Transaction Intelligence
Sub-50ms semantic intelligence across every payment decision point, from authorization to dispute resolution, on infrastructure you control.
Four specialist models replace regex parsing, rule-based fraud detection, manual dispute triage, and cloud NLU routing. Each runs on a fraction of a single GPU, processes transactions within the authorization window, and adapts to new patterns via LEAP in minutes. Total deployment: hours of engineering, dollars of compute.
5 specialist models
How It Works
One specialist model per payment decision,
adapting to your transaction patterns in minutes
Semantic Parsing Where Regex Fails and Cloud APIs Are Too Slow
Raw merchant descriptors are cryptic strings that rule-based systems cannot reliably parse. Cloud enrichment APIs add network latency that breaks the authorization window. A specialist LFM parses merchant descriptors semantically in under 35ms, directly in the hot path with no cloud round-trip and no per-call cost. When new POS formats appear, LEAP fine-tunes the model same-day. One task, one model, sub-50ms: the economics of doing one thing perfectly.
Fraud Signals That Rule Engines Were Never Built to Detect
Rule engines maintain thousands of regex patterns. Enterprise fraud platforms add hundreds of milliseconds and six-figure annual costs. Both architectures miss novel attack vectors that require semantic understanding: gift card splitting, P2P velocity anomalies, synthetic identity indicators. A specialist LFM extracts these signals within the authorization budget. When a new fraud vector emerges, the model retrains in minutes via LEAP, not months via vendor release cycles. Your fraud detection evolves at the speed of the threat.
Structured Triage From Free-Text Complaints in Under 40ms
Dispute complaints are emotional, messy, and full of implicit context. Manual triage creates million-case backlogs. Cloud language models could parse them, but at 800ms+ latency with PII exposure. A specialist LFM reads the complaint and outputs structured triage JSON in under 40ms, on your infrastructure, with zero data leaving your perimeter. Deterministic output for auditable decisions. The backlog clears. Analysts focus on edge cases, not classification.
On-Device Intelligence That Eliminates Per-Query Cloud Costs
Millions of mobile banking queries arrive with typos, abbreviations, and colloquial phrasing. Keyword matching fails. Cloud NLU adds network latency and per-query costs that compound at scale. A specialist LFM quantized to approximately 150MB deploys directly on-device as a semantic router: typo-resilient, zero network latency, zero marginal cost. The model understands intent, not keywords. Scale to tens of millions of queries with no incremental infrastructure.
One Encoder, One Frozen Backbone, Four Predictions in 22ms
Specialist models per task are economical but they don't share representation. A second architecture pattern, already shipping in LFM2.5-Audio and LFM2.5-VL, applies to payments directly: a small encoder produces continuous embeddings from a 64-transaction history, a frozen LFM2.5-350M backbone with attention LoRA processes them as 960 pseudo-tokens, and four lightweight heads predict fraud probability, next merchant, amount bucket, and MCC in parallel. ~16M trainable parameters on top of a 350M frozen base. One forward pass produces all four signals. Adding a new objective is a head, not a new model.
Try each model
All Demos
Semantic Transaction Enrichment
Parse chaotic merchant descriptors into structured data within the 50ms auth window.
Semantic merchant parsing in <35ms. Fits inside the auth window where regex and keyword matching fail
Fraud Signal Extraction
Extract fraud indicators from transaction data
Semantic fraud detection within the 50ms payment-auth budget
Dispute & Chargeback Intelligence
Convert unstructured customer dispute complaints into structured triage data instantly.
Unstructured complaint → structured triage JSON in <40ms. Zero PII leaves your perimeter
Mobile Intent Router
Typo-resilient semantic routing for mobile banking app queries. On-device, zero cloud.
On-device semantic routing in <15ms. Typo-resilient, zero cloud API calls, instant UX
Transaction Foundation Model
One model that reads a 64-transaction history and returns four signals together: fraud probability, next merchant, amount range, and MCC enrichment. Alternate tabs run dispute triage, collections, and fraud-pattern attribution on the same model.
One encoder + frozen LFM2.5-350M + LoRA + four task heads in a single ~22ms forward pass on H100. A new SSL pretraining stage catches ~5× more fraud on newly onboarded merchants with almost no label history — shown on synthetic data.
Ready to deploy in your environment?