Data Foundation Agent

Build the clean, governed procurement data foundation your AI strategy needs.

Atlas classifies every spend line, normalizes every supplier, and governs every data decision with explainable AI and human review at every step. No more misclassified spend. No more duplicate suppliers. No more guessing.

Raw ERP export
STAPLES NL, no category,
Staples Netherlands B.VOther
OFFICECENTREMisc.
Atlas output
Staples B.V. (3 entities merged)96%
Office Supplies › StationeryUNSPSC 44.10
EnrichedDUNS · NAICS · ESG flag
Why data foundation matters

Procurement analytics and AI fail because the data underneath them is broken.

Most procurement teams work from data that was never designed to be analyzed. Supplier names appear in five formats. Category codes were assigned manually years ago. ERP systems don't agree on what counts as a contract.

The result: spend dashboards no one trusts, savings reports that can't be acted on, and AI initiatives that stall before they start. Atlas doesn't mask bad data with clever analytics it fixes the data itself, at the transaction level, with explainable logic and your team in control.

Misclassified spend

Lines bucketed into "Other" or the wrong category entirely.

Duplicate suppliers

The same vendor in 8–12 name variants across systems.

Taxonomy gaps

Categories that don't match how the business actually buys.

Unmatchable invoices

Invoice data lacking the line-level detail analysis needs.

What Atlas does

Six capabilities. One governed data foundation.

Spend classification

Classify transactions against any taxonomy UNSPSC, eCl@ss, custom, or hybrid. Every line carries a confidence score and reason code. 90–95% first-pass accuracy.

Supplier normalization

Collapse 8–12 name variants into one clean supplier master consistent legal entity, hierarchy, and metadata enriched with DUNS and D&B data.

Taxonomy generation

Build a bespoke procurement taxonomy from scratch using AI trained on best practice and tuned to your spend, industry, and category mix. Owned by you.

Taxonomy optimization

Audit an existing taxonomy, identify gaps and overlaps, and recommend improvements reviewed by your team before they go live.

Data enrichment

Enrich records with external intelligence: DUNS numbers, D&B data, SIC/NAICS codes, sustainability flags, and firmographic signals.

Governance & review

Every decision is reviewable. Low-confidence classifications queue for sign-off. Reason codes explain every AI decision. Full audit trail included.

Taxonomy management

Your taxonomy is the backbone of every procurement insight.

A taxonomy isn't a list of codes it's the shared vocabulary that makes spend data meaningful. Most enterprises have one; very few have a good one. The telltale sign: an "Other" bucket that swallows 15–30% of spend.

  • Build, optimize, maintainFrom scratch, or improve what you have on your change cadence.
  • Merge, move, and edit with governanceEvery structural change is reviewed and approved by your team.
  • Enforced at every ingestion pointNew data is classified against your living taxonomy automatically.
app.mithra.ai / suppliers
Accenture B.V. 97%
Parent: Accenture plc · DUNS 40-123-8870 · enriched
€18.4M5 aliases merged
Resolved from source systems
ACCENTURE B.V.SAP ECC€9.2M
Accenture (NL)Coupa€4.1M
ACCENTURE-NL-0098Invoices€2.8M
Accenture ConsultingAriba€1.6M
accenture b v amsCSV€0.7M
Merge fully reversible · complete lineage retained for audit
Supplier normalization

One supplier identity across all your systems.

A typical enterprise has the same supplier in 8–12 name formats. Atlas resolves variants into clean parent/child hierarchies and customers typically see a 40–60% reduction in unique supplier count.

  • Entity resolution & merge historyEvery supplier merge shows its full lineage and is fully reversible.
  • Parent / child hierarchySee total spend with a corporate group, not scattered across aliases.
How Atlas works

From raw data extract to governed procurement intelligence

Responsible AI in procurement

Confidence scores and reason codes for every decision.

Enterprise teams can't act on a black box. Every classification includes a confidence percentage and a reason code explaining why. Your team can see the logic, challenge it, override it, and approve it and Atlas learns from every correction.

0
First-pass classification accuracy
0
Decisions with confidence + reason code
Full
Audit trail of every human override
Yours
Customer-specific model tuning
app.mithra.ai / review-queue
Globex Trading LtdInvoice 77412005 · € 1.2M
Critical
Atlas suggestion Uncategorized 38%

Why: No confident taxonomy match possible new supplier entity. Held for a human decision before publication.

Delpharm Milano SRLInvoice 89880001 · € 95.6M
Needs review
Atlas suggestion
ManufacturingContract Mfg
73%

Why: Supplier name matches two taxonomy branches; spend pattern favors Contract Manufacturing.

Bechtle Schweiz AGInvoice 23000342 · € 62.5M
Informational
Atlas suggestion
IT HardwareCompute
95%

Why: High-confidence auto-classification, logged with reason code for your permanent audit trail.

What you get

Six governed outputs from every Atlas deployment

Clean spend cube

Classified, normalized, deduplicated spend by category, supplier, entity, cost center, and time period ready for BI and savings analysis.

Governed supplier master

Normalized hierarchy with clean legal entity names and enriched reference data. One supplier identity across all systems.

Enriched transaction data

Every line enriched with classification, supplier reference, DUNS/D&B, sustainability flags, and taxonomy path. Export in any format.

Optimized taxonomy

A taxonomy reviewed, approved, and owned by your team built or optimized by Atlas and maintained on your cadence.

BI-ready exports

Clean exports in your format: CSV, Parquet, BigQuery, Snowflake, or a direct Looker Studio connector.

Input layer for Pulse

Atlas's governed data is the foundation Pulse reads to find savings. The two agents are designed to work together.

Customer result
"Atlas classified our spend across 14 different ERP instances with an accuracy level our internal team hadn't been able to reach in three years of manual effort."
Procurement Transformation LeadGlobal manufacturing enterprise
0
First-pass accuracy
2–4 wks
To first output
40–60%
Fewer unique suppliers
FAQ

Atlas questions, answered.

Atlas supports UNSPSC, eCl@ss, custom enterprise taxonomies, and hybrid structures. We work with whatever taxonomy your organization uses or wants to build and Atlas can generate a new one if you don't currently have one.
First-pass accuracy typically reaches up to 97% on clean procurement data. The remaining lines go through a human review queue for approval, and accuracy improves over time as Atlas learns from your team's corrections.
Atlas can work with your existing taxonomy structure. It classifies against it, identifies gaps and mismatches, and recommends optimizations. Any taxonomy changes require your team's approval before going live.
Low-confidence classifications, anomalous records, and proposed taxonomy changes are surfaced in a structured review queue. Your data stewards review flagged items, approve or override them, and every decision is logged with a timestamp and user ID.
Yes. Atlas is built for multi-entity, multi-currency, multi-source environments. We normalize currency and entity data as part of ingestion. Most enterprise customers connect three to six sources in their initial deployment.
Both are supported. Mithra offers API-based connectors for SAP, Oracle, Ariba, and other major platforms. Where direct connectors aren't approved, we support secure SFTP, database connections, and flat-file extracts. Most IT reviews take under a day.

See Atlas clean your spend data.

Share a sample extract and watch Atlas classify, normalize, and enrich it with confidence scores and a review queue you control.