Forward-deployed engineers embed with your team and ship your product: AI-native, at AI speed, with Cerebe's approval on every change. We deliver working software on the same platform we make, so speed comes with confidence, not instead of it.
How we work
Our engineers join your team and own outcomes: working software in your stack, to your goals. You get people who build AI-native products for a living, moving at the speed the tools now allow, with the judgment to know what to ship.
Blueprint
Proven patterns and scaffolding for the parts every AI-native application needs, so an engagement begins with a running, production-shaped product instead of an empty repository.
A production-shaped app from the first commit: backend, frontend, and the wiring between them, so a build starts as a running product, not an empty repository.
SSO, roles, and multi-tenant boundaries as a pattern we start from, so the B2B-ready auth surface is in place before feature work begins.
Tracing, logs, and metrics wired in from the start and correlated, so behavior in production is legible from day one rather than bolted on later.
Real Kubernetes locally and the same chart to any cloud, with GitOps delivery: the path from a developer's machine to production is one we've already paved.
Accessible, keyboard-navigable UI primitives, including the conversation surfaces AI-native products need, so the interface starts polished.
A migration-gated database layer with a clean repository pattern, so data access is structured and safe to evolve as the product grows.
AI Infra
Memory, model routing, prompt management, caching, and retrieval: the substrate our engineers build your product on, so the work goes into your features instead of the plumbing beneath them.
Working, episodic, and long-term memory over a hybrid vector and graph store, so an application remembers the user, the domain, and what has already been discussed across sessions.
Route by capability, not by model name. Move between providers without touching application code, with per-tenant overrides, so a product stays resilient as the model landscape shifts.
Prompts managed as versioned artifacts with evaluation, so a change to how the system talks is deliberate, reviewable, and easy to roll back.
Response and retrieval caching that keeps latency and cost down as usage grows: the difference between a demo and something that runs in production.
Grounding an application in your own content, so answers are anchored to what's true for your domain rather than the model's general prior.
Memory, routing, prompts, and evaluation behind a single surface, cloud-managed or deployed into your own environment when data residency requires it.
Services
Tell us what you're building. We'll bring the team, the accelerators, and the quality engine, and deliver AI-native software with speed and confidence.