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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. Built by forward-deployed engineers who own the whole production substrate: the models, the cloud, the security, and the bill.

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01/ 05
What we doAI Enablement & FDEEngineers embedded in your environment
The hard part isn't the model.

Most enterprise AI never makes it out of the pilot.

Roughly 95% of enterprise GenAI pilots deliver no measurable business impact, and it's almost never the model's fault. It's the integration: the data plumbing, the security review, the cost of running it, the on-call reality at 3am. That gap, between a working demo and a system your business can actually depend on, is the only thing we do.

  • 95% of enterprise AI pilots stall before production (MIT, 2025)
  • The bottleneck moved from model access to deployment capability
  • We close it by embedding engineers who ship, not slides

Built on the tools
trusted by teams shipping real AI.

What we do

AI that survives contact with production.

We don't hand you a strategy deck or a prototype. Forward-deployed engineers embed with your team and build AI systems that ship, scale, pass audit, and don't blow the budget.

How forward-deployed engagements work

From pilot to production

Take AI from notebook to a system that ships and scales. Inference platforms, GPU scheduling, and MLOps pipelines that hold under real traffic.

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Safe by construction

Human-approved actions, least-privilege by default, and full auditability across every AI and cloud workflow. Built for regulated environments from day one.

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Embedded, not outsourced

Senior engineers who work inside your tooling, ship in your repos, and leave you self-sufficient. No ticket queue. No lock-in.

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Spend you can explain

Cost visibility and rightsizing across cloud, GPU, and LLM spend, so AI scales without a runaway bill.

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Built for scale. Engineered for impact.
How we're different

Forward-deployed engineers. Not a ticket queue.

The best AI teams in the world (OpenAI, Anthropic, Palantir) don't win enterprise deployments by handing over an API. They put senior engineers inside the customer's environment to learn the data, the constraints, and the politics, then ship production code against the real mess. That's the model we're built on. You get engineers who own the outcome end-to-end, from the model call to the cluster it runs on.

Trusted Partners
Partner
And More Innovative Partners Joining Us On Our Journey

Not a model vendor. Not a slide-deck consultancy.

Model vendor / generic AI consultancyRescape
EngagementAPI access or a deckEmbedded engineering
ApproachPrototype, then handoffShip to production
SecurityYour problemSafe by construction
SubstrateNot their concernWe own cloud, K8s & cost
SupportSupport team / communityDedicated senior engineers
OutcomeA pilotBusiness impact

We partner with best-in-class innovators to deliver future-ready, secure and scalable solutions.

Skip the delivery path

Scroll to move through the eight steps, or jump straight to any step on the left.

Featured success story

Real outcomes.
Not rounded up.

We publish one number here because one engagement has signed off on it. More land as they finish.

40%
Faster AI model deploymentVazudev Corp.
Challenge

Machine learning models were stuck in notebooks, blocked by infrastructure complexity and disjointed deployment pipelines.

Approach

Forward-deployed engineers built a secure, production-grade AI platform with integrated ML pipelines on Kubernetes.

Outcome

Seamless transition from prototype to production, improved reliability, and robust GPU scheduling for inference.

Rescape helped us integrate production-grade AI capabilities into our infrastructure without disrupting day-to-day operations.
Jagatveer Singh
Jagatveer SinghCEOVazudev Corp.

Results vary based on industry, workload and implementation.

How we work

Four rules we don't break.

  1. 1

    Read-only and least-privilege by default.

    We start by listening and understanding your systems and data, never by making changes.

  2. 2

    We work inside your tooling.

    GitOps, IaC, and your workflows. We plug in, we don't work around.

  3. 3

    Nothing ships to production without your sign-off.

    Every consequential action, human or AI, is yours to approve.

  4. 4

    We leave you self-sufficient.

    Runbooks, documentation, and enablement so your team owns what we build. No lock-in.

FAQ

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 AI into production.

Talk to an engineer and see how we build, secure, and operate AI systems your business can depend on.

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