Forward Deployed Engineering Services

Get private AI intoproduction.

Gridlight FDE services close the gap between a private AI use case and a working deployment on infrastructure you control. Certified engineers deploy the control plane, integrate the first workflow, and measure success against a defined baseline.

Every prompt and byte stays inside your perimeter. Sovereignty is the architecture.

Why services

Platform value compounds when the first use case is live.

Regulated teams usually know which AI workflows matter, but the highest-value ones sit behind sensitive data, strict network boundaries, and existing enterprise systems. Gridlight's FDE model pairs the private inference platform with embedded engineering so the first deployment is scoped, installed, integrated, and measured as one motion — anchored to a success metric and a measured 30-day report, not an open-ended implementation project.

Engagement paths

Pick an FDE path.

Four ways to engage, from a fixed deployment sprint to an ongoing embedded engineer — each tied to the environment you control.

Tier 1 · Days 0–30

Deployment Sprint

Scope the first use case, install and connect the Gridlight control plane, and complete initial integration with auth, data sources, and monitoring.

Tier 2 · 30–90+ days

Embedded Integration Engagement

Dedicated Forward Deployed Engineers build your workflows, agentic pipelines, model routing policy, and compliance or audit wiring.

Tier 3 · Monthly retainer

Ongoing Advisory / Fractional FDE

A part-time embedded engineer supports continued expansion: new use case scoping, model swaps, routing updates, and private AI architecture guidance.

Tier 4 · 60–90 days

Small Language Model Development

Fine-tune and package a small, task-specific language model on your own data, optimized to run on your Gridlight-deployed hardware.

Delivery model

Deploy, prove, and expand inside your environment.

Every engagement follows the same arc: stand up private inference, prove it against a baseline, then extend the same control plane to more workflows.

Deploy

Stand up the private inference foundation.

Gridlight works inside your environment to connect hardware, identity, data sources, observability, and the first target workflow.

Prove

Measure against the baseline.

The success metric is defined up front, measured against baseline, and reported after the first 30 days so the engagement has a concrete outcome.

Expand

Turn the first use case into a shared platform.

Once the first workflow is live, the same control plane can support additional workflows, model routing policies, and team-specific governance.

Scope boundary

Clear boundaries keep the engagement tied to private AI outcomes.

What a Gridlight FDE engagement covers — and what it deliberately leaves out — so scope stays focused on getting private AI into production.

In scope

  • supported: Architecture design and deployment of the Gridlight control plane.
  • supported: Integration with your named systems — authentication, data sources, monitoring, and observability.
  • supported: Workflow and agentic pipeline engineering for the use cases named in the engagement.
  • supported: Scoped small language model fine-tuning and packaging when Tier 4 is selected.

Out of scope

  • not supported: General IT staff augmentation unrelated to Gridlight architecture or integration work.
  • not supported: Open-ended frontier-model research or training unconnected to a defined SLM deliverable.
  • not supported: Scope, timeline, or fee changes without a written amendment.

What to expect

Each engagement is anchored to practical outputs.

Concrete deliverables, a clear set of inputs from your team, and guardrails that keep the work measurable from kickoff to handoff.

What Gridlight delivers

  • Gridlight control plane deployed and connected to hardware you own or designate.
  • First use case integrated with your authentication, data sources, and monitoring.
  • Baseline and 30-day success-metric report measured against the metric you define.
  • Documented deployment runbook for the installed environment.
  • For SLM engagements: a fine-tuned model evaluation report and packaged model on your hardware.

What you provide

  • A sponsor with authority to define the success metric and approve scope.
  • Timely access to relevant systems, data sources, stakeholders, and environment details.
  • Confirmed hardware and network prerequisites during kickoff.
  • For SLM work, representative training data and evaluation criteria defined up front.

How it stays on track

  • A single success metric anchors the engagement to a measurable outcome.
  • Fixed scope, timeline, and fee are agreed before work begins.
  • Changes are handled only through a written amendment.
  • All work is performed under Gridlight’s services agreement (MSA).

Next step

Map the first use case to a measurable engagement.

Bring the target workflow, the systems involved, and the success metric that matters most. Gridlight will scope the FDE path around the environment you control.