How We Work
We don't advise and leave.
We build and stay.
CloudIQ engineers embed
inside your team, your stack, and your workflows — writing production code against
your real data until the system ships and the outcomes are measurable.
Still exploring FDE?
Forward Deployed Engineering is a delivery model — not a title — where engineers embed inside your business and own the outcome, not just the engagement. Scroll down to see what it means in practice.
Evaluating firms?
CloudIQ has operated this way since our first enterprise engagement. Below you'll find our methodology, our proof points, and the questions we'd recommend asking every firm you're considering.
The Model
What does it mean to work this way?
Traditional consulting separates thinking from doing. A firm advises, delivers a recommendation, and moves on. Your team is left to turn the strategy into a working system.
Forward Deployed Engineering closes that gap. Our engineers work inside your team, access your codebase, work with your data, and own production outcomes — not just deliverables.
The FDE model, plainly stated
What the industry means — and what CloudIQ delivers
Definition
"A forward deployed engineer is a senior AI builder
embedded inside a client's environment — identifying where AI creates real value, designing the system, writing the production code, and staying until it's adopted and measurable."
Why it's gaining attention now: OpenAI launched a $4B+ venture built entirely on this model in May 2026. Anthropic, IBM,
Salesforce, and Google Cloud followed. The delivery model enterprises have been asking for now has institutional validation — and CloudIQ has been running it for years.
Questions to ask any FDE firm
? Does your engineer find and define the opportunity, or wait
to be told?
? What does the engineer personally build vs. delegate?
? Is production adoption part of how success is measured?
? What specifically gets transferred to our team — and in
what form?
Our Engagement Model
Find. Build. Operate.
Every engagement runs on AI-DLC, our spec-to-production methodology. Each phase ends with something measurable.
Map your workflows, data, and systems to find the highest-value buildable opportunity.
- Opportunity scoped to your stack
- First build chosen for value and feasibility
- Compliance surfaced early
Design, build, test, and deploy production AI inside your workflows.
- Software tied to a real workflow
- Metrics defined before development
- Traceability and automated QA
Stay past go-live to tune, improve, and transfer patterns to your team.
- Monitoring and tuning
- Reusable components and patterns
- Faster path to the next build
Who This Is For
Two kinds of buyers. One model that works for both.
Whether you're still figuring out what FDE means for your business or you're actively evaluating firms — this is what a conversation with CloudIQ looks like.
IF YOU'RE STILL EXPLORING
You know AI should be doing more. It isn't yet.
You've run pilots. You've bought tools. You've heard the pitches. But nothing has made it to production in a way that changes how the business actually operates. That's not an AI problem. That's a delivery model problem.
- You have a proof of concept that hasn't become a product
- Your AI initiative is "on the roadmap" — and has been for a while
- Your team is good at prompting but hasn't shipped a production system
- You received a strategy deck. You needed an engineer.
IF YOU'RE EVALUATING FIRMS
You know the model. You're deciding who executes it best.
You've done the research. You understand what FDE means. Now you're comparing firms on methodology, proof points, and whether their engineers actually own production outcomes — or just call themselves forward deployed.
- CloudIQ's AI-DLC is a production-tested spec-to-deployment pipeline
- Our engineers are embedded — not coordinated remotely from a delivery center
- We measure on production adoption and business outcomes, not ticket velocity
- Our partnerships span Microsoft, AWS, and Anthropic — we work across your stack
What This Isn't
Three things CloudIQ is not.
The FDE label is being adopted fast. Here's how to tell the real thing from a rebadged version of something older.
Staff Augmentation
Adding capacity to a backlog
you've already defined. Capacity multiplies zero if nobody in the building knows what to build.
CloudIQ engineers find and define the opportunity — they don't wait for a ticket.
Advisory that leaves a deck
A strategy produced from outside
your environment, handed off before the hard part starts. The gap between the recommendation and the running system is exactly where enterprise AI fails.
CloudIQ stays in the room until the
system is live and the metrics are moving.
A rebadged body shop
A contractor with a new title who waits to be told what to build, guards knowledge rather than transferring it, and disappears at go-live.
CloudIQ engineers own the loop from opportunity identification to production adoption.
Our Delivery Engine
AI-DLC: the methodology inside the model
Every CloudIQ engagement runs on AI-DLC — our spec-driven, AI-assisted delivery pipeline. It's what makes our embedded model repeatable, traceable, and faster than conventional delivery at every layer.
📋 Spec-driven from the start
Requirements converted to structured specs before a line of code is written. Ambiguity is the enemy of production-grade software.
🤖 AI-assisted at every layer
AI accelerates code generation, QA, and evaluation — with human judgment directing every decision that matters.
✅ Built-in traceability and QA
Every output is evaluated against the spec. No surprises at go-live. No post-deployment scrambles.
Continuous — not project-based
AI systems need ongoing tuning and governance. AI-DLC is designed for continuous delivery, not one-time launches.
In Production
What we've shipped.
Real engagements. Real environments. Outcomes that were agreed before the build started.
1,200+
Pages of legacy logic modernized · Under one year
Healthcare · Compliance · Modernization
Legacy application modernized at scale — HIPAA compliance maintained throughout
A major healthcare operator's legacy application logic — 1,200+ pages — rebuilt into a modern, AI-assisted platform. Delivered in under a year at a fraction of conventional cost. Full HIPAA compliance maintained throughout.
3→1
Fragmented platforms unified into one system
Healthcare · Mobile · AI Engineering
Three siloed mobile platforms rebuilt into a unified AI-native system
Scheduler, Messenger, and Records — three disconnected platforms critical to daily operations — rebuilt into a single unified system with AI embedded at the data and workflow layer.
Azure
Global distributed system · Rebuilt from monolith
Pet Care · Azure · App Modernization
Monolithic referral system rebuilt as a global distributed platform on Azure
Medical records that didn't follow patients. A referral system that duplicated instead of connected. Rebuilt on Azure — records follow patients, not paper, across a global network of care providers.
Your AI pilot
shouldn't stay a pilot.
If your AI pilot hasn't reached production, that's not an AI problem. That's a delivery model problem.
That's exactly where Forward Deployed Engineering starts.
Values
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Leveraging the power of leading platforms to deliver exceptional AI solutions.
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