We put engineers inside your business until the AI is actually running.
Most enterprise AI does not fail at the model. It fails in the last mile — integration, data access, security review, and the unglamorous work of getting something into production and keeping it there. That is the part we do.
The prototype worked. Then it sat there.
The pattern repeats across every industry we work in. None of these are model problems, which is why buying a better model does not fix them.
Applied AI engineering, on infrastructure we already know how to secure.
We are a network, security and cloud engineering firm that now builds the AI layer running on top — which means the application and the platform underneath it are designed by the same people.
AI applications
Bespoke applications built for your environment and owned by you — from document and voice pipelines to decision-support tooling that plugs into the systems your teams already use.
Agentic workflows
Agents and automations that take real actions against real systems, with the permissions, audit trail and human-in-the-loop controls that make them approvable in a regulated environment.
Data & retrieval
The unglamorous half of every AI program: getting to the data, cleaning it, indexing it, and building retrieval that returns the right context — without moving information somewhere it is not allowed to go.
MLOps, evals & guardrails
Evaluation harnesses so you can prove behaviour rather than assert it, deployment pipelines, monitoring, and safety controls aligned to Cisco’s AI Security and Safety Framework.
Our engineers work inside your environment, not from behind a statement of work.
The forward deployed model — sometimes shortened to FDE — means a senior engineer sits in your working sessions, builds against your real systems and data, and is measured on whether the thing reaches production. It replaces long specification cycles with short build-and-measure loops.
In your environment, on your cadence
The engineer joins your stand-ups, your change process and your ticketing — and builds against the real systems rather than a sanitized copy.
- Senior practitioners, not coordinators
- Aligned to your change and release process
- Working software in front of stakeholders early
Short loops instead of long specs
AI requirements are discovered by observing behaviour on your own data. We build the smallest useful thing, measure it, and let the evidence set the next increment.
- Evaluation before expansion
- Go / no-go proven on real constraints
- No committing budget ahead of evidence
Your team ends up owning it
Knowledge transfer is part of the engagement, not a closing formality. You own the code and the design, and your engineers can operate it without us.
- You own what we build
- Documentation and enablement included
- We are finished when your team is confident
Senior engineers in North America, Europe and LATAM.
Our forward deployed engineers are based in North America, Europe, and Latin America, and we deploy them to customers across all three regions.
We do not run an offshore delivery model. The engineer in your working session is the practitioner doing the work — in your timezone, reachable, and accountable for the outcome.
Prove the hardest question first.
We would rather tell you in four weeks that something will not work than in nine months. The sequence is built to surface that early.
The application and the infrastructure, designed together.
Our engineers design and deploy the network, security and compute layer — and build the AI applications that run on it. Where your business already runs on a commercial software platform, our partner ecosystem brings it and we integrate it.
We engineer software for our customers; we do not sell or license anyone’s. Ngenium carries no product margin. We never sell hardware and we never sell or license software, so nothing on a vendor price list influences what we recommend to you. What we build under a services contract belongs to you.
- AI & AI PODs — the infrastructure layer: Cisco AI PODs, GPUs, fabrics and the Secure AI Factory
- Professional services — how we engage: consulting, design, implementation, enablement, automation and PMO
- Vendor-agnostic engineering — why services-only changes the recommendation you get
- A worked example — an applied-AI proof of concept, documented honestly
Straight answers.
What is a forward deployed engineer?
A forward deployed engineer is a senior engineer embedded directly in a customer’s environment and working sessions, rather than delivering from behind a statement of work. The model trades long specification cycles for short build-measure loops against your real systems and real data. Ngenium’s forward deployed engineers are based in North America, Europe, and Latin America.
Ngenium builds software now — is it still services-only?
Yes, and the distinction matters. Services-only has always meant one thing at Ngenium: we carry no product margin. We do not sell hardware, we do not sell or license software, and no vendor price list influences what we recommend. When we build an AI application or engineering tooling for a customer, that is engineering work delivered under a services contract and the customer owns the result. Building software for you does not give us a reason to recommend the wrong platform to you. Selling you someone else’s would.
Where are your engineers based?
Our engineers are based in North America, Europe, and Latin America, and we deploy them to customers across all three regions. We do not run an offshore delivery model — the engineer in your working session is a senior practitioner, not a coordinator in front of an unnamed team.
How is this different from staff augmentation?
Staff augmentation fills a seat. A forward deployed engineer owns an outcome: they sit in your environment, build against your real systems, and are measured on whether the AI reaches production and stays there. Ngenium offers both, but the forward deployed model is the one that suits work where the requirements are still being discovered.
Have an AI project that stalled after the prototype?
Tell a senior engineer what you have and where it stopped. Thirty minutes, an honest read, no deck.