Overview
AI is only valuable when it works reliably in production — not just in a notebook. Elytic Labs helps organizations move beyond proof-of-concept and build AI and machine learning systems that are production-ready, explainable, and actually trusted by the teams using them.
Whether you're evaluating where AI can create leverage in your business, building a custom LLM-powered application, or operationalizing a model that's been sitting on a data scientist's laptop for six months — Evan Watson brings the engineering discipline to take it across the finish line.
What's included
- AI strategy & use-case assessment — identifying where machine learning creates genuine ROI versus where it adds unnecessary complexity
- Custom LLM application development — RAG pipelines, agents, fine-tuning, prompt engineering, and LLM-powered workflows using LangChain, LlamaIndex, and the OpenAI / Anthropic APIs
- Model development & training — supervised, unsupervised, and reinforcement learning pipelines built with PyTorch, Scikit-learn, and Hugging Face
- MLOps & model operationalization — CI/CD for models, experiment monitoring with MLflow, model registries, monitoring for drift and degradation
- Vector database design & integration — Pinecone, Weaviate, pgvector — semantic search, retrieval systems, and knowledge bases
- Responsible AI review — bias auditing, explainability layers (SHAP, LIME), and documentation for regulated industries
Platforms & tools
Python · PyTorch · Scikit-learn · Hugging Face · LangChain · LlamaIndex · OpenAI API · Anthropic API · MLflow · Pinecone · Weaviate · pgvector · AWS SageMaker · Vertex AI · Docker
Ideal for
- Product teams that have a working prototype and need someone to productionize it
- Companies evaluating AI vendors or approaches and needing an independent technical perspective
- Data teams with strong analysis skills but limited ML engineering experience
- Enterprises exploring LLM integration into existing workflows or internal tools
- Startups building AI-native products who need a senior ML engineer without a full-time hire
A note on scope
AI projects have a higher degree of inherent uncertainty than traditional software — model performance depends on data quality, and business requirements evolve as teams see what's possible. Elytic Labs scopes AI engagements in phases: a focused discovery and feasibility sprint first, followed by build phases with clearly defined checkpoints. This protects the client's budget and ensures the work stays grounded in what will actually ship.