A model that works in a demo still needs twelve layers around it to run in production: compute, serving, retrieval, a gateway, evaluation, observability, guardrails, cost tracking, and more. We build the ones you're missing.
Kubernetes and NVIDIA GPUs for compute, vLLM and Docker for serving, PostgreSQL with pgvector, Qdrant, or Neo4j for retrieval, Model Context Protocol for agent access, Python for pipelines, and Prometheus and Grafana for observability.
We work hands-on with: PostgreSQL, Model Context Protocol, Neo4j, Python, Hugging Face, Anthropic, Qdrant, LangChain, Kubernetes, NVIDIA, Docker, Prometheus, Grafana.
Each layer is a separate failure point. Most teams have three or four of them and find out about the rest during an incident.
The hardware and cluster layer every model runs on.
The runtime a model answers requests from, and the path a new version takes to reach it.
The data a model reads from at request time, kept clean and current.
One endpoint between your applications and every model provider.
The pipeline that takes a model from experiment to a versioned production release.
The practices that make a non-deterministic system testable before it ships.
Visibility into what the model did, how long it took, and whether the answer was right.
Checks on what goes into a model and what comes out of it.
Spend attributed to the feature, team, or agent that caused it.
The records that show which model ran, on what data, and who approved it.
Operations for agents that call tools and change data on their own.
Using machine learning to run the infrastructure itself.
We map which of the 12 layers you already run, which are missing, and which are running without an owner.
Regulated and high-volume industries get value from AI only when the retrieval, serving, and access-control layers are built for them. These are the builds we see most in 2026.
KYC checks and financial reporting need answers pulled from accounting systems and transaction logs. We build retrieval with source citations, document-level access control, and an audit log of every answer.
Clinical knowledge assistants must cite the protocol or paper behind each answer. We build PHI-safe retrieval and an evaluation set that measures citation accuracy before anything reaches a clinician.
Research over case law, precedent, and contract templates needs matter-level separation. We build per-matter indexes with jurisdiction filters so one client's documents never surface in another's results.
Product search that understands intent beats keyword matching. We add semantic search and recommendations on pgvector or your existing database, without standing up a separate system.
In-product AI features add a model bill and a new failure mode. We put an LLM gateway in front of providers, track spend per tenant, and add observability for prompts, retrieval, and failures.
Technicians lose time searching manuals and equipment logs. We build retrieval over both, serve the model on GPUs you control, and connect it to the maintenance history it needs to answer well.
Tell us what you run today. We'll map it against the 12 layers and show which gaps can take production down first.
