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Promotions. Customer Revenue. 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. [Request a Demo](/contact/) ## Three Gaps ### Vendor selection on habit 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. ### Promotions on gut feel 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. ### Customer risk on stale data 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. ## The Agent Crews Each module runs its own multi-agent crew — specialized agents in sequence, each building on the last. Vendor Intelligence Promotion Intelligence Customer Intelligence 01 Deterministic ### Pre-RFQ Vendor Scoring 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 02 3-Agent Crew ### Quote Analysis Crew Quote Analyst → Vendor Evaluator → Procurement Advisor: each agent builds on the last, producing a ranked recommendation with data-backed negotiation points. Recommendation Ready 03 On Demand ### Vendor Intelligence Dossier 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 04 Weekly ### Vendor Quality Watch 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 01 Weekly Scan ### Near-Expiry Detection Finds batches expiring within 7 days, computes financial exposure per batch, assigns CRITICAL / HIGH / MEDIUM urgency, and maps top buyers per SKU. Batches Detected 02 On Demand ### Context-Aware Advisor Free-text business context triggers AI cross-referencing of live inventory — all product names, SKUs, and quantities sourced from your database only. Advisor Ready 03 4-Tier Council ### AI Defense Council Four sequential agents evaluate market fit, revenue impact, financial risk, and strategic alignment — auto-rejecting any promotion that can't break even. Council Briefed 04 State Machine ### Approval → Activation Draft → Pending Approval → Approved → Active, with an immutable audit trail at every transition; retention-agent promotions enter the same flow automatically. Active 01 Weekly Scan ### Health Scoring Engine 10-signal deterministic score (0–100) per customer with revenue-at-risk computed before any LLM call; classified as CRITICAL, AT\_RISK, STABLE, or GROWING. Portfolio Scored 02 4-Agent Crew ### Retention Crew Signal Analyst → Root Cause Analyst → Intervention Architect → Dispatch & Forecaster: each agent's output feeds the next in a sequential crew. Analysis Complete 03 Parallel Weekly ### Supporting Intelligence Credit risk scoring, basket churn detection (4–6 weeks early signal), demand sensing, and salesman coaching run in parallel each week. Intelligence Updated 04 Cascade Crew ### Churn Cascade Detection Post-retention scan identifies clusters of 3+ declining customers, classifies the pattern type, and produces an executive-level strategic recommendation. Cascades Detected ## How It Flows Each module is a sequential agent pipeline — data in, structured decision out, every step traceable. Vendor Promotion Customer RFQ Created Vendor History DB 3-Agent Quote Crew Agent 1 #### Quote Analyst Benchmarks line items vs 12-mo avg; flags delivery risk below 85% Benchmark Report Agent 2 #### Vendor Evaluator 100-pt: Price (40) + Lead Time (30) + Reliability (30) Ranked Scores Agent 3 #### Procurement Advisor 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 #### Quote Analyst Benchmarks line items vs 12-mo avg; flags delivery risk below 85% Benchmark Report Agent 2 #### Vendor Evaluator 100-pt: Price (40) + Lead Time (30) + Reliability (30) Ranked Scores Agent 3 #### Procurement Advisor Ranked recommendation + backup + negotiation points Advisory Report Triggered on RFQ creation · Celery task · Pre-scored vendor shortlist injected as context Ranked Recommendation Negotiation Points Expiry Scan Context Input 4-Tier Defense Council L1 Agent #### Market Intel Product fit, timing, demand depth Market Brief L2 Agent #### Revenue Impact Sell-through %, route coverage Revenue Brief L3 Agent #### Financial Risk Break-even; auto-rejects if impossible Risk Assessment L4 Agent #### Strategic Align Synthesizes council vote → executive brief Council Vote Promotion Brief Human Approval Inputs Agent Pipeline — 4-Tier Defense Council Output Expiry Scan Context Input L1 Agent #### Market Intel Product fit, timing, demand depth Market Brief L2 Agent #### Revenue Impact Sell-through %, route coverage Revenue Brief L3 Agent #### Financial Risk Break-even; auto-rejects if impossible Risk Assessment L4 Agent #### Strategic Align Synthesizes council vote → executive brief Council Vote Weekly expiry scan + on-demand context advisor · State machine: Draft → Approved → Active Promotion Brief Human Approval Weekly Health Scores Transaction History 4-Agent Retention Crew Agent 1 #### Signal Analyst GENUINE RISK vs. seasonal / trend Risk Class. Agent 2 #### Root Cause Primary churn driver: attrition / service / credit Cause Report Agent 3 #### Intervention Arch. 2 options + live inventory check 2 Options Agent 4 #### Dispatch & Forecast Brief + 2W/4W trajectory + recovery % Briefing Ready Salesman Brief Cascade Alert Inputs Agent Pipeline — 4-Agent Retention Crew Output Weekly Health Scores Transaction History Agent 1 #### Signal Analyst GENUINE RISK vs. seasonal / trend Risk Class. Agent 2 #### Root Cause Primary churn driver: attrition / service / credit Cause Report Agent 3 #### Intervention Arch. 2 options + live inventory check 2 Options Agent 4 #### Dispatch & Forecast Brief + 2W/4W trajectory + recovery % Briefing Ready Weekly scheduled run · Cascade Crew runs after all retention crews complete to detect portfolio-level patterns Salesman Brief Cascade Alert ## The Suite in Action A glimpse at the actual outputs — what each module surfaces for your team to act on. Vendor Intelligence Promotion Intelligence Customer Intelligence RFQ-2204 — PR for Coconut Soap + Dishwash Liquid 4 quotations · Due 11 Jul 2026 · 1 quotation approved Re-analyze A quotation has been approved — this RFQ is locked. No further analysis or revisions. Supplier 1 Rev 7 ✓ Best reliability record ✓ Approved Total $16,741 Unit $9.10 Lead 8d Reliability 107% Supplier 2 Rev 10 ▲ Highest cost ▲ Longest lead Approve Total $39,018 Unit $21.22 Lead 24d Reliability 61% Supplier 3 Rev 7 ✓ Lowest price ✓ Fastest delivery Approve Total $15,893 Unit $8.64 Lead 4d Reliability 61% Supplier 4 Rev 7 ▲ Long lead time Approve Total $29,944 Unit $16.28 Lead 19d Reliability 99% Vendor Total Unit Price Lead Reliability Sub-Scores Action Supplier 1 Rev 7 ✓ Best reliability record Soap 250g × 100 · Dishwash 1L × 200 · Net 60 $16,741 $9.10 8d 107% Price 32.9 Lead 24.5 Rel 38.0 ✓ Approved Supplier 2 Rev 10 ▲ Highest cost ▲ Longest lead Soap 250g × 100 · Dishwash 1L × 200 · Net 60 $39,018 $21.22 24d 61% Price 8.8 Lead 1.4 Rel 6.7 Approve Supplier 3 Rev 7 ✓ Lowest price ✓ Fastest delivery Soap 250g × 100 · Dishwash 1L × 200 · Net 60 $15,893 $8.64 4d 61% Price 40.0 Lead 29.8 Rel 18.4 Approve Supplier 4 Rev 7 ▲ Long lead time Soap 250g × 100 · Dishwash 1L × 200 · Net 60 $29,944 $16.28 19d 99% Price 21.1 Lead 8.6 Rel 29.4 Approve 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 * ·Request lead time reduction 8d → 6d, citing urgency and competitive standing. * ·Align pricing to 12-month benchmark average for further cost optimization. Coconut Soap 250g BP80708 · SC-LOW-01-BP80708 · Near-Expiry Batch MEDIUM ✕ Days Left 7 Discount 30% Qty Available 4 Revenue Recovery $560.00 Write-off Risk $800.00 Expiry date: **2026-06-29** AI-Targeted Customers (5) Customer A Customer B Customer C Customer D Customer E Close \+ Create & Submit AI Urgency Signal Exclusive Flash Offer: Secure 4 units of Coconut Soap 250g at a 30% discount for immediate delivery. Valid for the next 24 hours only to ensure freshness before expiry. AI AI Justification With 7 days remaining, the risk of total write-off is high. A 30% discount is calibrated to incentivize bulk procurement from top buyers while still recovering 70% of the $800 exposure. This trade-off prioritizes cash flow recovery over margin preservation, as the alternative is a total loss of the inventory value. Discount Calibration 70% recovered vs. 100% write-off at full price David M. At Risk 53.8 HEALTH Days Since 20d Avg Invoice $1,744 SKUs (12W) 4 Return Rate 18.6% Credit Risk 70/100 Overview AI Intelligence Action Plan 12-Week Health Trend 100 55 0 W1 W3 W6 W9 W12 Signal Profile Recency Credit Util Payment Credit Note Return Rate Repeat Rate New SKU SKU Breadth Avg Invoice Frequency Revenue Trajectory Now At Risk 53.8 2 Weeks At Risk 4 Weeks At Risk $2,616/mo At Risk/mo 60% Recovery Prob. 39d Days to Critical The current negative trend of -2.43 points per week indicates a high likelihood of the account reaching a critical state within 40 days. Without immediate intervention to resolve credit and supply issues, the customer is likely to remain at risk for the foreseeable future. AI Analysis 95% confidence Genuine Churn Risk Primary driver is service failure (product quality issues causing loss of trust); secondary driver is SKU attrition and operational constraints including high credit utilization and supply shortages. Credit Risk 70/100 Risk Score HIGH Risk Factors high\_utilisation (665%) revenue\_decline (-25.5%) Action: Reduce Limit · Suggest immediate credit limit review The customer presents a high credit risk due to a significant 25.5% revenue decline and extreme credit utilisation of 665%. We must immediately review the credit limit to mitigate exposure and protect against potential default given the unsustainable utilization ratio. Scored 2026-06-22 Intervention Plan Option 1 Conservative Schedule an immediate account management visit to address the trust deficit caused by previous service failures. Option 2 Recommended Initiate a formal supply-side escalation for low-stock SKU and perform a credit review to address the high utilization rate. Recommendation Option 2, as it addresses the operational bottlenecks (credit and supply) preventing the customer from ordering rather than offering ineffective discounts. Salesman Briefing PRIORITY: Customer #1. The account is at risk due to past service failures and current credit/supply constraints. Do not offer discounts. Focus on resolving credit utilization and escalating the supply-side stockout. Goal: Stabilize account credit and restore supply chain reliability to enable future ordering. Intervention ROI $2,616/mo Recovery Est. 60% Probability 4.6x Est. ROI ✓ Approve ✕ Reject ## Under the Hood A reliable, inspectable stack — every output traceable to the data that generated it. ### Hybrid Deterministic + AI Scores, financial exposure, break-even percentages, and trajectory forecasts are computed deterministically before any LLM call. AI handles synthesis and classification only. ### Pre-Fetched Context Pattern 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. ### CrewAI Sequential Crews 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. ### Celery + Redis Task Queue Each crew run is an independent Celery task — rate-limited, concurrency-capped, with automatic retry on failure. No analytical jobs dropped under load. ### Closed-Loop Outcome Learning Approved interventions create outcome records evaluated 4 weeks later. Recovery probability estimates become grounded in real company data — the system improves without code changes. ### Cross-Module Integration Retention crew outputs auto-create promotions entering the 4-tier council flow. Credit risk signals directly constrain intervention architect recommendations in the same run. ## Business Outcomes 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 ## Why Incentius ### Not a platform you manage Incentius deploys, configures, and maintains the full stack. You don't run the crews, update the models, or manage the infrastructure. ### Connectors mapped to your schema 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. ### Business rules, not global averages Thresholds, signal weights, and recovery priors are configurable to your business. The system calibrates against your actual outcomes over time. ### Human approvers at every decision 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 ## Three gaps. One system. Deployed for you. 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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