Decline diagnosis
Assemble transaction, routing, and processor context into a reviewable explanation.
Resolve merchant, transaction, reconciliation, and dispute work faster—with every handoff visible.
System liveProduction path
What entersTransaction, merchant, or dispute
AI workInvestigate, reconcile, and assist
Control planeEvalsAccessLogs
What leavesA resolved operationOwned by your team
What we build
Merchant, transaction, reconciliation, and dispute systems.
Production AI for payment gateways, PSPs, aggregators, and fintech teams across merchant operations, reconciliation, disputes, analytics, and support.
Merchant onboarding
Extract and verify onboarding data from documents, forms, and connected systems.
Reconciliation
Match transactions, settlements, and payouts, then route unresolved exceptions.
Disputes and chargebacks
Triage cases and assemble supporting evidence from approved sources.
Merchant support
Ground operational answers in merchant, transaction, and scheme context.
Payments analytics
Ask approval, settlement, dispute, and operations questions through validated queries.
Case studies
Production systems, with measurable outcomes.
Document intelligence, governed analytics, and real-time call understanding in production.
Engineering scope
Model capability is only one part of the system.
01
Signal
Connect the evidence around a payment event.
Bring transaction, processor, merchant, device, and operational context into one controlled workflow.
02
Investigate
Shorten exception handling.
AI assembles evidence and likely causes; people retain the decision and escalation path.
03
Operate
Automate repeated payment-operations steps.
Bounded agents handle lookups, matching, drafting, and routing with replayable logs.
04
Measure
Evaluate against the outcomes that matter.
Track resolution quality, false positives, handling time, cost, and exception rates.
Delivery model
Make the production decision early.
Define the workflow
Map the users, systems, data, controls, and measurable operating result.
OutputA focused production plan
Validate with real examples
Test representative inputs against quality, latency, cost, privacy, and review requirements.
OutputMeasured scope and baseline
Engineer the system
Build integrations, evaluations, interfaces, permissions, monitoring, and recovery paths.
OutputProduction-ready software
Launch and hand over
Launch with real users, improve from production feedback, and hand over code, runbooks, and baselines.
OutputAn operated system your team owns
Production controls
Built to evolve as models change.
Models can change without changing the operating contract. Permissions, evaluations, observability, and ownership stay explicit.
01
Cardholder and merchant data protected by explicit boundaries and access
02
No autonomous money movement or consequential decision without designed authority
03
Inputs, tool calls, model versions, outputs, and reviewer actions logged
04
Validated metrics and query plans for operational analytics
05
Cost, latency, and fallback behavior visible under production load
06
Provider-agnostic architecture where model flexibility matters
Buyer questions
Before you start.
Do you build fraud models?+
We can build or integrate signal and investigation systems, but we do not present a generic LLM as an autonomous fraud decision-maker. The design depends on available labels, existing rules and models, latency, explainability, and review requirements.
Can AI work with our payment and merchant systems?+
Yes. We integrate through approved APIs, data stores, queues, and identity boundaries, with tool permissions constrained to the workflow.
How do you choose the right model and architecture?+
We test the workflow against your quality, latency, cost, privacy, and operational constraints. Model choice follows the evidence; the architecture stays replaceable where practical.
Can you work inside our existing cloud and engineering stack?+
Yes. We design around your APIs, identity model, data boundary, observability, release process, and ownership requirements rather than forcing a separate platform.
Who owns the finished system?+
Your team does. We deliver the code, evaluation baselines, monitoring, runbooks, and transfer needed for your engineers to operate and extend it.
What is a sensible first engagement?+
One valuable workflow with real samples, an accountable owner, and measurable success criteria. We prove the path before expanding the scope.
Start with one system
Bring the workflow, constraints, and real examples.
We will help determine the smallest production slice worth proving, what must be measured, and where human control belongs.