AI-NATIVE. FORWARD-DEPLOYED. PRODUCTION-PROVEN.
We get enterprise AI into production, and keep it there.
AI systems that do real work, with real safeguards. Our engineers own the whole substrate: the models, the cloud, the security and the bill.
Four things we own.
Roughly 95 percent of enterprise AI pilots stall before production (MIT, 2025). The blocker is rarely the model.
From pilot to production
Inference platforms, GPU scheduling and ML pipelines on Kubernetes, serving real traffic.
Learn moreSafe by construction
Policy-as-code, least privilege, and human approval on every consequential action.
Learn moreObservability that matters
SLOs on inference latency, output quality and token spend, not just uptime.
Learn moreSpend you can explain
Cloud, GPU and token cost attributed to a team, a workload and an outcome.
Learn moreForward-deployed engineers. Not a ticket queue.
The best AI teams do not win enterprise deployments by handing over an API. They put senior engineers inside the customer's environment and ship against the real system.
You get engineers who own the outcome from the model call to the cluster it runs on.
Not a model vendor. Not a slide-deck consultancy.
We partner with select innovators to deliver production-ready, secure and scalable systems.
Stalled pilot
Works on one dataset, one laptop, one person.
The model is not the blocker. Integration, sign-off and running cost are.
- Works on one dataset only
- No path from notebook to service
- Integration and security sign-off unaddressed
- Cost per call unknown
- Nobody owns it on-call
Establish accounts, identity, networking and cost governance.
Scroll to move through the four stages, or jump straight to any stage on the left.
Real outcomes.
Not rounded up.
We publish one number here because one engagement has signed off on it. More land as they finish.
Machine learning models were stuck in notebooks, blocked by infrastructure complexity and disjointed deployment pipelines.
Forward-deployed engineers built a production AI platform with ML pipelines on Kubernetes.
Direct transition from prototype to production, improved reliability, and efficient GPU scheduling for inference.
Rescape helped us integrate production-grade AI capabilities into our infrastructure without disrupting day-to-day operations.
Results vary based on industry, workload and implementation.
Four rules we don't break.
- 1
Read-only and least-privilege by default.
We start by listening and understanding your systems and data, never by making changes.
- 2
We work inside your tooling.
GitOps, IaC, and your workflows. We plug in, we don't work around.
- 3
Nothing ships to production without your sign-off.
Every consequential action, human or AI, is yours to approve.
- 4
We leave you self-sufficient.
Runbooks, documentation, and enablement so your team owns what we build. No lock-in.
Questions we get asked
What is a forward-deployed engineer?
A forward-deployed engineer (FDE) is a senior engineer who embeds directly in your environment rather than working from a statement of work at arm's length. They learn your data, constraints and controls, then ship production code against the real system: the model calls, the integration, and the cluster underneath it.
How do you keep enterprise AI secure?
We start read-only and least-privilege, put policy-as-code guardrails on every change, human or AI-generated, and require human approval for consequential actions. Every step leaves an auditable trace, which is what SOC 2, PCI DSS, ISO 27001, GDPR and RBI reviews actually ask for.
How fast can you get a pilot into production?
It depends on what the pilot is blocked on: integration, security sign-off, or cost. We scope that in the first engagement phase and tell you plainly what stands between the demo and production, rather than quoting a timeline before we have seen the environment.
Let's get your systems into production.
Talk to an engineer about the AI or digital asset infrastructure you need to run.