What is Forward Deployed Engineering?

    Forward Deployed Engineering (FDE) means engineers embed directly with your team and customers to ensure AI solutions work in production. Unlike traditional consulting, FDE teams own outcomes—not tasks—and specialize in the 'last mile' of enterprise AI: air-gapped deployment, legacy integration, and compliance.

    Forward Deployed Engineering

    We close the “last mile” of enterprise AI.

    Growth-stage software vendors hire us to land enterprise deals. We embed with your team, integrate with legacy systems, and deploy to environments your cloud can't reach.

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    At a glance
    6 months → 6 weeks
    Enterprise deployment cycle reduction for a Series C FinTech firm
    40% lower COGS
    Inference cost reduction through model optimization for a healthcare AI startup
    Zero-downtime
    Air-gapped migration for a defense contractor's ML pipeline
    00What we do

    Four areas of expertise

    Plan with the frontier. Execute inside your boundary — the stack for AI applications on infrastructure you control.

    Enfuse extends existing technology-services organizations with specialized sovereign and physical AI capabilities. See where Enfuse fits in the sovereign AI ecosystem.

    01

    Physical AI & Perception

    Computer vision, LiDAR, sensor fusion, and Jetson edge AI for machines that see and act.

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    02

    Sovereign AI Platforms

    On-prem and air-gapped LLM runtime, governance, RAG, and the App Factory for repeatable apps.

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    03

    AI Infrastructure & GPU Systems

    NVIDIA architecture, hybrid cloud on GKE, Azure and Azure Local, and GPU-as-a-Service.

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    04

    Forward Deployed Engineering

    Embedded engineers delivering hybrid sovereign AI from pilot to production.

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    01Sovereign AI Platforms

    Security & compliance for AI

    Enable enterprise sales in regulated sectors that demand complete data sovereignty.

    Sovereign Infrastructure

    Private AI for finance, healthcare, and defense on your infrastructure.

    Red Teaming & Guardrails

    NeMo Guardrails and red teaming before enterprise clients see it.

    Compliance Automation

    Automated audit trails, policy enforcement, and reporting.

    02Physical AI & Perception

    Perception infrastructure for machines and environments

    The layer that lets robots, vehicles, and sites see, understand, locate, predict, and act.

    Explore Physical AI engineering

    NVIDIA Jetson & Edge AI

    Jetson Orin and Thor bring-up, TensorRT builds, and fleet updates.

    Computer Vision

    Detection, tracking, and re-ID trained on your imagery, running in real time.

    LiDAR & Sensor Fusion

    Camera, LiDAR, radar, and IMU fusion with field-proof calibration.

    Robotics & Autonomy

    ROS 2 and Isaac ROS perception, SLAM, and digital twins.

    03AI Infrastructure & GPU Systems

    Turn bare metal into an AI cloud

    We help colocation and GPU hosting providers productize capacity, from rack design to billing.

    Talk GPUaaS strategy

    AI-Ready Rack Design

    NVIDIA-certified stacks on Dell, Lenovo, and Supermicro, sized for network, storage, and cooling.

    Multi-Tenant Orchestration

    Kubernetes tenant isolation, quotas, and GPU scheduling with Run:ai or Volcano.

    Metering, Billing & Tenant UX

    GPU-hour metering, self-service portals, and API keys, so you can bill like a cloud.

    Sovereign & Regulated Workloads

    Air-gapped options and data residency for ITAR, FedRAMP, and HIPAA buyers.

    Go-to-Market Engineering

    Sales playbooks, RFP support, and joint SLAs that turn capacity into revenue.

    Managed AI Platform Layer

    Optional MLOps, model serving endpoints, RAG pipelines, and App Factory templates that differentiate your offering from commodity bare-metal GPU rentals.

    Whether you are adding H100/H200 clusters to existing colo footprints or launching a dedicated GPU cloud, we provide the systems engineering and go-to-market muscle to get to revenue faster.

    04The "NVIDIA Alpha"

    Performance optimization

    Software companies often write inefficient code. We make it cheaper and faster to run.

    Model Quantization & Pruning

    Reduce VRAM requirements to lower your COGS. We make inference cheaper without sacrificing accuracy.

    Compute Orchestration

    Move workloads between cloud and edge efficiently. Dynamic scheduling based on cost, latency, and compliance.

    Inference Pipeline Optimization

    Batch processing, caching strategies, and async patterns to maximize throughput per GPU dollar.

    05Forward Deployed Engineering

    Solutions for your bottlenecks

    We don't list technologies. We solve the specific problems that stop AI products from becoming enterprise solutions.

    01

    Forward Deployed Engineering

    We Embed, Not Just Deliver

    We don't hand over code and walk away. Our engineers embed with your team to ensure software actually works in production. This is the Palantir model applied to AI implementation.

    • On-site technical leadership & Field CTO services
    • Joint development with your engineering teams
    • Production troubleshooting & escalation support
    • Knowledge transfer & capability building
    02

    The 'Last Mile' of Enterprise AI

    From Working Demo to Full Integration

    VCs fund the product. We close the gap between a working SaaS product and a fully integrated enterprise solution—the difficult 'last mile' that determines whether deals close.

    • Custom adapters & legacy system connectors
    • Enterprise data silo integration
    • Deployment automation at scale
    • Proof of Concept (PoC) acceleration
    03

    Sovereign & On-Prem AI

    Data Residency & Air-Gapped LLMs

    Many VC-backed startups can't sell to GovTech or Defense because they can't deploy outside their cloud. We enable Private AI for sectors that demand complete data sovereignty.

    • Air-gapped LLM deployment & orchestration
    • Data residency compliance (ITAR, FedRAMP, GDPR)
    • Sovereign cloud & hybrid-cloud architectures
    • Zero-egress inference pipelines
    04

    Edge AI & Hardware Abstraction

    The Bridge to Low-Level Optimization

    Your NVIDIA investment should deliver maximum ROI. We bridge high-level software and low-level hardware optimization across the full GPU compute stack.

    • TensorRT & Triton Inference Server optimization
    • NVIDIA JETSON edge deployment
    • NVIDIA Fleet Command orchestration
    • Hardware abstraction layers for portability
    06Institutional knowledge

    Our technical mastery

    Proprietary playbooks for deployment. We're not just engineers—we're a repeatable process.

    Architecture

    • NVIDIA Blackwell
    • DGX Spark
    • Lenovo SR675 V3
    • NVIDIA H200

    Frameworks

    • LlamaIndex
    • LangChain
    • NeMo
    • NVIDIA NIM

    Inference

    • TensorRT
    • Triton Server
    • vLLM
    • Groq LPU

    Orchestration

    • Kubernetes
    • Fleet Command
    • Ray
    • Run:ai
    NVIDIA Preferred Partner

    NVIDIA-certified engineering: full-stack NVIDIA AI Enterprise expertise from edge to data center.

    For growth-stage software vendors

    You've built the product. Now you need to land enterprise deals.

    We provide the field engineering, integration expertise, and deployment capabilities that turn pilots into production contracts.

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    FAQ

    Common questions

    Forward Deployed Engineering · Delivery scope

    From a defined use case to an operable system.

    Our engineers work alongside customer teams to turn a defined use case into an integrated, operable system. Delivery includes the application and infrastructure work, evaluation, documentation, and operational preparation required for the agreed production environment.

    Assess and scope

    Architecture and readiness assessment, then a scoped implementation plan tied to the target environment.

    Build and integrate

    Application development, enterprise integration, deployment, and evaluation against agreed criteria.

    Hand off and support

    Operational handoff, runbooks, and agreed ongoing support so your team can own the system.

    AI-assisted application modernization

    We apply AI-assisted engineering to understand legacy systems, update integrations, improve tests, and modernize deployment workflows. Every change goes through engineering review and validation — AI-generated code is a starting point, not automatically production-ready.

    Forward Deployed Engineering · Hybrid cloud

    Sovereign where it matters. Cloud where it's permitted.

    Hybrid AI systems that use Google Cloud and Azure where policy allows, and keep sensitive data and agent actions inside your boundary.

    Core FDE capability

    Hybrid sovereign AI delivery

    Google Cloud logo
    Google Cloud
    GKE · Vertex AI
    Microsoft Azure logo
    Microsoft Azure
    AKS · Azure Local
    + Private GPU & edge

    An embedded team maps each workload to its boundary, builds the platform and controls, and hands over runbooks.

    Google Kubernetes Engine (GKE)

    GPU node pools, private clusters, and fleet management.

    Azure, AKS, and Azure Local

    AKS in region, Azure Local on-site, one policy set via Arc.

    Private infrastructure

    Owned GPUs, colocation, and air-gapped enclaves.

    Kubernetes as the common layer

    One GitOps deployment model across cloud, on-prem, and edge.

    vLLM and open models

    Open-weight models served where the data is allowed to live.

    Enterprise networking and security

    Private endpoints, segmentation, federated identity, and audit.

    Hybrid inference routing

    Policy routing by data class, with no cloud fallback for sensitive work.

    Hybrid AI infrastructureAgentic AI engineering
    Next step

    Ready to close enterprise deals?

    Let's discuss how Forward Deployed Engineering can accelerate your go-to-market.

    Talk to an architect