What we do
Integrate large language models into products, build custom ML models, set up recommendation engines and search systems, and establish MLOps pipelines for training, evaluation, and serving.
Deliverables
- Deployed model or LLM integration
- Evaluation report (metrics, benchmarks, cost analysis)
- MLOps pipeline (training, versioning, serving)
- API serving layer
- Cost estimate for inference at target scale
Scope examples
- LLM integration: RAG pipelines, structured output, tool use, prompt engineering, evaluation frameworks.
- Custom models: Classification, NER, recommendation — trained on your data with experiment tracking.
- Vector search: Embedding pipelines, similarity search, hybrid search (keyword + semantic).
- MLOps: Model versioning, A/B testing infrastructure, automated retraining pipelines.
Tech stack defaults
- LLM provider: Anthropic (Claude)
- Embeddings: Voyage AI or OpenAI
- Framework: Direct SDK calls (LlamaIndex for RAG-specific work)
- Training: PyTorch
- Experiment tracking: MLflow
- Serving: FastAPI + ONNX Runtime, or managed services (SageMaker, Vertex AI)
- Vector store: pgvector or Qdrant