Working together
Forward Deployed Engineering.
Work directly with an engineer who understands the data, helps define the solution, and carries it through implementation and handover.
Forward deployed engineers sit at the intersection of product, data and delivery — close enough to the business to understand it, and technical enough to build it.
A forecasting problem might require source reconciliation, a statistical model, and a new reporting workflow. Working closely with your team lets us connect those pieces and resolve questions as the system takes shape.
In practice
A shared understanding. A working system.
Direct collaboration
Work with the people who know the data and operate the system, in the repositories and tools the team uses.
Defined scope
Agree on the problem, acceptance criteria, and milestones, then make progress and trade-offs visible.
Explicit rules
Capture business constraints in specifications and relevant checks, with exceptions available for review.
Supervised AI development
Use coding agents for investigation and implementation while retaining engineering ownership of the result.
How we work
Close to the problem.
Accountable for the build.
We work directly with your team, inside your data and systems. The engagement follows the problem—from understanding the inputs to building, validating, and handing over a working solution.
Our approach to embedded engineeringHow it works
Five stages from first call to handover.
- 01
Understand
Map the decision, the people, and the systems involved.
- — Understand the current workflow
- — Review access and data availability
- — Agree on the problem and success criteria
- 02
Investigate
Inspect the data and identify the constraints that shape a useful solution.
- — Review sources, models, and code
- — Document assumptions and source conflicts
- — Prioritize a practical scope
- 03
Build
Develop in manageable increments, with regular reviews of working software.
- — Implement the agreed design
- — Use supervised AI where it helps
- — Review progress and technical trade-offs
- 04
Validate
Compare results with source data, expected behavior, and agreed acceptance criteria.
- — Run relevant checks and comparisons
- — Record exceptions and limitations
- — Review the evidence together
- 05
Handover
Make the system understandable and operable for the team that owns it.
- — Document the architecture and workflow
- — Walk through operation and maintenance
- — Agree on any ongoing support
Is it a fit?
Where this way of working is useful.
- Recurring processes that depend on manual spreadsheet work
- Forecasts or allocations that have to respect business rules
- AI ideas that need a defined task and a way to evaluate them
- Teams that want to work directly with the engineer building the system
- Numbers that need to be explained, checked, and reproduced
- Existing code or workbooks that need careful modernization
Engagement models
Choose your starting point.
Focused assessment
Technical Review
Understand an existing workflow, model, or codebase and identify a practical next step.
- Source and system review
- Findings and priorities
- Recommended scope
- Technical walkthrough
Scoped project
Embedded Build
Work directly with your team to design, implement, and validate a defined system.
- Agreed scope and milestones
- Implementation and integration
- Validation evidence
- Documentation and handover
Ongoing collaboration
Technical Partnership
Continue improving a system through recurring development, model updates, and architecture work.
- Prioritized technical backlog
- Recurring development cycles
- Architecture and model reviews
- Operating support
FAQ
Questions, answered.
What is forward deployed engineering?
It means working directly with your team in the environment where the problem exists. The work includes understanding the business context, writing the code, and helping the team operate the result.
What do we receive at handover?
The agreed system and source code, relevant validation evidence, and documentation for operating and maintaining it. Ownership, access, and any ongoing support are agreed as part of the scope.
Which problems are a good fit?
Data preparation that needs automation, forecasting or allocation models with business constraints, and custom software or AI workflows. The price-tier modeling project is one example of our work in market intelligence.
How do you use AI in development?
Supervised coding agents help with investigation, implementation, refactoring, and documentation. An engineer owns the technical decisions and reviews the work. Data access and approved tools are agreed with the client.
Start a conversation
Put an engineer in the room.
Tell us about the data, the decision, or the workflow you want to improve. We’ll start there.
Discuss your project