AI, engineered end to end

LLM Evaluation & Benchmarking

Know how a model behaves before it ships.

• Evaluation harnesses & task suites
• Quality and safety benchmarks
• Regression tests across model versions

Training & Fine-tuning

Adapt open models to your data and your task.

• Domain fine-tuning & instruction tuning
• Distillation into smaller models
• Dataset curation from your sources

Optimization & Compression

Make models small and fast enough to run anywhere.

• Quantization & pruning
• On-device and edge optimization
• Latency and memory profiling

Observability & Monitoring

Keep deployed models healthy in production.

• Inference tracing & logging
• Drift and quality tracking
• Alerting on regressions

Multi-Modal & Computer Vision

For the problems that go beyond text.

• Vision and image understanding
• Speech and audio models
• Combined multi-modal pipelines

Retrieval & Agents

Ground models in your own knowledge and tools.

• Private retrieval over your data
• Tool-using, multi-step agents
• Model Context Protocol integrations

Safety & Guardrails

Keep private AI inside your rules.

• Policy enforcement & content filters
• Alignment and refusal tuning
• Red-teaming and abuse testing

Data & Synthetic Data

The fuel behind evaluation and training.

• Curation and cleaning
• Labelling and annotation
• Synthetic data generation

Model Serving & MLOps

The bridge from lab bench to your ground.

• Serving in your own network
• Autoscaling and cost control
• Model CI/CD and versioning

Research & Evaluate

Select, benchmark and evaluate candidate models against your task, quality and safety bar.

Train & Optimize

Fine-tune, distill and compress until the model fits the device and the data it has to run on.

Deploy & Observe

Serve it in your own environment, add guardrails, and monitor quality and drift once it's live.

Productize & Apply

Ship it as part of the platform stack and into industry solutions.