Forward-Deployed Engineering Solution
Build the system, ship at AI speed. GoGloby’s forward-deployed AI engineering model embeds AI Solutions Architects and Applied AI Engineers into your team to set up the Agentic SDLC, connect the AI Intelligence Layer, and improve delivery against your own baseline.
- Forward-deployed in under 4 weeks.
- Agentic SDLC setup and the AI Intelligence Layer benchmark.
- Measured against your own baseline.
- 120-day performance guarantee.
What Is Forward-Deployed Engineering?
Forward-deployed engineering, often shortened to FDE, is an embedded engineering model where senior engineers work inside your company, not from the outside. Unlike a consultant who recommends and leaves, or a staffing vendor who sends capacity for your backlog, an FDE owns the outcome: the system, the delivery, and the result.
Inside your team
They work through your pipeline, repos, and review process, on your real backlog. Not from a conference room.
System first
They set up your Agentic SDLC and the AI Intelligence Layer before shipping: one way of working, full visibility.
Delivery at AI speed
They ship production features from your backlog, with speed and quality measured together.
Proven, not promised
Every feature is priced in dollars and compared against your own baseline. You see the progress in your own numbers.
Why AI Teams Need Forward Deployed Engineering
You bought the tools. The team has the licenses. Delivery looks the same. Here’s what’s actually in the way.
Uneven AI Adoption
A few engineers ship at AI speed, the rest work like it’s 2022, so the tools are paid for but delivery never speeds up. Forward-deployed engineers install one way of working with AI instead of ten private workflows.
AI Fails on Established Codebases
AI shines on greenfield demos but stalls on established codebases: aging code, hidden business logic, weak tests, unclear ownership, long review paths, and outdated docs. The platform you actually run is where the tools help least.
No Proof of Real AI Adoption
AI changed what the old metrics can prove. Story points and velocity alone cannot show who ships with AI versus who just holds a license, what a feature costs, or whether AI is improving delivery.
The FDE Talent Gap
The missing skill goes beyond headcount: system design, Agentic SDLC, codebase work, delivery ownership, telemetry, and outcome accountability. That profile takes 6 months to hire and rarely sticks.
Forward Deployed Engineering vs Other Models
Four models address different parts of the same problem. GoGloby combines delivery and measurement in one contract.
Consulting Recommends
Consulting typically focuses on strategy and roadmaps. GoGloby continues through implementation, works inside your production environment, and measures the result.
Staffing Adds Capacity
Staffing adds engineering capacity for your backlog. GoGloby also installs a shared AI delivery system and measures the result against your baseline.
Dashboards Measure
Engineering dashboards show activity and surface problems. GoGloby also embeds the people needed to address those problems inside your workflow.
GoGloby Delivers and Measures
GoGloby forward-deploys AI Solutions Architects and Applied AI Engineers, connects the AI Intelligence Layer, sets up the Agentic SDLC, ships through your pipeline, and proves every improvement against your own baseline. We embed people to fix it, then measure the progress.
The Forward-Deployed Engineering Team You Couldn’t Hire
GoGloby’s forward deployed engineering services combine senior Architects and AI engineers who have already made this transition. They embed into your team and build the system before delivery begins.
AI Intelligence Layer Deployment
Connected to your Jira, GitHub, and AI tools in the first week. AI adoption, spend, and cost per feature visible from day one, then the Layer keeps reporting as delivery moves.
Agentic SDLC Setup
Task decomposition rules, model selection per task type, quality gates for AI-generated code, PR rules, and testing, built into your existing pipeline and review process.
Model and Cost Architecture
They watch what the Layer surfaces and fix the causes: wrong models on wrong tasks, expensive agent loops, AI spend leaks, and review bottlenecks.
Feature Delivery From the First Delivery Sprint
Real features from your backlog, through your review and release process, at a pace your team can see.
Established-Codebase Expertise
Context engineering, careful decomposition, and AI workflows that hold up on code with 15 years of history.
Quality Held to Your Bar
Every feature passes the same gates as your team’s code. Speed and quality reported together, never one without the other.
Claude Code in Production
Your team’s setup, agent workflows, and context tuned for real delivery on your codebase.
Model Mix and Cost Control
The right Claude model per task, spend optimized against what each task actually needs.
Production Patterns
Proven approaches for agentic development at scale: guardrails, evaluation, and reliability.
How the Forward Deployed Engineering Model Works
Our people work inside your team from week one: Architects build the system, engineers ship through it, and everything they do is measured.
Security and Boundaries for Forward Deployed Engineering
Everything runs inside your perimeter: code, data, telemetry, metrics, and the baseline stay in your environment, under your access rules and revocable any day.
What You Get From Forward Deployed Engineering
Everything our team builds runs inside your environment and improves while it runs: the AI Intelligence Layer keeps finding leaks, the engineers keep closing them, and every fix raises your baseline. When we leave, the whole system stays with you.
One Agentic SDLC
A single way of working with AI for the team: decomposition rules, model selection, and quality gates, built into your pipeline.
Feature-Level Visibility
Every feature priced in dollars through the AI Intelligence Layer, with adoption and spend visible per developer, team, and model.
AI-Speed Delivery Through Your Review Process
Your real backlog moves through your own review and release process, with delivery speed and quality measured against your baseline.
AI ROI That Grows Monthly
The AI Intelligence Layer finds where money and time leak, the engineers fix it, and every fix becomes next month’s baseline.
Forward Deployed Engineering for Enterprise Software Teams
Get Your Forward Deployed Engineering Benchmark
Submit your information to schedule a technical briefing. We will show you what your features cost, who really uses AI, and where delivery gets stuck, then walk you through forward-deployment in under 4 weeks.
FAQ
Forward-deployed engineering is an embedded model where senior engineers work inside your environment to build, deploy, and improve production systems. At GoGloby, forward-deployed engineers work through your pipeline, repos, backlog, and review process, and prove every result against your own baseline.
FDE means Forward Deployed Engineer. On this page it is more than a job title. It is the operating model that turns AI tools into AI-native production, run by an engineer who owns the outcome inside your team.
A consultant recommends and leaves. A forward-deployed engineer works inside your environment and owns delivery until the system works in production, measured on your own numbers rather than a slide deck.
Staff augmentation gives you capacity and nothing else. GoGloby’s model brings Architect-led setup, the AI Intelligence Layer, the Agentic SDLC, hands-on delivery, and measurement against your baseline, together in one contract.
AI tools do not improve delivery by themselves. Forward-deployed engineers connect the tools to your real codebase, workflow, review process, and production constraints, so faster writing turns into faster shipping.
They work in your sprints, decompose tasks, use Claude and other AI tools inside the Agentic SDLC, build features, improve tests, fix blockers the AI Intelligence Layer surfaces, and report progress against your baseline.
You do. Your team sets priorities and owns the roadmap. GoGloby handles vetting, replacement, Agentic SDLC setup, performance visibility, and delivery support, so you get senior output without the hiring risk.
Through the AI Intelligence Layer: cost per feature, AI adoption, spend by team, developer, and model, delivery blockers, review signals, and improvement against your own baseline. Every number is anchored to where you started.
No. It is most useful for established software companies trying to turn AI tools they already bought into production delivery, measurable ROI, and AI-native engineering operations.
Palantir helped popularize the term. GoGloby applies the model to AI-native software development for established software companies, working inside your pipeline and proving results against your baseline.
Every engagement starts with the Benchmark: a fixed fee for one month, with the AI Intelligence Layer deployed inside your VPC and your baseline delivered at the end of it. The fee is credited in full toward your first month of delivery. If you stop there, you keep the baseline.