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Real case · Supply_Ops in production

Pilot client: R$ 86 million in modeled cost reduction

A platform in production across the purchasing and contract cycle of a large-scale industrial operation — six operational capabilities and a mesh of sixteen specialized AI agents.

R$ 86M
Savings / year (model)
16 · 6
Agents · capabilities
87%
Recommendation acceptance
−85% to −92%
Document errors

A projection built on the metrics observed in the pilot and illustrative spend bases — figures to be validated against the client's audited data. Levers treated as independent, with no double counting; it does not consider the platform investment.

The R$ 86-million model

Six value levers, one compound result

AI-driven competitive selectionComparisons · −2.8% on R$ 1.2B of spend under quotation
R$ 34.0M
Predictive purchasing (ML)ML Purchasing · −4.0% on R$ 550M in recurring items
R$ 22.0M
Productivity and capacityCross-cutting · ~22 thousand hours/year freed for strategic sourcing
R$ 12.0M
Errors and document riskDraft review · −3.0% on R$ 300M of contract base
R$ 9.0M
Inventory and working capitalML Purchasing + Qualification · 12% p.a. on R$ 50M of reduced inventory
R$ 6.0M
Compliance and riskQualification + Governance · −5.0% on R$ 60M of exposure
R$ 3.0M

SENSITIVITY: conservative R$ 60.2M · base R$ 86.0M · optimistic R$ 103.2M

End-to-end process

From demand forecasting to signed contract, a governed flow

01

ML forecast

Demand anticipated with machine learning

02

PR creation

Data-guided purchase requisition

03

Proposals

Suppliers respond to the quotes

04

AI comparison

Objective scoring per criterion, 0 to 10

05

Recommendation

Best choice, with justification and savings

06

Draft & Review

Clauses flagged by criticality

07

Qualification

Partner validated and documented

08

Signature & Filing

Digital, with a complete audit trail

The agent mesh

Sixteen specialized agents, three fronts of action

PURCHASING · 6 AGENTS

Proposal comparatorDemand forecaster (ML)PR creationStage follow-upSeasonality analysisNegotiation potential

LEGAL · 5 AGENTS

Draft reviewerClause validatorDocument sign-offSupplier help deskMeeting assistant

GOVERNANCE & DATA · 5 AGENTS

Partner qualificationDocument validationSmart filingAutomated auditSystems integrator
Supply_Ops on screen

The platform in production, module by module

Single command of the cycle

FEATURES

  • KPIs for comparisons, forecasts and acceptance rate
  • Estimated savings accumulated in the fiscal year
  • Real-time health of the agent mesh

GAINS

  • End-to-end visibility on a single screen
  • Decisions based on data, not guesswork
  • Proactive management by status and alerts
1 screen
Single view of the cycle
100%
Agents online
app.gemops.digital
Single command of the cycle

Platform screen · illustrative data

Proposals compared by AI

FEATURES

  • Automatic reading of supplier proposals
  • 0–10 scoring per criterion and weighted score
  • Recommendation with justification and CSV report

GAINS

  • Objective, fast and auditable decision
  • Optimal choice with proven savings
  • Standardizes supplier selection
Minutes
Of analysis, not hours
R$ 148k
Savings in one event
app.gemops.digital
Proposals compared by AI

Platform screen · illustrative data

Predictive purchasing via ML

FEATURES

  • Recurring forecasts scheduled per item
  • Model confidence and drivers displayed
  • Recommendation: buy now or wait

GAINS

  • Brings the purchase forward and avoids stockouts
  • Reduces excess inventory and idle capital
  • 80–90% less demand-analysis time
38
Forecasts per month
87%
Acceptance rate
app.gemops.digital
Predictive purchasing via ML

Platform screen · illustrative data

Partner qualification

FEATURES

  • Centralized documentation per supplier
  • Validation of certificates and expiry dates
  • Panel of qualified, in-analysis and pending suppliers

GAINS

  • 100% of documentation centralized
  • Compliance and risk reduction
  • Only fit suppliers advance
118/142
Partners qualified
100%
Digital filing
app.gemops.digital
Partner qualification

Platform screen · illustrative data

Before × After

What changes when agents take over the operational work

BeforeManual, reactive operation
  • DATASilos across ERP, legal and spreadsheets — no single source of truth
  • COMPARISONManual, hours of work per event, subject to subjectivity
  • DATA ENTRYRequisitions typed in, with costly errors that are hard to audit
  • STRATEGYReactive, spot purchasing and recurring stockouts
After · Supply_OpsAgent mesh in production
  • DATAAPI-integrated systems — a single datum, 100% digital filing
  • COMPARISONAI compares in minutes, with auditable scoring and justification
  • DATA ENTRYData-guided PR creation: −92% of errors
  • STRATEGYML predictive purchasing, with 87% acceptance
Deployment

How it enters your operation — without stopping what works

01

Integration

Connection to current systems via API, without replacement.

02

Configuration

Activation of agents and criteria per area, via catalog.

03

Pilot

Operation on a real process, proving the ROI in weeks.

04

Scale

Expansion to new areas and units by configuration.

Receive the savings model calibrated with your numbers

We validate the real financial baselines of your operation and finalize the savings model with audited data.

Book an executive demonstration