AI & machine learning.Not hype. Working systems.
LLM integration, RAG, agents, vision, prediction. AI embedded as a value-generating layer, not decoration. Honest about when it isn't worth it.
Six areas of depth.
- 01
LLM Integration
Right model choice, prompt architecture, cost-quality tradeoff.
- 02
RAG & Document Intelligence
Vector DB, chunking strategy, reranker, cited responses.
- 03
AI Agents
Tool-using, decision-making, auditable systems.
- 04
MLOps & Evaluation
Eval suites, A/B testing, cost monitoring, prompt versioning.
- 05
Computer Vision
Document extraction, quality control, anomaly detection.
- 06
Predictive Models
Demand forecasting, churn, risk scoring, pricing.
The tools we reach for.
No stack is universally right. These are the tools we work with every day and pick based on fit.
- Claude (Anthropic)
- GPT (OpenAI)
- Gemini
- Llama & Mistral
- LangChain
- LangGraph
- LlamaIndex
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Langfuse
- Helicone
- Model Context Protocol (MCP)
How we work.
80% of AI projects end up as “works in demo, never used in production”. The difference: evals, observability, cost tracking, and human-in-the-loop design. We don't start with the model. We start with the problem.
Outcomes that live in production.
Enterprise knowledge base RAG
40K documents, cited answers, role-based access.
- 40K docs
- 92% answer acceptance
- p95 1.8s
Operator copilot
Call summarization, action suggestions, CRM note drop. One UI.
- 4.3× faster
- 38% AHT ↓
- Audit log