We design, deploy, and run the infrastructure behind production ML and LLM systems — model training, vector retrieval, agent orchestration, observability, governance, and the unsexy plumbing that keeps inference cheap and uptime perfect.
Best-of-breed components, integrated into one accountable stack — so your ML team ships features instead of fighting infrastructure.
Foundation model fine-tuning, RAG pipelines, evals, guardrails, prompt versioning, and inference cost control.
Explore LLMOpsMulti-agent orchestration, tool-use, memory, evals, and observability for production agent systems.
Explore agentsKubernetes-native ML platforms across AWS, GCP, Azure, and bare-metal GPU clusters. From H100 fleets to spot autoscaling.
View serviceToken caching, KV cache reuse, distillation, quantization, GPU right-sizing — typical 40–70% inference cost reduction.
View serviceCanary deploys, model rollbacks, drift detection, multi-region failover. Four-nines uptime is the baseline.
View serviceLakehouse architecture, feature stores, vector DBs, streaming pipelines — clean data is the unlock for every model.
View serviceReproducible training, model registries, automated promotion, shadow traffic, and progressive rollouts via GitOps.
View serviceEU AI Act, NIST AI RMF, SOC 2, HIPAA. Model cards, audit logs, red-teaming — defensible AI from day one.
View serviceMost ML projects die in production. Our entire practice exists to close the gap — bringing battle-tested patterns from companies that have already shipped agentic systems, RAG search, and real-time inference at scale.
We don't sell vendor lock-in. We assemble open, modern stacks (MLflow, Kubeflow, Ray, vLLM, LangChain, Pinecone, Snowflake) and operate them with the discipline of a platform team — until your team is ready to take the wheel.
One-week audit: stack, data, models, cost, risk. You leave with a written architecture and ROI model.
We propose the smallest, sharpest architecture that solves the problem — and the path to get there.
We build alongside your team. Reference implementation in 4–8 weeks, in your accounts, with your IAM.
On-call coverage, drift monitoring, cost reviews. Or we hand off and document our way out.
A few engagements we can talk about — see all case studies for the full set.
Replaced GPT-4 with a distilled Llama 3.1 70B fine-tune on vLLM, added semantic cache + KV reuse, kept eval scores within 1.2 points of the original.
Private VPC deployment on AWS Bedrock + OpenSearch vector, full audit trail, model cards, and an LLM-as-judge eval harness running nightly against 1,400 gold answers.
30-minute discovery call. We'll review your stack, surface the two changes with the highest ROI, and tell you whether we're the right partner — or who is.