We embed engineers with your teams to design, build and run AI systems end to end, from the models and GPUs underneath to the data pipelines and cloud platforms around them.
Forward deployment puts engineers inside your environment, with your people, systems and data, until the system works in production and your teams own it.
Assessments, strategy and roadmaps, usually handed over before anything reaches users.
Ends with recommendations
Starts once requirements are written, and often misses the workflow realities that decide adoption.
Ends with a delivered spec
From the first valuable question through architecture, engineering and deployment, embedded with your teams.
Ends with a system in production
Production AI fails at the seams between models, compute, data and platforms. We cover all four, so one team owns the outcome.
Production LLM applications and agents that are evaluated, guarded and maintained, not demos.
The compute layer under your models: sized, scheduled and tuned so inference is fast and GPU spend is justified.
Reliable, governed data that AI can actually use, structured and unstructured, batch and real time.
The platforms that let AI run safely at scale, with the guardrails your security and finance teams expect.
Understand deeply. Engineer practically. Deploy into production.
Users, workflows, systems, data, constraints and the outcomes that matter.
Models, compute, data flows, cloud architecture, security and evaluation.
Agents, applications, pipelines, APIs and the infrastructure beneath them.
Enterprise apps, data platforms, knowledge sources, identity and processes.
Reliability, observability, security, automation and clear ownership.
Support teams, measure outcomes, refine and scale what proves valuable.
We deploy into your own cloud tenancy, under your security, identity and data-residency controls, and choose models on merit for each workload.
Managed foundation models, custom training and GPU serving on AWS.
Enterprise AI inside the Microsoft estate, on your existing identity and data.
Gemini and open models on Vertex AI, next to BigQuery data.
AI and GPU compute close to Oracle databases and applications.
Model providers and hyperscalers need engineers who can turn platform commitments into production workloads inside customer environments. NMI Forward is built to be that team.
Industry-agnostic by design. We work wherever AI, data and cloud can create real operational or commercial value.
Agents that orchestrate multi-system workflows, and copilots for specialised roles.
Enterprise search, retrieval and decision support grounded in your own information.
Structured, reviewable intelligence extracted from large document collections.
Open-weight and fine-tuned models served on your own GPUs, inside your network.
Lakehouses, pipelines and vector stores that turn scattered data into usable context.
Kubernetes, infrastructure as code and release workflows built for AI workloads.
Every engagement is designed around the environment it will enter: people, systems, data, risk, reliability, security, evaluation and long-term ownership.
Bring the problem, the environment and the outcome you need. We’ll map a practical path from idea to production in 30 minutes.