Introducing agentic analytics for financial institutions

Talk to your data.
Get an analyst-grade answer.

Ask one high-level question. The agent investigates across every source and returns an analyst-grade answer.

Talk to Data — demo
Book a Walkthrough →Deploy on your cloud. Bring your own LLM. Full control.

Traditional BI finds a number.
Our AI agent does actual research.

Ask one high-level question. It runs the analysis in parallel and keeps going until it has the full picture.

Talk to Data
Q · Why did delinquency rise in Q2?▍
Investigating
✓ Segment analysis
✓ Geography analysis
✓ Utilization trend
✓ Spend shift
✓ Risk score drift
Conclusion
Primary driver: Tier-2 revolvers with high utilization
Traced to: transactions · risk_scores · segment_map
✓ 5 queries run automatically
Not single-shot

One question becomes multiple investigative paths — the agent runs queries in parallel, reads the results, and decides what to check next.

Follows the evidence

It reads each result and forms the next question itself — testing the likely explanations until the picture holds together.

Built for enterprise control

Your data stays in your environment. Every query and reasoning step stays traceable and human-auditable.

Use Cases

The questions that don't have a one-line answer.

Hand it the open, high-stakes question you'd normally give an analyst for a week. It explores your data end to end, tests the likely explanations, and comes back with a written note and the charts to back it — not a single number, and not another text-to-SQL box.

01 Portfolio & Program
Falling spend per card Why is spend per active card sliding, and which programs are dragging it?
Co-brand economics Which co-brand partnerships are actually earning their economics?
Flat base — why? Is our base flat because acquisition slowed or attrition rose?
02 Credit & Risk
Root of early delinquency Is early-stage delinquency a vintage, a segment, or a policy problem?
Approvals → default Where is our approval strategy letting in first-payment defaults?
Cohorts aging off-plan Which cohorts are aging worse than we priced for?
03 Collections & Recovery
Where recovery leaks Where are we leaking recovery dollars across buckets and strategies?
Strategy vs cost-to-collect Which strategies underperform once you factor in cost-to-collect?
04 Fraud & Disputes
Emerging fraud & exposure What fraud pattern is emerging this month, and how big is the exposure?
What's driving disputes Which merchant / BIN clusters are driving the dispute surge, and why?
05 Growth & Marketing
Untapped EMI opportunities Which cohorts are our best untapped EMI / cross-sell opportunities?
Why activations faded Why did this campaign's activations fade, and who actually converted?
06 Finance & Interchange
Shifting interchange mix How is our interchange mix shifting, and what does it do to fee income?
Fee income — what moved What moved fee income this quarter — structural or one-off?
07 Insurance
Rising claims ratio What's pushing the claims ratio up, and in which product lines?
Digital vs agent lapse Why do digital-sourced policies lapse faster than agent-sourced?
Why Teams Trust Us

The answer is already in your data.
We just make sure you can find it before you finish your coffee.

Built by a team that runs credit infrastructure for banks and fintechs — processing millions of transaction events where accuracy is non-negotiable. That same rigor powers this agent.

Deploy on your cloud

Runs entirely inside your own VPC — data never leaves.

Bring your own LLM

Plug in the model you already trust and govern.

Full query traceability

Every query, table and row behind a claim is recorded.

Human-auditable reasoning

Retrace each step before anyone acts on it.

Book a walkthrough See the agent work end-to-end on realistic, true-to-life sample data - book a demo for a detailed walkthrough.
FAQ

Built for how regulated teams buy.

Security, auditability, and control — the questions every diligence checklist starts with.

Text-to-SQL turns a question you already know how to ask into one query and one number. This takes an open, ambiguous question - the kind with no obvious query - decides what to pull, tests the likely explanations, and returns a reasoned note with charts. Think analyst, not query box. The two sit side by side: keep your SQL tool for the known metric, bring this for the "why" and "so what."

You choose where it runs. On your own cloud, all your data stays inside your infrastructure - exactly like any other application you run - and nothing leaves. If we host it, we set up a bank-grade encrypted pipeline for the data pull, and you can enforce anonymization on your side so we never even see raw PII identifiers.

Only aggregated, anonymized data - never raw customer or transaction records. We keep full tracing in place so you can continuously validate exactly what is shared, and with bring-your-own-LLM you can keep the model entirely within your own environment.

Yes, answers honor your existing row- and column-level access controls, so people only see data they're already entitled to.

It shows its work. Every step, and the exact query, tables, and rows behind each chart and claim, are laid out so an analyst can retrace the whole chain and verify before anyone acts on it.

Any data warehouse. We support AWS, Azure, and GCP today, and can add a new platform in about a week. On the source side it reaches your core card and transaction systems, loan management, ledgers, and warehouses.

Sensitive fields can be masked or excluded by policy, with access governed centrally, and you can enforce anonymization at source so raw PII identifiers never leave your side.

Yes, every question and access event is logged for internal audit and regulatory review.

A full exploration runs in minutes, not the days a manual deep-dive takes. Onboarding is typically about two weeks: our forward-deployed and AI product teams spend up to ten days understanding your data, then two to three days to set up and fine-tune the application once your cloud environment is ready.