Answers you can trust
Figures are computed from your transaction and settlement data, never generated by a language model. Each answer is designed to trace back to its source records, so a reviewer can verify it.
Accuracy & Evaluation →Why DeepSpent
Conversational Spend and Merchant Intelligence your risk team can sign off on.
DeepSpent's own models understand the question. Deterministic analytics compute the answer from your data. Nothing is guessed.
Banks have had two options. Neither is good enough.
Dashboards tell you what happened. They rarely tell you why. Customers click through screens, set filters and squint at charts, and still can't ask the obvious next question. Inside the bank, anything beyond a fixed view becomes a report request, a spreadsheet, a SQL query and a wait for an analyst.
General-purpose AI chatbots are flexible, but they can produce figures that are not in your data, send queries to third parties, and bill per token.
DeepSpent gives you the flexibility of plain-language questions with the reliability of computed answers.
Four reasons teams choose DeepSpent
Your data stays in your environment
DeepSpent is designed to deploy in the environment you control: your compute, storage and network, on standard CPU servers with no GPU required. Our own models mean no third-party LLM calls, no per-token bills and no model-vendor dependency.
Trust Center →Both sides of payments, one vendor
Spend Intelligence serves cardholders and customers. Merchant Intelligence serves merchants and their settlement and payment business.
Products →Built to plug in
Use the REST API in your apps, or connect your own AI copilots and agents through MCP, using the same OAuth trust model. Each deployment is validated against your data before go-live.
How it works
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Ask
A customer or analyst asks in plain language: "How much did I spend on travel last quarter?"
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Understand
DeepSpent's purpose-built model identifies the intent, time period, merchant and category.
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Compute
Deterministic analytics run against your structured data and return the figure with its source trail.
Want the detail? See how we measure accuracy →
How DeepSpent compares
| Capability | DeepSpent | General-purpose AI chatbot | Dashboards and BI reports |
|---|---|---|---|
| Where the numbers come from | Computed from your data | Generated by the model; can be wrong | Computed from your data |
| Plain-language questions | Yes | Yes | No; fixed views |
| Traceable to source records | Designed to be | Rarely | Partly |
| Where it runs | Your environment | Usually the vendor's cloud | Varies |
| Hardware needed | Standard CPU servers; no GPU required | Vendor-managed GPU infrastructure, or GPUs you provide if self-hosted | Standard servers |
| Cost model | Predictable; no per-query LLM fees | Per token or per query | Licence plus build effort |
| Access for your AI agents (MCP) | Included | Varies | Rarely |
Built for the people who have to say yes
Risk & Compliance
Every figure is computed, not generated, and designed to be traceable to source data. The platform is designed to run in your environment, and models are trained on synthetic and non-customer data. Each deployment is validated per customer before go-live.
Product & Digital
Replace static reports with questions customers can ask in your app. Personalised insights and financial-wellness guidance give customers a reason to come back, and can reduce routine support queries. Integrate through REST, or give your own AI agents access through MCP.
Don't take our word for it
Trust Center
Security, privacy and responsible AI.
Visit the Trust Center →Accuracy & Evaluation
How we measure and report accuracy.
See how we measure accuracy →
"We built DeepSpent because banks shouldn't have to choose between answers that are easy to get and answers they can defend. Our team brings more than two decades of engineering experience and deep banking domain expertise to that problem."