AI Agent Suite
A multi-agent intelligence system deployed across procurement, inventory, and customer operations — each module running its own crew of specialized agents, connected to your existing systems, and maintained by Incentius.
Procurement teams default to familiar suppliers because cross-referencing 12 months of delivery history, price trends, and quality data before an RFQ deadline doesn't happen manually.
Markdown decisions get made without a break-even projection or a route coverage check. The financial exposure of a near-expiry batch is visible only after the write-off, not before the discount.
Churn signals — basket attrition, invoice frequency drops, credit stress — sit scattered across transaction logs. By the time they surface in a report, the customer has already shifted.
Each module runs its own multi-agent crew — specialized agents in sequence, each building on the last.
Deterministic
3-component formula ranks vendors by reliability, PO depth, and pricing — top 8 shortlisted with a single AI justification before any RFQ is sent.
Shortlist Ready
3-Agent Crew
Quote Analyst → Vendor Evaluator → Procurement Advisor: each agent builds on the last, producing a ranked recommendation with data-backed negotiation points.
Recommendation Ready
On Demand
12-month pricing trend, seasonal reliability profile, product competitiveness rank, and AI procurement narrative — built on demand for any vendor in your portfolio.
Dossier Built
Weekly
A–F grade per vendor from 90-day return and rejection data; AI quality summary generated for underperformers (C, D, F grades) only.
Grades Published
Each module is a sequential agent pipeline — data in, structured decision out, every step traceable.
RFQ Created
Vendor History DB
3-Agent Quote Crew
Agent 1
Benchmarks line items vs 12-mo avg; flags delivery risk below 85%
Benchmark Report
Agent 2
100-pt: Price (40) + Lead Time (30) + Reliability (30)
Ranked Scores
Agent 3
Ranked recommendation + backup + negotiation points
Advisory Report
Ranked Recommendation
Negotiation Points
Inputs
Agent Pipeline — 3-Agent Quote Crew
Output
RFQ Created
Vendor History DB
Agent 1
Benchmarks line items vs 12-mo avg; flags delivery risk below 85%
Benchmark Report
Agent 2
100-pt: Price (40) + Lead Time (30) + Reliability (30)
Ranked Scores
Agent 3
Ranked recommendation + backup + negotiation points
Advisory Report
Triggered on RFQ creation · Celery task · Pre-scored vendor shortlist injected as context
Ranked Recommendation
Negotiation Points
A glimpse at the actual outputs — what each module surfaces for your team to act on.
RFQ-2204 — PR for Coconut Soap + Dishwash Liquid
4 quotations · Due 11 Jul 2026 · 1 quotation approved
A quotation has been approved — this RFQ is locked. No further analysis or revisions.
Supplier 1
Rev 7✓ Best reliability record
Total
$16,741
Unit
$9.10
Lead
8d
Reliability
107%
Supplier 2
Rev 10▲ Highest cost
▲ Longest lead
Total
$39,018
Unit
$21.22
Lead
24d
Reliability
61%
Supplier 3
Rev 7✓ Lowest price
✓ Fastest delivery
Total
$15,893
Unit
$8.64
Lead
4d
Reliability
61%
Supplier 4
Rev 7▲ Long lead time
Total
$29,944
Unit
$16.28
Lead
19d
Reliability
99%
Supplier 1
Rev 7✓ Best reliability record
Soap 250g × 100 · Dishwash 1L × 200 · Net 60
$16,741
$9.10
8d
Price
32.9
Lead
24.5
Rel
38.0
Supplier 2
Rev 10▲ Highest cost
▲ Longest lead
Soap 250g × 100 · Dishwash 1L × 200 · Net 60
$39,018
$21.22
24d
Price
8.8
Lead
1.4
Rel
6.7
Supplier 3
Rev 7✓ Lowest price
✓ Fastest delivery
Soap 250g × 100 · Dishwash 1L × 200 · Net 60
$15,893
$8.64
4d
Price
40.0
Lead
29.8
Rel
18.4
Supplier 4
Rev 7▲ Long lead time
Soap 250g × 100 · Dishwash 1L × 200 · Net 60
$29,944
$16.28
19d
Price
21.1
Lead
8.6
Rel
29.4
AI Recommendation
Recommended Vendor
Supplier 1
107% fulfillment rate ensures operational continuity. Choosing Pacific Supplies' lower price would be a false economy.
Decision Rationale
High fulfillment rate eliminates hidden costs of supply disruption. Pacific Supplies' poor reliability history poses a direct operational risk despite lower headline cost.
Negotiation Points
A reliable, inspectable stack — every output traceable to the data that generated it.
Scores, financial exposure, break-even percentages, and trajectory forecasts are computed deterministically before any LLM call. AI handles synthesis and classification only.
All required data is loaded in Python and injected as plain text before each crew runs. No tool calls during agent reasoning — eliminates ~60% of LLM API calls per run.
Three independent multi-agent crews run in sequence. Each agent's output becomes input context for the next — no agent can skip the preceding analysis.
Each crew run is an independent Celery task — rate-limited, concurrency-capped, with automatic retry on failure. No analytical jobs dropped under load.
Approved interventions create outcome records evaluated 4 weeks later. Recovery probability estimates become grounded in real company data — the system improves without code changes.
Retention crew outputs auto-create promotions entering the 4-tier council flow. Credit risk signals directly constrain intervention architect recommendations in the same run.
Procurement
Vendor selection cycle reduced — 3-agent crew produces a ranked recommendation with sub-scores in under 3 minutes
Data-backed negotiation points citing exact historical paid rates — not gut-feel asks
Delivery risk vendors flagged before they cause a supply disruption — 85% fulfillment threshold enforced on every RFQ
Promotions
Revenue recovered from near-expiry batches with a specific discount recommendation per batch before you commit
Recovery vs. write-off projection before any markdown is committed — the decision is made with numbers
4-tier council catches financial and strategic risks before activation; auto-rejects when break-even is mathematically impossible
Customer Operations
Churn risk identified 4–6 weeks before revenue drops — basket composition signals trigger earlier than any revenue-based metric
Salesman briefing auto-injected into route planning before the delivery visit — specific to what happened and what to do
Cascade alerts distinguish individual churn from competitive events — the right response is a strategic decision, not individual visits
Incentius deploys, configures, and maintains the full stack. You don't run the crews, update the models, or manage the infrastructure.
No rip-and-replace. The agents connect to your existing ERP, WMS, and CRM using data connectors mapped to your specific table structure during onboarding.
Thresholds, signal weights, and recovery priors are configurable to your business. The system calibrates against your actual outcomes over time.
The AI council produces structured briefs and ranked recommendations. Humans approve vendor selections, activate promotions, and confirm interventions — the system makes those decisions faster, not autonomously.
Get Started
The Retail Intelligence Suite connects to your existing systems, runs on a schedule, and produces decisions your team can act on the same day.
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