Forward Deployment Engineering

Forward Deployment Engineering | Stonetusker Systems
Forward Deployment Engineering · Now Accepting Engagements

Your AI Platform Is Live.
Production Is Still Broken.

Most engineering teams nail the deployment. What they can't recover from is the gap between "works in staging" and a system the business can actually depend on at 2am. Stonetusker engineers embed in your team to close that gap — permanently.

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AI model deployed — but no drift monitoring, no alerting, no retraining pipeline
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LLM integrations fragile under load — every release is a gamble
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No observability — you don't know when the AI degrades or why
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Team can't extend what the consultant built — you're still dependent
✓ NDA before any architecture discussion · ✓ 2–3 week pilot before full commitment · ✓ Milestone billing — pay for results
Free · 30 Minutes · No Pitch Deck
Book Your Discovery Call

We arrive having already reviewed your public stack. You leave with a clear picture of where FDE has the most impact — and what a 90-day engagement would look like for your specific environment.

✓
Stack-specific diagnosis — not a generic AI pitch. We review your architecture before we speak.
✓
Priority workstream — the one thing to fix first that unlocks the most production stability.
✓
Pilot scope outline — what a 2–3 week Discovery Pilot would cover and what it would deliver.
✓
Indicative investment range — ballpark confirmed after the pilot, not estimated upfront.
🛡️
Pilot-first, always. Every engagement starts with a 2–3 week Discovery Pilot before you commit to the full plan. If the pilot doesn't deliver a working result on your real environment — you don't pay for the full engagement.
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✓ NDA in writing
✓ No retainers
✓ Pilot before commitment
90
Days to production-ready AI systems — structured & time-boxed
26+
Years of embedded engineering delivery across Nokia, Stryker & VeriSign
8×
Build time improvement — typical result from embedded FDE engagements
0
Retainers or lock-in dependency left behind at engagement close
The Real Problem

Deployment Is the Easy Part. Production Is Where Teams Stall.

You've chosen the AI platform, stood up the cloud infrastructure, and wired the APIs. But somewhere between "it works" and "the business depends on it" — things quietly break. Latency. Drift. Integration fragility. Monitoring gaps. Missing runbooks.

This isn't a tooling problem. It's a structural gap between how modern platforms are architected for demos and how they need to behave under real production conditions. That's exactly what Forward Deployment Engineering is designed to close.

  • !
    AI systems deployed but not operationalized Models run, but there's no drift monitoring, alerting, or retraining pipeline in place.
  • !
    Integration complexity slowing every release Connecting LLMs, RAG pipelines, and enterprise systems creates fragile dependencies that break under load.
  • !
    No operational visibility into AI behaviour Teams lack observability into what the AI is doing, when it degrades, and why.
  • !
    Engineers left dependent on the implementer After the consultant leaves, the team can't confidently operate or extend what was built.
What Is FDE?

Forward Deployment Engineering — Defined

Forward Deployment Engineering combines platform engineering, customer-facing technical delivery, and operational readiness into a single embedded engagement. Our engineers work directly inside your team — not alongside it — to deploy, integrate, and operationalize modern AI and automation platforms.

Unlike traditional consulting that ends at implementation, FDE stays aligned with the outcomes: adoption, integration stability, and production success. You get engineers who've built production systems at Nokia, Stryker, and VeriSign — not slide decks.

AI Platform Deployment DevOps Modernization Enterprise Automation Production Operationalization LLM Integration RAG Pipelines AI Observability Embedded Engineering
Traditional Consulting
Ends at implementation
Playbook-driven
Off-site recommendations
No handover accountability
Team left dependent
Forward Deployment Engineering
Aligned to production outcomes
Built around your stack
Embedded in your team
Runbooks & docs delivered
Team owns it at close
Core Services

Four Engineering Domains. One Embedded Team.

Each service is scoped around your specific environment — not a generic template applied across clients.

01 — AI PLATFORM DEPLOYMENT

From AI Infrastructure to Production-Grade Systems

We deploy and operationalize modern AI systems across cloud, edge, and hybrid environments — the full operational stack your team can monitor, extend, and own.

  • AI infrastructure deployment & GPU readiness
  • LLM integration workflow engineering
  • AI agent orchestration & RAG pipeline integration
  • AI observability, monitoring & drift detection
  • Enterprise AI security alignment
  • Edge AI deployment support
02 — DEVOPS & CI/CD MODERNIZATION

Delivery Platforms That Scale With Your Team

We design and implement scalable delivery platforms that improve release velocity and reliability — built around your specific team structure, not a generic pipeline template.

  • CI/CD pipeline design & implementation
  • Infrastructure as Code (IaC) engineering
  • Kubernetes platform engineering & GitOps
  • Cloud-native deployment automation
  • Secure software supply chain implementation
  • Developer platform enablement
03 — ENTERPRISE AUTOMATION ENGINEERING

Operational Workflows Automated End-to-End

We help organizations automate operational workflows and integrate modern systems across teams and platforms — from API integration to AI-assisted operations.

  • Workflow automation & API integration engineering
  • Event-driven architecture implementation
  • Enterprise system orchestration
  • Internal developer workflow automation
  • Business process automation
  • AI-assisted operational workflows
04 — PRODUCTION OPERATIONALIZATION

Deployment Isn't Done Until the Team Owns It

Production success requires operational alignment, observability, and a team that can maintain what was built without external dependency.

  • Production onboarding & environment stabilization
  • Deployment readiness assessments
  • Observability, monitoring & alerting implementation
  • Incident reduction strategies
  • Performance optimization & reliability engineering
  • Runbooks, documentation & live handover
How We Work

Embedded Engineering Partnership —
Not Remote Consulting

⚙️

Embedded Technical Partners

Our engineers work within your team — in your Slack, your standups, your PRs. Not as advisors sending weekly reports, but as delivery team members accountable to your production milestones.

🔍

Assessed Before Proposed

We study your architecture, team structure, and deployment blockers before writing a single line of the engagement plan. Every engagement is specific to your environment — never adapted from a playbook.

📋

Full Visibility Throughout

Pipeline progress, integration status, deployment metrics, and milestone tracking shared with your leadership in real time. No black-box delivery. You always know exactly where things stand.

🎯

Milestone-Based Billing

You pay for results, not hours logged. If a milestone isn't achieved, you don't pay for that phase. Our incentives align entirely with your outcomes — as it should be when you trust someone with production systems.

📘

Handover Built Into the Plan

Runbooks, documentation, and a live operations period where your team runs everything with us alongside — before we step back. At engagement close, your team owns it fully. No ongoing dependency required.

🔐

NDA Signed Before We Begin

Every engagement begins with a mutual NDA signed before any technical discussion. Your architecture, codebase, AI model configurations, and internal processes remain completely confidential. In writing.

Typical Outcomes

What Changes After an FDE Engagement

Measured results across AI deployment, automation integration, and production operationalization.

⚡

Reduced Deployment Timelines

AI platforms and automation workflows moved from weeks-long manual processes to repeatable, automated pipelines.

🛡️

Improved Production Stability

Fewer incidents, faster resolution, and observability built into the system — not bolted on after the fact.

🤖

Accelerated AI Adoption

AI systems your engineering team can operate, monitor, and extend — not black-box deployments only the consultant understands.

🔄

Modernized Delivery Pipelines

CI/CD infrastructure rebuilt around your actual team structure, release cadence, and compliance requirements.

📈

Developer Productivity Reclaimed

20–30% of productive engineering capacity returned to product work — freed from operational overhead and manual release processes.

🔗

Reduced Integration Complexity

Enterprise system integrations built with event-driven architecture and proper failure handling — not duct-taped APIs.

📊

Operational Visibility

Full observability into AI behaviour, system health, and delivery metrics — surfaced to the right stakeholders automatically.

🏗️

Scalable Infrastructure

Infrastructure designed to scale with team growth and product complexity — not just today's load.

🎓

Team Owns the Outcome

Runbooks, documentation, and a live handover period ensure your team can operate and extend what was built — permanently.

Industries

Built for Industries Where Production Actually Matters

FDE is specific to your sector's compliance requirements, deployment patterns, and operational constraints.

🏢 Enterprise Software
🏭 Manufacturing & Industrial
🏥 Healthcare Technology
🏦 Financial Services
☁️ SaaS Platforms
🤖 AI & Data Platforms
🛒 Retail & Logistics
🔧 Embedded & Defense
🚗 Automotive & Mobility
📡 Telco & Networking
🔒 Cybersecurity
⚡ Energy & Utilities

Don't see your sector? Talk to us — we'll map the right FDE approach to your specific environment.

Discovery Pilot — Pilot-First

See the Work Before You Commit to It

Every FDE engagement starts with a focused 2–3 week Discovery Pilot — scoped, time-boxed, and structured to produce something tangible before you've committed to the full engagement.

01

Architecture & Environment Assessment

We review your AI infrastructure, integration architecture, and current deployment process. NDA signed before this starts. We scope the pilot to the highest-value use case before any implementation begins.

02

Working Deliverable on Your Stack

A functioning deployment pipeline, integration workflow, or operational system — delivered within the pilot window, integrated into your actual environment. Not a sandbox demo.

03

Documentation & Operating Guide

Configuration decisions, integration patterns, and how to extend the system — documented during the pilot so your team can operate it from day one. Not delivered as an afterthought.

04

Full Scope Proposal

At pilot close: a specific engagement proposal covering remaining workstreams — ordered by expected impact and implementation effort, with a defined timeline and milestone structure.

Pilot Guarantee

The Discovery Pilot produces a real, operating deliverable — on your actual infrastructure, integrated with your actual stack. Not a proof-of-concept in a sandbox environment.

If the pilot doesn't deliver a working result on your real environment, you don't pay for the full engagement. That's in the agreement before the pilot starts.

In the contract. Not just a promise.
✓
Pilot scoped and time-boxed before you commit to anything
✓
NDA signed before any technical or architecture discussion
✓
Milestone-based billing — you don't pay for phases not achieved
✓
No retainers. No long-term contracts. No ongoing dependency.
✓
Full team handover with runbooks before engagement closes
✓
Optional post-engagement support available — on your terms
Typical engagement investment
$20K – $60K USD
Confirmed after Discovery Pilot · Not estimated upfront · Scoped to your specific environment
Common Questions

Questions We Hear Before Every First Call

How is Forward Deployment Engineering different from your existing DevOps consulting? +
Our DevOps consulting focuses specifically on CI/CD pipeline modernization, build automation, and delivery engineering — typically for teams scaling from 50 to 200+ engineers where the existing delivery architecture has become a bottleneck. Forward Deployment Engineering is broader: it's the right engagement when the primary challenge is deploying and operationalizing AI platforms, integrating automation workflows at the enterprise level, or bridging the gap between a technology deployment and a production-grade system your business can depend on. Many clients combine both. We scope the right approach during the discovery call.
We've already deployed our AI platform. Is it too late for FDE to help? +
No — and this is actually the most common starting point. Most teams that reach out have already deployed the platform; what they haven't done is operationalize it. That means there's no proper observability, no drift monitoring, no retraining pipeline, no incident response runbook, and the system is fundamentally still in a "it works when we watch it" state. FDE is specifically designed to take deployed systems and make them production-grade — adding the operational layer that turns a working deployment into a system your business can depend on without engineering oversight.
Our stack is unusual — hybrid cloud with on-prem AI inference and edge components. Can you handle that? +
Yes — and hybrid and edge deployments are among our most common FDE engagement types. Our founder spent years at Nokia and IPInfusion working on embedded systems, network OS platforms, and infrastructure that had to work reliably in constrained, distributed environments. We have specific experience across Embedded Linux, Yocto, bare-metal deployments, cloud-native platforms, and hybrid architectures. The Discovery Pilot is structured exactly to assess your environment and validate the approach before proposing the full engagement.
What happens to our team's engineers during the engagement? Are they replaced or sidelined? +
The opposite. FDE is an embedded model — our engineers work alongside your team, not over it. Your engineers are involved in every design decision, every architecture review, and every implementation. The handover isn't an event at the end of the engagement; it's a continuous process throughout it. By day 90, your team should be running everything with us in an advisory capacity — not still waiting to be handed the keys.
We're in a regulated industry with strict data governance requirements. How does FDE handle that? +
Compliance requirements are scoped into the engagement architecture from the start — not added as an afterthought. We've delivered FDE-style engagements for healthcare AI products under FDA requirements, financial services platforms under SOC 2 and financial services AI governance, and defense-adjacent embedded systems with strict audit requirements. This means policy-as-code wired into CI/CD, automated audit trail generation, and compliance gates built into every deployment. If your regulatory environment is complex, the Discovery Pilot specifically maps those requirements to the technical architecture before any implementation begins.
What does the engagement cost, and how is it structured? +
Typical FDE engagements run $20K–$60K USD over 90 days, confirmed after the Discovery Pilot — not estimated upfront. Billing is milestone-based: you pay for outcomes achieved, not hours logged. If a milestone isn't reached, you don't pay for that phase. The Discovery Pilot is a paid, separate engagement — scoped specifically for your environment — and produces a concrete deliverable and a full proposal for the remaining scope at close. There are no retainers, no long-term lock-in, and no ongoing dependency after the engagement closes.
Start the Conversation

Your AI Platform Deserves to Run in Production — Not Just in Staging.

30 minutes. No pitch deck. We arrive having already studied your public stack and we'll tell you exactly where Forward Deployment Engineering would have the most impact first.

Book a Free Discovery Call → Start the 2–3 Week Pilot
No retainers · No lock-in · NDA signed before we discuss your architecture

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