The agentic spend intelligence layer

From messy spend data
to measurable savings.

Mithra's agents clean, normalize, and classify spend, then surface savings across consolidation, PPV, contract leakage, and payment terms.

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HOW MITHRA WORKS

Opportunities are built layer by layer.

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Built for procurement

Atlas, your procurement copilot.

The copilot your team works with every day to question, understand, and act on spend.

Procurement copilot

Atlas

The copilot your team works with every day.

Ask Atlas anything about spend, suppliers, contracts, and savings. It reasons over your governed data, and every agent below, to give answers you can act on.

  • Analysis Agent
  • Report Agent
  • Category Strategy Agent
  • Data Extract Agent
Meet Atlas
Clean & Govern

Data Foundation Agents

Clean, classify, normalize, and govern your procurement data.

Ingest fragmented data from any source, then classify, normalize, and enrich every line, with human review and explainable AI built in.

  • Categorization Agent
  • Normalization Agent
  • Taxonomy Agent
  • Enrichment Agent
  • Material Harmonization Agent
  • Contract Agent
Explore the data agents
Optimize Value

Opportunity Agents

Surfaces savings opportunities from your trusted data foundation.

Scans your governed data for consolidation, PPV, off-contract spend, and leakage, prioritized by business impact.

  • Tail Spend Agent
  • Maverick Spend Agent
  • External Benchmark Agent
  • PPV Agent
  • Price Leakage Agent
  • Master Agreement Agent
Explore Opportunity Agents
The path to agentic procurement

Procurement can only be as intelligent as its data

Every AI initiative depends on one thing: a clean, governed data foundation. Mithra gives you that \u2014 plus the optimization layer on top.

Mithra operates at levels 1 through 5. Most procurement teams are stuck at level 1 or 2. We help them climb.

What Mithra solves

Use cases across the procurement data lifecycle

Data Foundation

  • Spend classification against any taxonomy (UNSPSC, custom, hybrid)
  • Supplier deduplication & hierarchy normalization
  • Taxonomy generation from scratch with AI
  • Taxonomy optimization for existing codebases
  • Enrichment: risk, price index, market intelligence, ESG
  • Governance & review workflows for data stewards

Insight & Optimization

  • Supplier consolidation opportunity identification
  • Purchase price variance detection & root cause
  • Contract leakage & off-contract spend tracking
  • Invoice anomaly & duplicate detection
  • Payment terms optimization analysis
  • Category tail spend visibility & rationalization

Enterprise Architecture

  • Multi-ERP, multi-entity, multi-currency harmonization
  • BI-ready clean exports for Looker, Power BI, Tableau
  • Clean data layer for AI agent projects
  • Integration with SAP, Oracle, Ariba, Coupa, Ivalua
  • Secure data handling for IT & compliance
Time to value

From raw data to governed intelligence in days, not months.

01

Connect

ERP, P2P, invoices, contracts, CSV

02

Ingest

Structured and unstructured data parsed and mapped

03

Classify

AI categorizes every transaction to your taxonomy

04

Normalize

Supplier deduplication and master data harmonization

05

Enrich

External data, confidence scores, reason codes added

06

Detect

Opportunity Agents identify savings, leakage, and anomalies

07

Act

Opportunities ranked, packaged, assigned to initiatives

Enterprise-safe by design

Procurement data handled with enterprise-grade controls

Built for environments where privacy, access control, and AI governance are non-negotiable and need regional hosting, SSO, audit trails, and human review as standard.

See full security & governance detail
ISO/IEC 27001 certified by BSI GDPR compliant SSO & RBAC Regional hosting Google Cloud Partner Immutable audit trail
In practice SPAR
"Once you get your data right, the world's your oyster you can layer insights on top of that."
Kate HandsKate HandsGroup Procurement Executive, SPAR Group
Read the full case study
FAQ

Product questions, answered.

No. Mithra is a data intelligence layer that sits alongside your existing systems, it connects to them, cleans and governs the data they produce, and gives you better insights without replacing them.
Most customers have clean, classified procurement data and their first opportunity report within four to six weeks. A proof-of-value engagement using a sample data extract takes less than two weeks.
Mithra connects to SAP, Oracle, Ariba, Coupa, Ivalua, cloud data warehouses (BigQuery, Snowflake, Azure), Looker Studio, and any system that produces structured data exports including CSV, Excel, and flat files.
Mithra supports regional data hosting, SSO, role-based access control, audit trails, and AI explainability controls. See the Security & Governance page for full detail.
Atlas can work with your existing taxonomy, optimize it, or build a new one alongside it. We support UNSPSC, custom taxonomies, and hybrid structures. Taxonomy changes go through your review and approval workflow.
Yes. A proof-of-value engagement often starts with a representative data sample, a single category, business unit, or entity, to demonstrate accuracy and identify early opportunities before a full deployment.
All of them. Atlas understands and classifies spend data in any language, so mixed-language supplier names, descriptions, and free-text line items are handled natively across regions. You can ask questions and read outputs in your own language too.

See the product on your own data.

Share a sample extract and we'll show you classified spend, normalized suppliers, and your first ranked opportunities in one session.

Request a demo Explore Atlas