Put engineers where the outcome happens.
The hardest problems rarely reveal themselves in the specification. They appear when technology meets real users, real data, real systems and the realities of day-to-day operations.
Margins Forward Deployed Engineers work close to your business — understanding what happens in production, solving what gets in the way, and continuously adapting the technology until it delivers the intended outcome.
Engineering measured by what works in the business, not what was delivered against the specification.
The specification is an assumption. Production is reality.
- Understand the process.
- Interview users.
- Analyze data.
- Design workflows.
- Define requirements.
- Test the system.
- Users behave differently than expected.
- Data contains edge cases.
- Processes have exceptions nobody documented.
- Integrations behave differently under real load.
- The model encounters information it hasn’t seen before.
- The workflow makes sense technically but creates friction operationally.
- Sometimes the original assumption about the problem itself turns out to be incomplete.
The difference between software that was delivered and software that creates value is what happens next.
Engineers close enough to the business to understand what needs to change.
Traditional delivery moves information through layers. By the time the engineer sees the problem, they see a ticket. Forward Deployed Engineering shortens that distance.
- Business
- Product Owner
- Project Manager
- Business Analyst
- Engineering
Information moves through layers. The engineer receives a ticket.
- Users
- Operations
- Data
- Systems
- Management
The engineer doesn’t just receive requirements. They understand why the requirement exists.
Part engineer. Part problem solver. Close to the operation.
A Forward Deployed Engineer is a senior technical professional who works directly with the customer and stays close to the environment where the technology is actually being used.
Instead of waiting for perfectly defined requirements, they investigate problems, work with users, understand constraints, make technical decisions and implement solutions.
Their job isn’t simply to write code. Their job is to make the technology work for the business.
From problem to production without the handoffs.
Work with users
Observe how people actually use the system and understand where reality differs from the original design.
Investigate problems
Go beyond tickets to understand the underlying operational or technical cause.
Build and adapt
Implement changes across AI, software, data, integrations and workflows.
Handle edge cases
Identify the exceptions that emerge only once a system meets real-world conditions.
Connect systems
Solve integration problems across enterprise platforms, APIs, databases and internal software.
Improve AI performance
Evaluate outputs, investigate failure modes and improve models, prompts, retrieval, tools and surrounding logic.
Measure outcomes
Understand whether the system is actually producing the business result it was designed for.
Find what comes next
As engineers learn the operation, they often uncover additional opportunities for automation and improvement.
Not staff augmentation. You’re putting engineering closer to the problem.
Staff augmentation provides people against roles. Forward Deployed Engineering provides technical ownership around an outcome.
The goal isn’t more engineering hours. It’s less distance between the problem and the people capable of solving it.
AI systems can’t be fully specified before they meet reality.
if X happens → do Ythousands of variations in language, documents, images, behaviour and contextThat makes the feedback loop after deployment significantly more important.
The FDE observes
- 01Where is the AI wrong?
- 02Where is confidence too low?
- 03Which edge cases keep appearing?
- 04What information is missing?
- 05Where should a human intervene?
- 06Which users aren’t adopting the system?
- 07Where is the workflow creating friction?
- 08Is the system actually changing the business metric?
Production becomes part of the development process.
Build. Observe. Learn. Improve.
Instead of waiting weeks for operational feedback to travel through layers of management and backlog prioritization, the engineer sees what is happening and can act.
Signals the FDE watchesThe FDE closes the feedback loop between what happens in production and what engineering does next.
Wherever technology meets a complex operation.
Stay close to users and continuously improve AI systems after launch.
Understand exceptions and extend automation as real-world complexity emerges.
Work across ERP, CRM, internal systems, APIs and data infrastructure.
Move quickly between customer feedback, product decisions and engineering.
Embed technical capability inside business units as new AI-enabled workflows are introduced.
Navigate systems and processes that can’t be fully understood from documentation alone.
Support initiatives where requirements evolve quickly as the business learns.
Close to your team. Backed by ours.
An FDE shouldn’t feel like an isolated contractor placed inside the customer. They have the entire Margins engineering organization behind them.
Embedded with your business. Backed by 40+ technology professionals.
- 01A dedicated technical counterpart
Someone who develops deep context around the business and system.
- 02Direct access to the operation
The FDE works with the people closest to the problem rather than only through tickets.
- 03Margins engineering depth
When the problem needs deeper expertise in ML, data, cloud, frontend, backend, computer vision or architecture, the FDE brings the wider Margins team in.
- 04Continuity
Knowledge accumulates instead of being repeatedly transferred between disconnected teams.
Shorter distance. Faster learning. Better decisions.
Same problem, two routes to a fix.
- 01User finds problem
- 02Reports it
- 03PM interprets it
- 04Ticket created
- 05Engineering receives it
- 06Questions return
- 07Ticket refined
- 08Solution built
- 09User tests it
- 10New problem discovered
…and the loop starts again.
- 01FDE sees problem with user
- 02Understands context
- 03Investigates system / data
- 04Defines solution
- 05Builds with engineering
- 06Deploys
- 07Observes result
- 08Improves
Learning stays with the engineer who acts on it.
Go-live is where Forward Deployed Engineering begins to matter most.
The software is live. Now real users interact with it. Real data flows through it. Real exceptions appear. And the assumptions made during design finally meet reality.
- 01Deploy
- 02Train
- 03Adopt
- 04Forward Deployed EngineeringThis service
- 05Operate
- 06Improve
Systems need both operations and evolution.
Change the system.
- Works close to users and operations.
- Investigates problems.
- Implements improvements.
- Adapts workflows.
- Solves new requirements.
- Improves how technology fits the business.
Keep the system running.
- Monitors infrastructure.
- Tracks model / system performance.
- Manages reliability.
- Observes usage and cost.
- Maintains production operations.
- Responds to operational issues.
Managed AI keeps the system healthy. Forward Deployed Engineering keeps it moving forward.
Shipping isn’t the outcome.
Engineering output matters. But the ultimate question is whether the system is changing what it was built to change. That might mean:
- Less manual work
- Faster operations
- Higher employee adoption
- Better decisions
- Fewer errors
- Earlier detection
- Lower operating cost
- More revenue
The FDE is the engineering function closest to that outcome.
Put the right engineer close to the right problem.
Every engagement starts with a mission — the problem, outcome or production responsibility the FDE will own. Not a job description.
- 01
Understand the environment
We learn the business objective, existing system, technology environment and operational context.
- 02
Define the mission
Agree on the problem, outcome or production responsibility the FDE will own.
- 03
Match the engineer
Select an engineer with the technical depth and working style appropriate to the environment.
- 04
Embed
The FDE works directly with your operational and technical teams.
- 05
Build the feedback loop
Establish direct access to users, production signals, business KPIs and the broader Margins engineering team.
- 06
Improve continuously
Prioritize and execute against what is actually happening in the business.
Forward Deployed Engineering works particularly well when…
If the work can be completely defined, handed to engineering and delivered against a stable specification, you probably don’t need an FDE.
- The technology is already in production but needs continuous adaptation.
- The problem can’t be completely specified upfront.
- Users and engineers need a much tighter feedback loop.
- AI behaviour needs to be evaluated against real-world conditions.
- The implementation spans multiple systems and business processes.
- Operational complexity creates frequent exceptions.
- The business is moving too quickly for traditional requirement handoffs.
- You need senior technical ownership without building an entire specialist AI team internally.
Forward deployed, with an engineering company behind them.
AI-native engineering
FDEs work across modern AI systems, software, data and enterprise integrations.
Business context
The role is designed around understanding the operation, not just the codebase.
End-to-end capability
An FDE can pull in AI, backend, frontend, data, DevOps, UX and architecture expertise when needed.
Production mindset
We optimize for what works after deployment, not merely what passes acceptance.
Outcome orientation
The engineering loop remains connected to the reason the system was built.
Proven delivery organization
One accountable team, with continuity between the people who scope and those who build.
Related capabilities
AI Engineering
Build production AI systems around your data, workflows and existing technology.
Explore AI Engineering →Managed AI
Operate, monitor and maintain AI once it enters production.
Explore Managed AI →AI Transformation
Embed AI systematically across the organization.
Explore AI Transformation →AI Training & Enablement
Prepare your people to work effectively with the technology being introduced.
Explore AI Training →
Put an engineer where the problem actually happens.
If you have a complex AI or software system where success depends on understanding what happens after deployment, we’ll help put the right technical capability close to the operation.
Talk to an FDEClose to the business. Close to the technology. Accountable for what happens between them.