LenouarWe don't hand over software. We make it work.
Forward deployed engineering: we work inside your environment to install the stack, connect your systems, configure the workflows, and validate the audit trail before anyone calls it live.
Most AI projects do not fail at the model.
They fail in the distance between a working demo and a system your team actually uses, that your assessors accept, and that still runs the week the person who built it goes on leave. That distance is where we work.
Inside your environment, not around it.
Forward deployment means the engineering happens where the problem is: on your network, against your real documents, under your permissions model, alongside the people who will use the system every day.
- Your document formats, not a clean sample set. Real scans, real templates, real inconsistency.
- Your permissions model, enforced at query time, so retrieval cannot surface what a user was never entitled to open.
- Your systems, connected directly: EHR, ERP, document stores and internal APIs.
- Your reviewers in the loop, with checkpoints on anything carrying legal or clinical consequence.
- Your audit trail, validated against the standards you are actually assessed against.
Six stages, in order.
Every deployment runs the same path. Installation is measured in days because the stack ships preconfigured. The stages that take real time are integration and validation, which is where deployments succeed or fail.
Scope
We map the workloads, data classes and language mix, and size the deployment against your real utilisation rather than a launch-week forecast.
Install
Deployment on your own infrastructure and your own network, on premises or in your private cloud.
Connect
Integration with the systems you already run, so the system reads from where your work actually lives instead of from a copy of it.
Configure
Retrieval scope, permissions, workflows and human checkpoints, tuned to how your teams operate rather than to a default template.
Validate
We confirm the audit trail answers the questions an assessor will ask, before handover rather than after the finding.
Operate
Monitoring, maintenance, patching and model lifecycle continue after go-live. Owning the system should not mean inheriting an operations problem.
What forward deployment is not.
The term gets used loosely, so it is worth being precise about what you are and are not buying.
- Not staff augmentation. We are not filling seats on your org chart or taking day-to-day direction as contractors.
- Not a body shop. The engagement is scoped to a deployment outcome, not to a monthly headcount.
- Not a replacement for your IT function. We work alongside your team and hand over a system they can hold.
- Not an open-ended retainer. Every stage has an exit criterion and a definition of done.
When forward deployment is the right call.
It fits when the constraints are real and the workload is operational. It does not fit when you mainly need a software licence and already have the platform team to run it yourself, and we will say so during scoping rather than after the purchase order.
Regulated data
Your data class cannot move freely, so the system has to come to the data rather than the other way around.
Document-heavy operations
The value sits in documents, records and repeatable workflows rather than in open-ended reasoning.
No platform team to spare
You need the system running without first hiring an inference infrastructure capability to support it.
Bring AI inside your walls.
Talk to us about a private, compliance-ready deployment for your organization.