Build the System, Ship at AI Speed
Senior Architects and AI engineers, forward-deployed: they instrument your delivery down to a single feature, adapt your SDLC where needed, and ship at AI speed, improving month over month.
- Forward-deployed in under 4 weeks
- 120-day trial period
- Measured against your own AI baseline
Why AI Isn’t Delivering Yet
You bought the tools. The team has the licenses. Delivery looks the same. Here’s what’s actually in the way.
AI chaos instead of AI process
Every engineer works with AI their own way and half the team uses nothing, so the tools are paid for but delivery never speeds up.
AI fails on your codebase
AI shines on greenfield demos but stalls on established codebases, so the platform you actually run is exactly where the tools help least.
Nobody can measure AI adoption
AI broke the old metrics: story points and velocity no longer mean anything, so nobody can say what a shipped feature costs or whether things are improving.
No way to hire the people who fix this
Engineers who’ve done this transition on real production code are the scarcest on the market, so hiring your way out takes six months and rarely sticks.
What Is Forward-Deployed Engineering?
A forward-deployed engineer works 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 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 against your own baseline. You see the progress on your numbers, not our reports.
The AI Team You Couldn’t Hire, Deployed in 4 Weeks
Senior Architects and AI engineers who’ve already made this transition. They forward-deploy into your team and build the system before they ship a single feature.
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.
Agentic SDLC
Task decomposition rules, model selection per task type, quality gates for AI-generated code, 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, broken workflows, review bottlenecks.
Feature delivery from week one
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 fifteen 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, reliability.
How It Works
Our people work inside your team from week one: Architects build the system, engineers ship through it, and everything they do is measured.
Operational and Security Boundaries
What You Get, and What Stays
Everything our team builds runs inside your environment and improves while it runs: the 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, quality gates, built into your pipeline.
Visibility, down to a feature
Every feature priced in dollars, adoption and spend visible per developer, team, and model.
Delivery at AI speed
Your backlog, through your own review process: more every month, each faster than the last.
AI ROI that grows monthly
The Layer finds where money and time leak, the engineers fix it, and every fix becomes next month’s baseline.
See Your Real AI Engineering Costs
One week to connect. You get your baseline: what every feature costs in dollars, who really uses AI, and where delivery gets stuck. Deployed inside your own environment: no interviews, no auditors, no code leaving your perimeter.
Powering The Enterprise Leaders in Major Verticals
Contact Us
Submit your information to schedule a technical briefing. We will walk you through how we put Claude into production safely and our under 4-week embedding process.
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FAQ
An Applied AI Software Engineer is a production-grade engineer who uses AI tools — Cursor, Claude Code, and agent frameworks — to architect, build, and ship software at significantly higher velocity than traditional engineers. They don’t just prompt AI; they govern it. They write specifications before code, manage AI context deliberately, and own the quality of every AI-assisted output. GoGloby’s Applied AI Engineers are vetted through a 4-stage funnel — only 4% pass.
A traditional software engineer writes code manually and sequentially. An Applied AI Engineer uses Agentic SDLC — a spec-first, AI-augmented development process — to compress the same work into a fraction of the time. The difference isn’t AI familiarity; it’s AI discipline. Applied AI Engineers manage context boundaries, validate AI output, and prevent hallucinations in production. The output gap is measurable: clients see 4× sprint velocity and 60–70% Agentic AI commit rates within six months.
They join your sprints, work in your environment, and report to your team leads — just like a senior in-house engineer. The difference is in how they work: spec-first before every build, AI-augmented execution throughout, and measurable commit output from day one. The median time to first production commit is 23 days. Your team directs the work. They multiply the output.
You do. Your team sets priorities, runs sprints, and owns direction. The engineer works inside your environment, under your processes, on your roadmap. GoGloby handles the sourcing, vetting, and replacement guarantee — so you get senior-level output without the hiring risk. If something isn’t working, we replace within our guarantee window. No negotiation required.
The median time to first production commit is 23 days. Engineers arrive with a standardised Agentic SDLC workflow already in place — no process ramp-up, no tool configuration guesswork. Day one they’re speccing. Week one they’re building. By sprint three, output velocity is measurable and visible.
A staffing agency screens résumés and places candidates. GoGloby runs a 4-stage technical elimination funnel — Specify, Navigate, Architect, Govern — that only 4% of applicants pass. You don’t receive a pile of CVs; you receive a shortlist of production-proven Applied AI Engineers in 3–5 days. Beyond placement, every engineer arrives with a standardised Agentic SDLC workflow and sprint-level performance visibility built in. GoGloby is an Applied AI Engineering Partner — not a headcount vendor.