Finance teams spend more time extracting data than acting on it. They file tickets, wait for engineering, and by the time the report arrives, the window to use it has narrowed. The problem isn’t a lack of data. It’s that the intelligence needed to interpret it sits in a separate tool, far from where the data actually lives.
Dakota’s platform ingests and normalizes transaction, customer, and billing data, then makes it queryable in plain English.
When a user asks, "Show me fee revenue by client segment for Q1," the system interprets the question, queries the normalized data, and returns structured results through a single API. Alongside the data, it recommends a visualization based on what the user asked, and surfaces related reports which others have run on similar queries.
This isn't a suite of five separate tools. It's a sequential capability stack where each layer builds on the one below. And because the intelligence lives at the data layer, it works the same way whether the user is a product manager in a web app, a finance team in Excel, or an autonomous AI agent querying via API.
The architectural distinction matters. Application-layer BI tools sit on top of data warehouses and require users to understand the underlying schema or pre-built dashboards. When you add conversational AI to application-layer tools, the intelligence doesn't understand the data model natively, can't guarantee consistency across queries, and can't serve both human users and AI agents through a single interface.
Dakota's API-first design at the data layer means:
- Consistency: Every query — whether from a human or an agent — runs against the same normalized schemas and returns auditable results
- Accessibility: Non-technical users and AI agents query the same system without SQL
- Composability: Queries, visualizations, and recommendations compose into workflows without switching tools
The Experience: From Questions to Insights in Seconds#
A Head of Finance preparing for a board meeting needs to understand why transaction volume spiked in one corridor last quarter and whether it held. Traditionally, this means filing a request with data engineering, waiting for a custom report, and iterating when the first pass doesn't answer follow-up questions.
With Dakota, they open the Reports tab and type: "Show me transaction volume by corridor for the last six months." The system identifies the relevant transaction data, segments by payment corridor, and returns a time-series dataset with a grouped bar chart (and suggests other related reports). It’s ready in seconds. If the follow-up is "What's driving the Q4 spike in the Mexico corridor?", they ask it directly.
This workflow extends beyond human users. Autonomous AI agents responsible for monthly reporting can query the same API, generate the same insights, and assemble them into board decks without human intervention. The data layer provides consistent, auditable answers whether the analyst is a person or a process.
The impact shows up in decision cycles. Teams that previously waited days for custom reports can now iterate on questions in real time. Finance operations leaders who spent hours manually extracting data now spend that time interpreting insights. And data engineering teams shift from building one-off reports to overseeing scalable, self-service analytics.
Democratization at Institutional Scale#
The phrase "democratizing data" is overused, but the underlying goal remains critical: enable anyone who needs an answer to get one, without bottlenecks. In financial services, this isn't just a productivity improvement. It's a strategic imperative.
Cross-border money movement platforms operate in environments where data literacy varies widely. Product teams, compliance teams, treasury teams, and partner teams all consume the same transaction and ledger data but have different technical capabilities. A data architecture that requires SQL expertise or data engineering support creates gatekeepers. A data architecture with intelligence at the layer where data lives removes them.
This also unlocks agentic workflows. As AI agents take on tasks like monthly reporting, portfolio rebalancing recommendations, and client communication, they need direct access to institutional data. If analytics intelligence lives only in application-layer tools designed for humans, agents can't participate. When it lives at the data layer, agents query the same API humans use—no special integrations, no parallel systems.
The combination of human and agentic access at the data layer compounds value. A finance analyst asks a question, gets an answer, and refines the query. An AI agent monitoring the same data notices a pattern, generates a report, and flags it for review. Both workflows run on the same infrastructure, producing consistent, auditable results.
This is the shift from analytics as a specialized function to analytics as a foundational capability.It’s embedded in daily operations, accessible to all team members, and ready for both human and machine consumers.
Conclusion#
The problem with most analytics platforms is that they add AI in the wrong place. Conversational dashboards and natural language query boxes at the application layer still require users to navigate fragmented tools, understand data schemas, and wait for engineering support. The intelligence is too far from the data.
Dakota embeds analytics intelligence at the financial data layer itself, where institutional data is already normalized, structured, and ready to query. This architectural choice eliminates the gap between asking questions and getting answers. Finance teams, operations leaders, and AI agents all query the same system, through the same API, with the same institutional-grade consistency.
The result: faster decisions, fewer bottlenecks, and a data architecture ready for the next generation of institutional workflows, where humans and agents collaborate on the same foundation.
If your team spends hours extracting data instead of interpreting insights, the problem isn't lack of effort. It's that the intelligence doesn't live where the data lives. Dakota changes that.
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