AI Data Analytics for Financial ServicesSurface business insights without the exposure.
We are building a secure, AI-enabled data analytics platform for regulated financial institutions. Ask a question in natural language and get a precise answer in seconds, delivered as a chart, table, or summary — without your data ever leaving your security boundary.
The Problem
Every AI analytics pilot hits the same wall.
It's not a technology problem — it's a trust problem: 73% of enterprises cite data privacy and security as their top AI risk (Deloitte, 2026). Here's what stops AI analytics before it ever reaches production in financial services.
Your security team blocks it before the demo starts
Access control and audit get evaluated before anyone even looks at the analytics itself. No clear answer there, and the deal's dead before a proof of concept begins.
Sending data to a third-party model is an instant no
If customer data — or any version of it — touches an outside AI provider, Legal shuts it down. Doesn't matter what the contract says.
A simple question takes your analysts days
Every question still has to go through the data team to get answered — a process that can take days to weeks. Business leaders still can't just ask and get an answer directly.
Built for spreadsheets, not your databases
Most AI analytics tools are built for spreadsheets and CSVs, not the legacy databases still common in financial services. At real volume, that means slow, expensive, and often wrong answers.
The Product
Ask a question. Get a precise business insight.
Any authorized user types a question into a secure chat interface and gets back a precise answer in the form of a chart, table, or narrative summary to uncover business insights — typically within seconds, without writing a single line of SQL query.
Each question moves through three coordinated tiers under the hood to deliver the insight:
Question → query
A fine-tuned model reads the natural-language question and generates a targeted, deterministic SQL or R query. No raw data touches the model here.
Query → result
The query runs directly against your data layer inside an isolated environment. Heavy processing happens in native, optimized engines — not a model's context window.
Result → insight
The model receives only the small, final result and turns it into the answer — a chart, table, or narrative summary your team can act on immediately.
Example Use Cases
Compliance
“Show me all wire transfers over $10,000 flagged for manual review last month, grouped by branch.”
Ranked by branch
No need to write a SQL query manually — the platform automatically generates and executes the query against the bank's transaction data, inside your secure, isolated environment.
Risk
“What's our current exposure concentration by sector, and how has it changed over the last three quarters?”
No need to know which tables hold the underlying position data — everything is combined across accounts and sectors in seconds, without writing a complicated query or waiting on a data team for days.
Fraud
“Which accounts show a pattern of small transactions just below our reporting threshold in the last 30 days?”
Flagged in the last 30 days
No custom detection logic required upfront — the pattern is flagged automatically, without your data ever going to a third-party AI and being exposed to external eyes.
Architecture
Security isn't a layer on top. It's the foundation.
Every design decision starts from a simple premise: no customer data, or any derived representation of it, should ever need to leave the customer's security boundary.
Customer-controlled encryption
Data is encrypted in transit and at rest, with the customer retaining control over keys and access — the control model of a self-managed store, without the operational burden of self-hosting.
Self-hosted, open-source models only
All inference runs on self-hosted, open-source models inside our managed infrastructure. No customer data is ever transmitted to a third-party foundation model provider.
Row- and attribute-level access control
Access is enforced at the row and attribute level — not just who can use the app, but exactly which data they can see, in which context.
Isolated execution, zero-trust retrieval
Each customer's workloads run in dedicated, network-isolated environments. Unauthorized data is physically prevented from ever reaching the model.
Why We're Built to Do This
Built on more than a decade of building secure data systems at scale.
Tarun Chauhan is a Senior Software Engineer with over 12 years of experience mainly designing and building large-scale, distributed data security and data analytics solutions across his career, including over 7 years at AWS building critical data systems for three of its flagship products. He holds a B.Tech from IIT Delhi, widely regarded as India's top engineering university.
Career Highlights
- AWS OpenSearch — Built the data security and access system vending secure credentials for OpenSearch domains.
- AWS Bedrock — Built guardrail services separating model access from raw data access.
- AWS FinSpace — Built data security, host-level access control, and data access permissions for AWS's managed financial-services analytics platform (KDB Insights).
- Misys (now Finastra) — Built data analytics products for banks, serving financial institutions.
Be first to bring natural-language analytics to your data.
We're building with early design partners in financial services. Tell us where to reach you and we'll follow up as pilots open.