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Svitla AI
Forward Deployed Engineering

Embed AI into your operating logic, then leave a repeatable in-house capability behind

AI-native operations can’t be created by bolting AI tools into old workflows and keeping the organizational design unchanged. Svitla’s Forward Deployed Engineering can help you rebuild the decision logic, controls, and data flows inside your operational environment, so your standards, patterns, and governance are production-grade from the first build cycle.

Book a Scoping Call
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20-40% faster
Cycle time on the target workflow
3 to 5
Production use cases live per quarter 
Senior FDE squad
No junior handoffs
The Problem
Your AI works in a demo but stalls the moment it hits your data, controls, and edge cases?

We combine product, data, and engineering skills so AI is shaped, tested, governed, and refined around your real operating conditions. 
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Fragmented workflows
Work passes through too many hands, and every handoff adds queue time, latency, and another point where context gets lost. That overhead is manageable at low volume and expensive at scale. 
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Undocumented decision logic
The rules that drive your decisions live in the judgment of experienced operators rather than in any documentation. You can’t automate or safely scale logic no one has written down. 
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Scattered and untrusted data
The data you need exists, but it’s spread across systems with no single source of truth and inconsistent quality. Teams don’t trust it enough to act on, so decisions stall or fall back to manual work.
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Governance and audit gates
Moving to production means proving your controls: audit trails, access boundaries, and human review. When those aren’t embedded in, the system never clears compliance and never ships.

Book a Free AI consultation

Can you spot an operational challenge but not sure about the right path to production? 

Book a Scoping Call
What AI FDE delivers? 
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Documented and reusable operating pattern 
The pipelines, standards, and integration patterns from the first build are documented as the work happens. They carry into the next use case, so your second and third projects start ahead. When the engagement ends, your team owns the pattern as an in-house capability.
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Cycle-time reduction on the target workflow
We map the workflow you want to compress, then rebuild its decision logic, data flow, and handoffs around AI. Senior engineers own the build end to end, with no junior handoffs adding queue time. You measure the before-and-after against the baseline set in scoping.
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Automation coverage of the chosen process 
We document the decision logic your operators apply today, then automate the part AI can safely run. Governance, audit trails, and human review are built in, so coverage expands without losing control. What stays manual is a deliberate call, not a gap. 
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Production use cases live per quarter 
Engineers embed in your environment and ship working use cases into production. Each build runs against your real data, security controls, and decision logic, so it clears review. The cadence holds across quarters as the squad reuses what earlier builds established.

Book a Free AI Consultation

Not sure if FDE fits your workflow?

Book a Scoping Call
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SVITLA AI

Gets You to ROI Positive Faster

Match the approach to the outcome you need 

AI consultingSolutions engineering Forward Deployed Engineering 
Starting question What should we do about AI? Can the product fit our stack? If AI were native here, how would this work be designed? 
Main outputAnalysis and recommendationsA proven fit and a target architectureA system running in production, plus reusable patterns
The processAssessed, not changedFitted to the existing workflowRedesigned around AI
Where the work happens Advises from outside; requirements travel back and forthDesigns the architecture, then hands to delivery Embedded in your team and environment through go-live
Success measured by Recommendations deliveredArchitecture accepted Live system, AI adoption, and cycle-time gains
Who owns production Hands off before the development Designs it, doesn’t run itBuilds, deploys, and owns it live
What’s left behindA strategy to act onA one-off delivered solutionA repeatable operating pattern and in-house capability 
Starting question 
AI consultingWhat should we do about AI? 
Solutions engineering Can the product fit our stack? 
Forward Deployed Engineering If AI were native here, how would this work be designed? 
Main output
AI consultingAnalysis and recommendations
Solutions engineering A proven fit and a target architecture
Forward Deployed Engineering A system running in production, plus reusable patterns
The process
AI consultingAssessed, not changed
Solutions engineering Fitted to the existing workflow
Forward Deployed Engineering Redesigned around AI
Where the work happens 
AI consultingAdvises from outside; requirements travel back and forth
Solutions engineering Designs the architecture, then hands to delivery 
Forward Deployed Engineering Embedded in your team and environment through go-live
Success measured by 
AI consultingRecommendations delivered
Solutions engineering Architecture accepted 
Forward Deployed Engineering Live system, AI adoption, and cycle-time gains
Who owns production 
AI consultingHands off before the development 
Solutions engineering Designs it, doesn't run it
Forward Deployed Engineering Builds, deploys, and owns it live
What's left behind
AI consultingA strategy to act on
Solutions engineering A one-off delivered solution
Forward Deployed Engineering A repeatable operating pattern and in-house capability 

Ready to own your AI direction?
Book a free 30-minute call. We’ll diagnose your situation and tell you honestly whether this is the right fit.