Get Started with your requirements and primary focus, that will help us to make your solution
We build and run AI agents for credit, KYC, fraud, operations and service, inside your core banking stack. Each agent works within limits your risk team sets, and every decision it makes is traced for model risk, audit and the regulator.
Trusted on a five-year contract by a leading Indian bank.
Banks use AI to assess credit, verify customers, detect fraud and money laundering, collect payments, serve customers, reconcile operations and prepare regulatory reports. Increasingly, AI agents do this work end to end. They read data, apply policy, propose a decision and act, while people approve high-risk steps and every decision is recorded.
Banks don't struggle to build the model. The hard part is putting it into production in a way that model risk, compliance and the regulator will sign off on. That needs explainable decisions, firm limits on what an agent may do, and evidence for every action. We build that in from the start.
Governed agents for banking workflows, with decision boundaries and an audit trail.
Gather bureau, statement and relationship data, apply credit policy, and propose a decision with reasons.
Onboarding checks, periodic reviews, sanctions and PEP screening, and drafting investigation narratives for alerts.
Triage transaction alerts, link related activity, and hold or release within limits, with analyst sign-off on the biggest cases.
Segment accounts, choose the next best action and offer restructuring that stays within conduct rules.
Assistants that resolve requests on the account itself, not just answer FAQs, and hand off to staff with full context.
Match ledgers, investigate breaks, route exceptions and clear payment queues.
Assemble returns from source systems, check them for consistency and keep the lineage behind every figure.
Liquidity monitoring, cash forecasting and limit checks, with traders and ALCO approving any action.
The RBI, the Fed and OCC under SR 11-7, the EBA, the EU AI Act (which treats credit scoring as high-risk) and the CBUAE all expect the same things. Banks have to show that a model is governed, that its decisions can be explained, and that a person stays accountable for them. Context, Control and Action, with a decision trace, gives you that by design.
Agents decide from governed customer, product and policy data, with the source of every input recorded.
Credit limits, risk appetite and conduct rules are enforced before any action runs, not checked afterwards.
Agents act through approved tools in core banking, LOS and CRM, with maker-checker for sensitive steps.
Inputs, policy version, model version, reasons and approver for every decision, ready for audit and model validation.
Every agent starts by running beside your team. Your model risk function sees the evidence before it approves the next level.
Banking data often can't leave the country, and sometimes can't leave the bank. We deploy agents, models and the decision trace on your premises, in your private cloud, or in an in-country cloud region, with open-weight models where needed.
Inside your data centre, with no external calls for regulated workloads.
An in-country cloud region that meets data residency rules.
Sensitive decisions stay local. Low-risk tasks can use cloud models.
Role-based access and encryption, with agent activity watched by our.
Governed agents for banking workflows, with decision boundaries and an audit trail.
Finacle, Temenos, Oracle Flexcube and other cores, through APIs, an ESB or read replicas.
Loan origination systems, credit bureaus, AML and fraud engines, and case management.
CRM, contact centre, document management, data warehouses and lakehouses such as Snowflake and Databricks.
Governed agents for banking workflows, with decision boundaries and an audit trail.
| Dimension | Banking chatbot | RPA | Governed AI agents |
|---|---|---|---|
| What it does | Answers questions | Repeats fixed keystrokes | Completes multi-step work and proposes decisions |
| Handles exceptions | Hands off to a person | Breaks | Reasons through them within policy, or escalates |
| Explainability | Limited | Script logs | Reasons and policy recorded for every decision |
| Controls | Content filters | Access rights | Decision boundaries and maker-checker before every action |
| Audit evidence | Chat transcripts | Bot run logs | A full decision trace for model risk and regulators |
| Rollout | Go live | Go live | Shadow, supervised, bounded, then full |
Governed agents for banking workflows, with decision boundaries and an audit trail.
One decision, such as a KYC refresh or alert triage, running in shadow mode on your data, with evidence your model risk team can review. Scope and timeline are agreed per engagement.
A portfolio of banking agents run under SLA, with working inside your teams.
We build with your AI centre of excellence, then hand over the agents and the platform. More on our services.
They are AI systems that complete banking work end to end, such as a KYC review or an alert investigation. They use bank data and tools, work within set limits, and send high-risk steps to people for approval.
It's AI that plans and acts rather than only predicting or answering. In a bank, that means doing the work under the same controls as a human employee: maker-checker, limits and audit trails.
Each agent comes with documentation, a test set, monitoring, and a decision trace that records the inputs, policy and model version behind every decision. Trust graduation gives your validators evidence before autonomy is raised.
Credit scoring is classed as high-risk, which brings duties on risk management, data quality, transparency and human oversight. Our approach is built around those duties, but your compliance team decides how they apply to you.
Yes. Agents, models and decision traces can run on-premises or in an in-country cloud, using open-weight models where external APIs aren't allowed.
Finacle, Temenos, Oracle Flexcube and others, plus loan origination systems, CRM, AML and fraud engines, and data platforms. Production-proven integrations are confirmed for your stack during scoping.
Start with a high-volume decision that has clear policy and a human reviewer today, such as KYC refresh, alert triage or reconciliation breaks. Run it in shadow mode first and prove its accuracy with decision traces.
We'll choose a decision with clear policy and real volume, map the systems and controls it needs, and show what shadow mode would prove to your risk team.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
