We engineer custom AI infrastructures for B2B, B2C, and D2C scale. No wrappers. No templates. Just rigorous, production-ready compute.
We engineer intelligence across the full stack of business, consumer, and direct commerce. Hover a tile to lift it from the deck.
Distributed inference, retrieval, and evaluation harnesses wired into your existing data plane. Built for throughput, audited for drift.
On-device and cloud models behind polished, low-latency interfaces. We ship the model, the UI, and the feedback loop that improves both.
Personalization, search, and agentic checkout tuned to your catalog. Conversion lift measured against your real funnel, not a benchmark.
Every system we ship runs on the same five layers. We own the seams between them so nothing leaks in production.
A live read from a shipped inference gateway, and the numbers behind it. Everything below is extracted from a production deployment.
1// production inference gateway, eu-west-2 2const gateway = await deploy({ 3 model: "axiom-ft-70b-q4", 4 runtime: "vllm", 5 replicas: 4, 6 region: "eu-west-2", 7 guardrails: ["pii", "secret", "policy"], 8 evalGate: { p95: 0.92, drift: 0.03 } 9});
End-to-end inference at the gateway, measured over a rolling 24h window.
illustrative figureThree availability zones, rolling deployments, automatic rollback on eval regression.
illustrative figurePass/fail threshold every commit must clear before traffic. Below it, the build refuses to ship.
illustrative figureFour phases, each with a concrete deliverable. No open-ended retainers, no discovery theatre.
Two weeks. We pressure-test the problem, pick the model, and write the deployment plan.
Build the data plane, fine-tune the model, wire orchestration. You see code in week one.
Load, adversarial, and eval gates. We will not ship something we have not broken first.
Deploy to your infra, hand over the runbook, and stay on call through the first production cycle.
A few systems we have shipped. Full case studies on the work page, including what went wrong and how we fixed it.
Real-time demand forecasting across a 40-warehouse network. Inference at the edge, decisions in under a second.
On-device spending assistant for a neobank. Personalized, private, and fast enough to feel instant.
Predictive commerce engine for a heritage retailer. Search, ranking, and agentic checkout on one stack.
Tell us the problem. We come back inside two business days with a scope, a model shortlist, and a deployment plan. No discovery decks, no pitch theatre.