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Agentic financial dashboards are dashboards with AI agents behind them: the agents connect to the market, ratings, filings and news data your firm already uses, check it continuously, explain what changed and why it matters, and show the source for every point, so a person decides what to do next.
Enso Labs is a principal-led AI studio in New York City. We build these on your own platform and data, with your engineers alongside ours, and we can operate them after launch or hand them over.
This page is written for heads of data, AI, risk, credit and research at banks, insurers, asset managers and fintech firms who already have the data and want agents that act on it.
Prices, trades, ratings, filings and news already arrive. People still read them by hand, on a calendar, one name at a time.
Most news about a borrower, issuer or holding does not matter. The one item that does is easy to miss among the rest.
A regulator, auditor or committee will ask why something was flagged. The answer has to point to a source and a written rule, not to a model.
Each dashboard is a set of agents working to rules your team writes down. They run on your cloud and your data platform, and every alert carries its sources.
Agents read news, filings and options flow, score each setup against your rules and limits, and present decision support with pre-trade checks and a full audit log. This is the pattern behind our own Trading Terminal.
Per-CUSIP monitoring that combines secondary-market trades, rating actions, continuing disclosure filings and local news into an early-warning score, with the reason for each change.
Watchlists for borrowers and counterparties that track financial statements, covenants, rating changes and news, and escalate to the analyst who owns the name.
Screens and notes across funds and holdings using the research data you license, with overlap, exposure and change-since-last-review views and every figure cited.
Agents assemble a first draft of an underwriting or review memo from the source documents, in your template, with each statement linked to where it came from. An analyst edits and signs.
Where the data is not yet in one place, we connect feeds to a governed layer keyed on the identifiers you already use, so every agent and dashboard reads the same numbers.
Maturity assessment, opportunity map, prioritized backlog. It ends with a written roadmap and a working agentic prototype.
Here the audit picks the first dashboard, maps the feeds it needs and the rules it must follow, and delivers a working prototype on a sample of your data or your historical records.
Scoped to one high-leverage use case: roadmap, business case and governance, a production agentic system, an eval harness and an ops runbook.
The pilot puts that dashboard and its agents into production on your platform, with your engineers embedded so they can run it on day two.
We run the system, report on it monthly, and train your people on it. When you want it in-house, we document it and hand it over.
We keep the agents tuned as rules and data change, add the next dashboard when the team asks for it, or hand over fully to your team.
Our closest proof is our own trading platform and a document-intelligence system that turns a large body of sources into a short list of findings experts trust.
Our own signal-intelligence and options platform, built on Claude and MCP with brokerage APIs, risk limits and kill switches.
See the Trading Terminal →How an early-warning agent for a muni portfolio separates material events from noise and explains each score change.
See muni surveillance →For a Fortune 500 manufacturer: 16 signals surfaced from 731 documents, using written expert rules and human review.
Read the case study →It is a dashboard with AI agents behind it. The agents connect to the market, ratings, filings and news data a firm already uses, check it continuously, explain what changed and why it matters, and attach the source for every point. Anything material goes to a named person, who decides what happens next.
The feeds you already license and store: trade and pricing data, rating agency data, regulatory filings, audited financials, research platforms and internal systems, plus public news and websites where you approve them. Many data providers now offer MCP access for AI agents. We connect by MCP or API where available and to your data lakehouse or warehouse otherwise.
Yes, and that is our default. We build on the platform you have invested in, for example AWS Bedrock AgentCore, Azure, Google Cloud or IBM watsonx, with your data platform such as Snowflake, MongoDB or Kafka. Your governance, access control and logging stay in place.
The reasoning can vary, so the rules and the outputs are fixed: each agent scores against a written rubric, returns a structured result, and is tested against an evaluation set built from your historical cases before it goes live. A person reviews anything the agent flags as material.
We work with them. Our engineers embed with your team during the build so the solution fits your platform and your people can maintain it after launch. We can also operate it as a managed service if you prefer.
With a fixed-fee 2-Week AI Audit that picks the first dashboard and delivers a working prototype on your data or historical records. A 12-Week Pilot-to-Production follows, then managed operations or a handover. You own the code, prompts, evaluation sets and documentation.
Tell us the portfolio, the feeds you already have and the decision that is slowest today. Sav replies within 24 hours.
