Parsing merchant statements. Catching fraud patterns. Mining settlement files. Recovering rejected transactions. We build AI that turns hours of analyst work into minutes — and usually pays for itself in months.
Identify pricing anomalies → classify rejection reason → compare merchant trend → rank opportunity → route exception to analyst.
The work starts with the operational problem, not the model. We build the data pipeline, choose the right model, wire the workflow into production and give your team the dashboards, alerts and guardrails it needs.
Workflow automation, document parsing, decision systems and production deployment built end-to-end.
OpenAI, Anthropic, Gemini or open-source — selected for cost, latency, accuracy and privacy, then tuned where it matters.
Dynamic prompts, retrieval, guardrails, evaluation loops and observability that hold up outside the demo.
Retrieval over your knowledge, transaction history and processor docs, plus multi-step agents with tool use and human checkpoints.
These are not generic chatbot demos. They're payment workflows where an analyst is reading, classifying, comparing or routing thousands of records by hand.
Extract structured fields from inconsistent merchant statements, build readable reports and flag anomalies automatically.
Find chargeback spikes, velocity outliers, strange MCC activity and the few rows that actually need human attention.
Train on historical disputes and chargebacks, then score incoming volume continuously for patterns a human may not see for weeks.
Surface fast-growing merchants, pricing mismatches, churn signals and upsell opportunities from settlement files every morning.
Classify failed transactions, identify which ones can be retried and route eligible volume back into the pipeline.
Resolve repetitive merchant support questions automatically and escalate only the exceptions that require a person.
Merchant applications, processor contracts, KYB and ID documents get a structured first pass before human review.
Parse production payment logs, classify events and surface performance, security and error patterns before they become incidents.
A payments analyst, fraud reviewer or support specialist can cost north of $80,000 annually once fully loaded. A well-scoped AI workflow is usually a one-time build that keeps running — often paying itself back before the next quarter closes.
Frontier models for reasoning, smaller models for speed, fine-tuned models for precision and offline models when privacy matters. The architecture follows the workload.
GPT models and fine-tuned variants.
Long-context reasoning and analysis.
Multimodal and high-volume workloads.
Llama, Mistral, Qwen and private deployments.
Custom training on proprietary data.
Retrieval across docs, history and APIs.
Multi-step workflows with tools and checkpoints.
Drop AI into the systems you already run.
Tell us about the workflow eating your team's time. We'll come back with a scoped build, a fixed price, and an honest opinion on whether AI is actually the right tool.
Tell us about your project — we route you to the right specialist.