AI agents your auditors and operations teams can trust
We help banks, processors and fintechs design AI agents that reconcile, triage and resolve routine payment work, with human approval, clear permissions and a complete audit trail built in from day one.
What our clients are up against
Pilots that never reach production
Agent demos impress quickly, then stall when risk, compliance and security teams ask how actions are approved and recorded.
Accountability for automated actions
When an agent resolves a reconciliation break or closes an alert, you need to know who authorized it, why and on what evidence.
Fitting agents into existing controls
Agents must respect your entitlements, change management and model risk processes rather than work around them.
How we cover Agentic AI
Governance and oversight
Keep people in charge of the decisions that matter.
- Human-in-the-loop approval workflows for agent supervision
- Role-based permissions and action limits for every agent
- Complete audit trails of inputs, tool calls and decisions
- Agent evaluation, testing and performance monitoring
- Agent security, including defenses against prompt injection
Payment operations agents
Take routine, well-defined work off your operations teams.
- Autonomous agents for payment operations and reconciliation
- Self-healing systems and automated incident resolution
- AI-driven payment routing and optimization agents
- Cloud financial operations (FinOps) cost optimization with AI agents
Risk, compliance and service agents
Help investigators, compliance analysts and service teams work through queues.
- Agent-based fraud investigation and alert triage
- Regulatory compliance automation with documented evidence
- Conversational AI and chat agents for customer support
Agent architecture and knowledge
Build the foundations agents need to act on accurate, current information.
- Workflow automation with large language model (LLM) agents
- Multi-agent orchestration for complex payment workflows
- Retrieval-augmented generation (RAG) over payment documentation
- Knowledge graph integration for grounded decision-making
- Agent deployment through machine learning operations (MLOps) pipelines
Ways to work with us
Program advisory
Guidance on choosing first use cases, defining the governance model and aligning risk, compliance and technology teams before you build.
Platform design & delivery
Senior architects who design your agent platform, controls and integrations and work alongside your teams through go-live.
Team training
Hands-on programs, including LangChain and multi-agent orchestration, that get your engineers building governed agents.
Build your team's capability
Our Agentic AI training is now available, from first agents built with LangChain to multi-agent orchestration, delivered as a standalone program or tailored to your platform.
Request informationTraining modules
- Building Agentic AI for Fintech Automation
- LLM-Powered Agent Development with LangChain and LlamaIndex
- Multi-Agent Orchestration and Workflow Design
- MLOps Pipeline Design with Agent Deployment
- RAG Systems for Payment Documentation
- Agent Evaluation and Testing Best Practices
- AI Agent Security and Governance
Services, case studies and tools
How do you keep a person in control of agent actions?
We classify each action by risk. Read-only and low-risk steps can run automatically; anything that moves money, changes customer data or closes a case waits for a named approver. Thresholds, permissions and escalation paths are configured, versioned and reviewed like any other control, and each agent works only with the access its role allows.
Can we audit what an agent did and why?
Yes. We design agents to record every input, retrieved document, tool call, recommendation and approval, with timestamps and identities. That record lets your auditors, risk teams and regulators reconstruct any decision, and it feeds the evaluation and monitoring that keep agent quality measurable over time.
Which workflows should we automate first?
Start where volume is high, the rules are well understood and mistakes are easy to catch: reconciliation breaks, alert triage, incident diagnosis and documentation search. These build confidence and evidence for your governance process before agents take on work that needs more judgment or affects customers directly.
How do agents fit our model risk management process?
We treat an agent as a system of models, prompts, tools and permissions, and document each part. Evaluation suites, test results, change logs and production monitoring give your model risk team the evidence to review, approve and periodically revalidate agents under your existing framework.
Put governed AI agents to work
Talk to a senior consultant about where agents fit in your payment operations.