Engineering trusted by teams behind
01 — The promiseUnder every AI promise is a system that has to work.
02 — The realityMost are never built — 95% of pilots die in slides.
03 — The startWe start where the value lives: one workflow, priced in dollars.
04 — The buildAgents, evals, pipelines — built in your stack, shipped in week one.
05 — The proof300M users. 5B impressions. 1T parameters. Shipped — and scored every day.
An event-sourced multi-agent runtime across cloud, desktop, and mobile — agents that schedule work, execute it, verify it, and report back. Every action logged, every outcome auditable. Not a notebook demo.
Automated auditioning of new models, regression gates wired into CI, and swap decisions grounded in evals — for AI features serving hundreds of millions of users. Nothing ships on a hunch.
Agent copilots for operations teams, retention and ranking models on warehouse-scale data for a creator-commerce platform — every model tied to a revenue line someone watches.
Image, video, and audio generation on H100 fleets — model routing, cost engineering, and brand-safety gates built in from day one, with cost-per-asset on a dashboard.
How we work
Before we write code, we write the number: which workflow, what it costs you today, what it’s worth automated. If the math doesn’t clear, we tell you — before the invoice, not after.
Six-figure discovery phases, junior leverage, a deck at the end.
Value scoped in dollars up front. Systems shipped in your infra. Support that stays.
Hands on keyboards, but nobody owning the architecture or the outcome.
Engagements
Every engagement starts with the value math — in writing.A senior AI team inside your org — measured on your P&L.
Scope and price fixed before you commit
Two weeks to a costed roadmap.
Scope and price fixed before you commit
The Atlas difference
Questions
Fixed, and scoped against the value math. We put the workflow’s current cost and its automated value in writing first — you know what you stand to make before you commit a dollar. No hourly meters, no surprise invoices.
Senior researchers and engineers who have shipped ML at frontier labs, global consumer products, and healthcare platforms. No leverage pyramid; nobody junior on your bill.
LLM agents and orchestration, evaluation harnesses and model ops, classical ML — ranking, retention, forecasting — and generative media pipelines. In production, on your infrastructure, with dashboards you can open any day.
We stay. Models drift, data changes, and vendors ship new releases — the eval gates keep scoring them, and we keep tuning. Support is part of the engagement, not an upsell.
Your infra, your repos, your models where possible. NDAs and clean IP assignment are standard, with contracts reviewed by counsel.
Yes — US and Greater China delivery with overlapping hours, which is how we cover around-the-clock build cycles.
