Turn messy infrastructure data into a trusted operational baseline.
Data Agent maps and reconciles spreadsheets, APIs, and fragmented records into a governed foundation for analytics, finance, and engineering. Every gap, assumption, and confidence score stays visible for review.
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.
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 normalize 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 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.
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.
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.
What Data Agent delivers
Trusted inventory in days, not quarters — with the evidence to defend it.
- Messy spreadsheets and exports
- CMDB and ITAM records
- Vendor inventory files
- Monitoring tool inventories
- Discovery feeds
- Parse and normalize
- Map fields across sources
- Reconcile duplicates and conflicts
- Enrich with lifecycle and ownership
- Confidence-score every record
- Trusted inventory baseline
- Reconciliation and gap report
- Confidence scores
- Ownership and location map
- Exceptions queue for human review
Meet the AI Team
Explores a workflows built on the Netos platform.
Your AI Team
The team that runs around Netos.
BackgroundOur AI Journey
The lessons baked into the agents.
You are hereData Agent
Clean, reconciled inventory.
Next →Finance Agent
Cost models, renewals, business cases.
Continue the story → AgentSystems Agent
Lifecycle, refresh, audit plans.