Capabilities
Eight capabilities from cloud foundation to production AI, delivered by one team with one delivery model.
From foundation to intelligence
Each capability has an overview and several pages: what it is, how we deliver it, the architecture, the tools and the proof.
Explore Capabilities
8 topics, each with an overview and a few short pages.
NGT at a glance
One team, one delivery model, five clouds.
Frequently asked questions
What customers ask first.
Mostly AWS, Microsoft Azure and Google Cloud, with delivery experience on Huawei Cloud and Alibaba Cloud too.
FPT Software is a partner of all five. The method is the same on each: a landing zone as code, a 7R decision per workload, waves, then operations.
Not always. For a handful of servers ready to rehost, a short express path is enough: replicate, test, cut over.
For a portfolio, the assessment pays for itself: dependencies, end-of-support items and applications to retire, ending in a roadmap, a TCO and a first wave.
With Scan: two to three weeks to assess repositories, pipelines, secrets and access, and to score maturity. Evaluate turns the findings into a prioritised roadmap in one to two weeks.
If you already know the gaps, we can pilot the pipeline with one team and assess in parallel.
Not when they are designed for it. Fast checks run first, on the laptop and in the pull request; slower scans run on deployed environments.
Thresholds are agreed per team and tightened over time, so the first gates stop critical issues without blocking every build.
No. Backstage is a common choice and NGT has delivered Backstage-based portals, but the portal is one of five planes.
We can build on a portal you have, or start with templates and pipelines and add the portal later.
The first deliverable is an MVP with two or three pilot applications: a portal, the first golden-path templates and the pipelines behind them. We grow from there, team by team.
Both, but the production work is agents that act: they log and route requests, run data queries, watch services and guide failure handling.
A knowledge assistant over your documents is often the first step, and the same harness carries over.
Whatever fits your data rules: Azure OpenAI, Amazon Bedrock, Google Vertex AI, open-weight models on GPU Kubernetes, or in-region endpoints in Vietnam and Japan.
One LLM gateway sits in front of them, so routing, budgets and audit work the same way for every model.
