How a multi-agent AI system cut spend-classification cost by about 92%
The client
Who they are
A procurement-analytics organization whose service depends on accurately classified spend data at high volume. Anonymized under our confidentiality policy.
What they do
They turn raw procurement transactions into categorized, decision-ready spend intelligence, vendor by vendor and line by line, for demanding enterprise customers.
Who they serve
Procurement and finance leaders who rely on clean category data for sourcing strategy, negotiation, and savings tracking.
The problem
Every record needed human judgment. Vendor names arrived in dozens of inconsistent formats. Taxonomy mapping required category expertise. Ambiguous transactions demanded research. The work was accurate but brutally expensive, hard to staff, and impossible to scale without scaling headcount with it. Quality depended on individual analysts, and every volume spike became a hiring problem.
Manual classification: accurate, expensive, and linear with headcount.
~$5M/yr
annual analyst-hour cost before automation (verified, anonymized)
Every record
required manual vendor normalization, mapping, or research
Spend classification ran on expensive analyst hours. Roughly $5,000,000 a year of them.
The solution
Decompose the workflow into specialized agents. Instead of one general classifier, Avannte built a pipeline where each agent owns one competency: vendor cleanup and normalization, taxonomy and category mapping, ambiguity routing, and QA review.
Score confidence at every step. Each classification carries a confidence score. High-confidence records flow through; low-confidence records route automatically to a human review queue. Nothing ambiguous reaches the output unreviewed.
Keep humans on judgment, not volume. Analysts moved from classifying everything to adjudicating the hard minority, with the agents' evidence and reasoning attached to every queued record.
Log everything for audit. Every classification, route, and human decision is recorded, making the output defensible to the client's own enterprise customers.
The impact
~92%
lower analyst-hour cost
Annual analyst-hour cost fell from about $5,000,000 to about $400,000. Verified and anonymized.
~$4.6M
saved per year
Recurring annual savings, not a one-time efficiency bump.
Quality held
review controls intact
Ambiguous records still get human judgment. The system scales volume without giving up the review discipline the business was built on.
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