AI Solutions
LLM integration, retrieval-augmented generation, ML pipelines. Built for the use cases that earn their cost.
AI that ships, and stays running.
Custom AI tooling embedded in production workflows. We pick the model for the job: hosted LLMs where speed of integration matters, fine-tuned or open-weight where data control or cost does. Either way the surface gets the same engineering rigor as the rest of your stack.
Every AI feature ships with evaluation harnesses, cost guardrails, and observability. So you can answer "is this still working?" six months in, not just at launch.
AI capabilities
LLM integration
OpenAI, Anthropic, Gemini, open-weight models on Bedrock or self-hosted. Streamed responses, structured outputs, function calling.
Talk to usRAG & vector search
Retrieval pipelines on pgvector, Pinecone, or Weaviate. Chunking, embeddings, reranking, evaluation. Tuned for the corpus, not the demo.
Talk to usML pipelines
Training, inference, and retraining loops with versioned data and reproducible runs. PyTorch and scikit-learn on SageMaker or self-managed.
Talk to usAI agents & automation
Multi-step agents on LangGraph, Claude Agent SDK, or OpenAI Agents SDK. MCP servers for tool access, Inngest for durable runs, budget caps and human-in-the-loop where it matters.
Talk to usComputer vision
Detection, classification, OCR on document and image workloads. Including the RaceProOnline face-recognition stack we run in production.
Talk to usEvaluation & monitoring
Eval suites, drift detection, latency and cost dashboards. So model quality is a measurable thing, not a vibe.
Talk to us