Yetti is SaaSquach's proprietary AI — purpose-trained for the four domains where ERPs stop short and decisions get expensive.
Request Early AccessGeneral-purpose LLMs can read your data. Yetti was trained to think through it — the way your most experienced operator would.
Yetti interprets inventory as a signal system — not a ledger. It reads demand patterns, aging curves, supplier lead times, and LOA commitments to surface risk and opportunity before they become write-downs. It speaks excess, obsolescence, and turns fluently.
Most ERPs store contracts. Yetti reads them. It extracts rebate tiers, volume commitments, exclusivity clauses, and penalty structures — then monitors live transactional data against those terms in real time. Missed rebate windows and auto-renewal traps get flagged before they cost you.
Pricing agreements, service level commitments, and program terms buried in PDFs become live operating parameters. Yetti maps customer entitlements to actual fulfilment, flags SLA drift, and identifies agreements that are bleeding margin without anyone noticing.
From SPIFF structures to GP-based commission splits and carried interest waterfalls, Yetti ingests compensation plans and maps them to deal and transaction data — eliminating month-end disputes and surfacing earnings in real time for reps, ops, and finance simultaneously.
How we build a model that thinks like your best operator — without touching your data.
The base model is grounded in enterprise operations vocabulary — ERP schemas, procurement taxonomy, supply chain ontology, and financial agreement structures. This creates a foundation that understands context, not just tokens.
Purpose-built synthetic datasets simulate the edge cases ERPs can't handle: multi-tier rebate calculations at volume thresholds, competing LOA priorities, contract term conflicts, and clawback triggers. No real customer data required at this stage.
Rather than fine-tuning on raw customer documents, Yetti uses a retrieval layer to inject per-tenant context at inference time — keeping proprietary data isolated and model weights clean. Every customer gets their own semantic layer without cross-contamination.
Exception resolution data — when a human overrides a recommendation — flows back as a learning signal. Over time, Yetti learns the judgment layer that separates a good recommendation from the right one for a specific business context.
Outputs are gated through a compliance filter trained on SOX, MNPI, and audit trail requirements — ensuring every recommendation is explainable, traceable, and defensible to finance and legal teams.
Yetti improves with every enterprise deployment. Anonymized aggregate patterns — not raw data — continuously sharpen the model's understanding of what "right" looks like across industries, ERP configurations, and business sizes.
The difference between a model that can read your data and one that was built to reason through it.
Early access is open to enterprise teams running Oracle Fusion, Blue Yonder SCPO, or Epicor Eclipse.