Data Agent

Automate the hardest part: the data.

Spreadsheets, APIs, exports, and fragmented sources turned into a trusted foundation for analytics and planning.

Meet your AI Team
Where Netos starts

Usually, this is where Netos starts.

Most network intelligence projects do not fail because the desired report is unclear. They slow down because the data is fragmented, inconsistent, duplicated, incomplete, or trapped in tools that were never designed to agree with each other.

Data Agent helps reduce that friction by preparing the foundation before finance or engineering outputs are generated.

Built for data and onboarding.

For

Data owners, platform owners, solution architects, onboarding teams.

Delivers

A clean, mapped, reconciled, enriched data foundation that the rest of Netos runs on.

Inputs

Spreadsheets, ITAM exports, NetBox data, CMDB exports, APIs, supplier files, lifecycle datasets.

D

Data Agent in action

Watch Data Agent import a messy spreadsheet, map it, reconcile it against NetBox, and score every record.

Data Agent in action

60 seconds

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What Data Agent does.

Messy inputs in, agent skills in the middle, trusted data out.

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Three columns, left to right. Left: input nodes (messy spreadsheet, API feed, NetBox, ITAM, CMDB, supplier file). Middle: Data Agent skill nodes (ingestion, mapping, reconciliation, enrichment, validation, ongoing data quality). Right: output nodes (clean inventory, reconciliation report, enriched records, scheduled pipelines, exception report). The Netos data model sits behind the middle column as the target schema and audit trail.

Suggested dimensions: 1600 × 900 px (16:9), SVG or PNG with transparent background.

Data Agent handles the messy middle.

Real customer data rarely lands clean. Common examples Data Agent works through:

Columns renamed across exports

Same data, different headers each time. Data Agent learns the mapping once and applies it to every future export from that source.

Duplicate devices, different hostnames

Two records, one device. Reconciliation merges, keeps the audit trail, and flags ambiguous matches for human review.

Missing serials and lifecycle fields

Records arrive incomplete. Data Agent enriches from vendor and EoX feeds, then scores what was inferred versus verified.

Supplier files vs internal naming

Vendor SKUs and internal product names rarely match. Mappings normalise both ends so finance and inventory speak the same line.

CMDB disagrees with NetBox

Two systems, two versions of the truth. The pipeline surfaces every disagreement so the chosen source of record wins.

Finance records that don't tie to assets

Invoices reference cost centres, not devices. Data Agent ties each line back to the asset, site, or circuit that drives it.

Manual edits with no audit trail

Untracked edits break trust. Every change is recorded with source, timestamp, and reviewer, visible end-to-end.

Human-reviewed

Human-reviewed data quality.

Data Agent does not silently overwrite your source of truth. It surfaces mappings, exceptions, confidence scores, and unresolved gaps so your team can review and approve the right changes.

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What you get out.

  • Clean, mapped, deduplicated inventory
  • Reconciliation reports with confidence scores
  • Enriched records with lifecycle, financial, and product context
  • Repeatable scheduled import pipelines
  • Data quality dashboards and exception reports

Reconciliation, scoring, and audit trail stay in Netos. Every downstream output traces back to the same source.

Platform

How Data Agent works with Netos.

Data Agent is not a standalone AI tool. It operates against the Netos platform: the data model, source of truth, analytics layer, reporting environment, and audit trail.

The agent prepares, maps, and reconciles your data, while your team keeps control of approvals, the source of truth, and how exceptions are resolved.

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What Data Agent delivers

Bring your data in. Netos does the work. You get evidence you can defend.

Inputs
  • Messy spreadsheets and exports
  • CMDB and ITAM records
  • Vendor inventory files
  • Monitoring tool inventories
  • Discovery feeds
Netos does
  • Parse and normalise
  • Map fields across sources
  • Reconcile duplicates and conflicts
  • Enrich with lifecycle and ownership
  • Confidence-score every record
Outputs
  • Trusted inventory baseline
  • Reconciliation and gap report
  • Confidence scores
  • Ownership and location map
  • Exceptions queue for human review
Buyer value
  • Trusted data, fast
  • Less time wrangling spreadsheets
  • A baseline finance and engineering can both trust
  • Source-tracked evidence on every record

Governance: Netos agents work only against governed Netos data, source trails, permissions, and human-reviewed workflows.

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Meet the AI Team

Each page covers one piece, all anchored to the same Netos platform.

Overview

Your AI Team

The team that runs around Netos.

Background

Our AI Journey

The lessons baked into the agents.

You are here

Data Agent

Clean, reconciled inventory.

Next →

Finance Agent

Cost models, renewals, business cases.

Continue the story → Agent

Engineer Agent

Lifecycle, refresh, audit plans.

From a messy spreadsheet to a trusted dataset.

Schedule a Demo See Data Assurance