Do you only use closed-model APIs?
No — we run open-weights models (gpt-oss, Llama, Mistral, Qwen) on customer infrastructure when data sovereignty, cost, or latency demand it.
Service
AI
We design and operate AI systems that work in production: retrieval-augmented generation, custom agents, evaluation harnesses, and the MLOps to keep them safe, fast, and cheap.
Outcomes
- ▸Time-to-pilot in 4–6 weeks with measurable evaluation harnesses
- ▸Cost per call optimized via caching, routing, and small-model distillation
- ▸Guardrails: PII redaction, prompt-injection defense, audit logs
Capabilities
What we do
●LLM applications: RAG, multi-agent workflows, structured output
●Open-source model hosting: gpt-oss, Llama, Mistral on Ollama / vLLM / Triton
●Vector stores: pgvector, Qdrant, Weaviate
●Evaluation harnesses, red-teaming, prompt-injection defense
●MLOps: feature stores, training pipelines, model registry, drift detection
Tools and clouds
We meet you where you are
Multi-cloud and on-prem. Same standards, same GitOps, same rigor.
gpt-ossOllamavLLMLangChainLlamaIndexQdrantTritonAWSAzureGCPOracle CloudOn-premOpenShiftVMware
Related work
Shipping a production RAG copilot for a telco's frontline support
Designed and operated a RAG assistant powered by an open-weights model on the customer's own infrastructure, with red-teaming and evaluation harnesses built in.
FAQ
Common questions
How do you measure whether an AI feature is good enough to ship?
Every project gets a written evaluation harness up front: golden test cases, automatic graders, red-team prompts. Nothing ships until the harness is green.
Let's scope your ai engagement.
A senior engineer responds within one business day.