer experience to the task and resolve disagreements against source evidence.
Domain Expertise
Expert Work Has Context
Start with the evidence, decisions, and deliverables your specialists recognize. These illustrative workflows can shape the scope of a project.
Finance
- Input
- Model versions, source documents, and a revised brief
- Judgment
- Resolve assumptions and explain changes
- Output
- Updated model with an evidence trail
Healthcare
- Input
- De-identified records and review criteria
- Judgment
- Connect findings and identify missing evidence
- Output
- Structured review with cited observations
Legal
- Input
- Contract versions, reference clauses, and comments
- Judgment
- Compare obligations and document revisions
- Output
- Annotated comparison with review rationale
Engineering
- Input
- Design revisions, simulation results, and constraints
- Judgment
- Trace a change through its dependent checks
- Output
- Change package with verification records
Take the next step
From a service to a clear project.
Explore training programs
See example project scopes, delivery formats, and evaluation methods.
Inspect the sample library
Open synthetic demonstrations with inputs, reference outputs, and evaluation criteria.
Discuss your project
Share your model, capability gap, and delivery requirements with our team.