Ingest approved data
ERP, AP, purchase-order, contract, rate-card, and supplier-master exports remain in the approved environment.
No system replacementAI-assisted spend intelligence
Turn messy spend data intoan actionable savings pipeline.
SpendLens securely cleans, classifies, and analyzes ERP and AP exports—then gives procurement a prioritized list of savings opportunities with evidence, owners, and finance-ready ranges.
01 Secure data environment02 Explainable findings03 Finance-validated pipeline
Enterprise spend scanSynthetic data
CSV
Source file processedAP_EXPORT_FY26.CSV15,842 transactions · 34 columns · controls passed
✓Spend analyzed$186.4M
Raw suppliers779
Opportunity$11.8M
01
Normalize suppliers612
✓02
Classify spend96%
✓03
Match contracts73%
✓04
Score opportunities12
✓InputExisting ERP or AP export
AnalysisAI + sourcing rules
OutputValidated savings pipeline
No rip and replaceWorks with current systems
A practical AI implementation
From export to action—not another dashboard.
SpendLens is a configurable intelligence workflow around the client's existing financial and procurement data. AI handles repetitive normalization and pattern detection. Sourcing and finance validate what becomes a committed initiative.
Create one spend view
Normalize supplier names, classify transactions, identify duplicates, and map available contracts.
167 names resolvedDetect opportunities
Apply sourcing rules to fragmentation, price variance, demand, compliance, terms, and contract leakage.
12 findings scoredValidate and assign
Review evidence, separate savings types, assign owners, and publish an auditable delivery pipeline.
Human approval requiredInteractive demonstration
Explore the savings opportunity scan
Filter the synthetic portfolio, inspect the evidence behind each finding, adjust confidence, and build an owner-assigned pipeline.
Analysis complete · human review open
01 Spend profile
Category spend and opportunity
02 Data health
Ready enough to act—with controls
Supplier normalization779 raw → 612 suppliers
93%Category classification15,208 of 15,842 mapped
96%Contract matching$136.1M matched
73%Purchase-order coverage2,941 exceptions
81%!
Three review items634 transactions need category confirmation; 42 suppliers lack payment terms; two contract extracts are incomplete.
03 Supplier normalization
One supplier view from inconsistent records
AI proposes entity matches using names, addresses, tax identifiers, payment accounts, contracts, and reviewer rules.
Raw supplier records
→Asterix Cloud Inc.
ASTERIX CLOUD USA
AsterixCloud #204
Normalized entityAsterix Cloud Systems
98% match$18.4M combined
Raw supplier records
→Ironwood Talent LLC
IRONWOOD STAFFING
Ironwood Tech Svcs
Normalized entityIronwood Talent Services
96% match$11.7M combined
Raw supplier records
→BrightField Media
BFM Collective
Bright Field Production
Normalized entityBrightField Media Collective
91% match$8.2M combined
04 Opportunity detection
Prioritized savings findings
Select a finding to review its evidence, assumptions, controls, and recommended next step.
OpportunityExpectedConfidenceEffortPipeline
05 Price variance
Like-for-like rates that merit challenge
ItemCurrent rangeModeled targetVarianceEvidence
Senior cloud engineer$165–$218 / hr$178 / hr22%High
CRM enterprise license$92–$124 / user$98 / user21%High
HVAC technician$132–$181 / hr$145 / hr20%Medium
Creative director$285–$410 / hr$325 / hr21%Medium
Consulting senior manager$395–$560 / hr$430 / hr23%High
06 Scenario controls
Test the evidence-strength assumption
Adjust comparability and data confidence to see how the expected pipeline changes before finance validation.
DirectionalValidated
Low case$4.4M
Expected$6.4M
High case$8.9M
Savings types remain separated: P&L, cost avoidance, demand reduction, and working-capital value are not combined without finance approval.
07 Savings pipeline
Human reviewExpected pipeline$6.4M
4 initiatives · 4 categories
Consolidate overlapping software$1.8M expected
Reset IT staffing rate cards$2.2M expected
Aggregate regional facilities spend$1.5M expected
Normalize agency and media fees$1.2M expected
Complete decision traceRaw records, normalization decisions, classifications, evidence, assumptions, owners, and approvals remain auditable.
An outcome clients can purchase
The 15-day AI savings pipeline sprint
I configure a secure analysis around the client's existing spend exports, validate the findings with procurement and finance, and leave behind a reusable workflow—not a disconnected report or another enterprise software subscription.
Normalized supplier masterAI-classified spend cubePrioritized opportunity registerLow, expected, and high casesInitiative owners and next stepsSecure workflow and controls
Best fit
Lean procurement teams with fragmented spend data, an upcoming savings target, or existing tools that are not producing an actionable pipeline.
Fixed-scope diagnostic15 business days
- Days 1–3
Define and secureConfirm sources, controls, savings definitions, and success measures.
- Days 4–8
Normalize and classifyCreate the supplier and category view from existing exports.
- Days 9–12
Detect and validateReview opportunities with category owners and finance.
- Days 13–15
Assign and activateDeliver the approved pipeline, workflow, and team training.
One connected sourcing story
From opportunity discovery to supplier decision.
SpendLens
Find and prioritize savings opportunities
Current demoMarketLens
Find suppliers and benchmark evidence
Market intelligenceSourceLens
Evaluate proposals and select suppliers
RFP decisionRenewalLens
Optimize software renewals
Commercial actionAI implementation for procurement
Find the value already hiding in your spend data.
Start with one secure export and finish with an evidence-backed pipeline your sourcing team can act on.