AI is the next chapter.
Netos turns fragmented network, finance, lifecycle, and supplier data into trusted decisions. The AI Team is how we make that delivery faster.
Hero illustration
Timeline of the Netos journey: years of enterprise delivery and a maturing data model on the left, AI agents emerging on top of that foundation on the right, with arrows showing how the platform's history feeds the AI Team's reliability.
The defensible foundation is the data model.
In enterprise software the screen is rarely the value. The value is knowing what the data means, which exceptions matter, what finance needs, what engineers trust, and what executives sign. Netos is built around those realities, and the AI Team operates on top of that knowledge.
For customers, that means outputs are not generated from loose prompts. They are grounded in structured network, finance, lifecycle, and project data.
[ILLUSTRATION PLACEHOLDER]
Stacked-bricks diagram of the data model: domain entities (device, site, contract, circuit), exception logic, finance schema, audit trail, each layer labelled with a real example (e.g. "EoX milestone", "TBM category", "reviewer ID").
Suggested dimensions: 1200 × 900 px (4:3), SVG or PNG with transparent background.
Why AI now.
Customers used to spend weeks preparing data, explaining sources, and mapping fields before Netos delivered value. AI agents now help reduce that work by preparing data, surfacing issues, drafting outputs, and keeping assumptions visible for review. The agents import, clean, reconcile, enrich, analyze, and explain, grounded in the same data model and audit trail the platform has always used.
[ILLUSTRATION PLACEHOLDER]
Before / after timeline. Before: weeks of manual data prep, mapping, and chasing exceptions. After: agents collapse that prep into days, with humans approving exceptions and signing off outputs.
Suggested dimensions: 1200 × 900 px (4:3), SVG or PNG with transparent background.
From software product to AI team.
The next version of Netos is AI agents around a mature product. Finance Agent works the financial questions. Data Agent prepares and maintains the foundation. Engineer Agent turns inventory into refresh, risk, and audit outputs.
Start with one governed AI-assisted workflow.
[ILLUSTRATION PLACEHOLDER]
Three AI agents (Finance, Data, Engineer) circling the Netos platform at the centre, labelled arrows showing what each agent reads from and writes back to the platform's data model and audit trail.
Suggested dimensions: 1200 × 900 px (4:3), SVG or PNG with transparent background.
What this means for customers.
Netos AI does not remove enterprise complexity. It helps your teams work through it faster.
You get the benefit of AI-assisted delivery without losing the governance, traceability, and maturity expected from enterprise software.
The result is faster onboarding, cleaner data, better reports, stronger business cases, and more defensible network investment decisions.
[ILLUSTRATION PLACEHOLDER]
Five outcome chips arranged as a rising ladder: faster onboarding, cleaner data, better reports, stronger business cases, more defensible decisions, each chip annotated with a one-line proof point.
Suggested dimensions: 1200 × 900 px (4:3), SVG or PNG with transparent background.
Built on real enterprise delivery.
The agents are powerful because the product underneath has been shaped through thousands of decisions, customer conversations, and edge cases.
in active development against real enterprise networks.
of engineering on the product, integrations, and data model.
tracked, fixed, and shipped in YouTrack.
shaping the product against real data.
with MSPs, vendors, and integrators.
What looks simple, and what actually makes it hard.
Every capability in Netos exists because of something learned the hard way.
Spreadsheets land messy
Customer files are inconsistent, incomplete, and renamed. Mapping is never one-shot.
Sources disagree
No single system is complete. Reconciliation takes judgment, not just rules.
Lifecycle is multi-layered
Vendors, components, support dates, software versions, and criticality all decay at different rates.
Finance speaks a different language
Cost models rarely map cleanly to infrastructure. CapEx versus OpEx is a per-customer conversation.
Business cases need options
Technical decisions become CapEx, OpEx, timing, risk, and a recommendation that survives the board.
Reports need trust
Outputs are only useful when the data model, assumptions, and audit trail are visible.
Meet the AI Team
Each page covers one piece, all anchored to the same Netos platform.