Use QVeris to help AI agents discover, inspect, and call verified financial capabilities for stock screening, company research, market data lookup, and structured research workflows.
AI agents can summarize, reason, and draft analysis, but financial research workflows require external data and structured tools. Useful financial research agents need access to market data, company profiles, fundamentals, filings, financial news, analyst context, and other verified capabilities.
QVeris gives agents one capability layer for discovering, inspecting, and calling relevant financial tools — without hardcoding every data provider, news API, or research source.
Not investment advice. QVeris provides a capability routing layer for developer and research workflows. It does not provide investment advice, stock recommendations, or guaranteed research accuracy. All financial research outputs should be reviewed and verified by qualified humans.
Four core challenges that make financial research agent development complex and time-consuming.
Market data, company profiles, news, filings, fundamentals, macro indicators, and alternative signals often come from different providers — each with its own API, schema, and pricing model.
Before calling a financial capability, agents need to understand required parameters, response format, billing rules, quality signals, and provider behavior — not guess after a failed call.
Manually wiring every financial data API or news provider creates wrappers, authentication logic, error handling, and maintenance overhead that compounds with each new data source.
Teams need visibility into which capabilities were called, whether calls succeeded, how much usage was consumed, and whether the structured output is usable downstream.
The agent searches QVeris for relevant finance, market data, company research, news, filing, or risk capabilities — all through one interface.
The agent inspects schema, required inputs, output structure, cost signals, and provider information before calling. No blind API calls.
The agent calls the selected capability and uses the structured result for screening, summaries, dashboards, reports, or downstream workflows.
Eight concrete financial research workflows powered by AI agents and QVeris capabilities.
Screen companies by market, sector, available signals, fundamentals, performance context, or research criteria — discovered and called through one capability layer.
Retrieve structured company information and prepare research-ready summaries for analysts or product workflows without wiring each data source manually.
Let agents access relevant market data capabilities and return structured outputs for apps, dashboards, or reports — with inspectable schemas and costs.
Build workflows that monitor market-relevant public information and summarize updates for review, without manually aggregating news sources.
Use financial and document capabilities to support structured review of company updates, reports, or investor materials within an agent workflow.
Route agents toward capabilities that help collect risk-related data, entity context, market context, or compliance-relevant information for due diligence.
Extend agents with capabilities for crypto prices, macro indicators, market context, or cross-asset research workflows — one capability layer, not multiple providers.
Turn structured capability outputs into watchlists, thesis snapshots, comparison tables, or analyst-ready briefs from diverse financial data sources.
An illustrative workflow showing how an AI agent uses QVeris to go from a research question to structured results. Not live financial data.
The agent receives a research question about a market, sector, or set of companies.
The agent uses QVeris to find capabilities for market data, company profiles, and financial context.
Before calling, the agent inspects required parameters, output structure, provider info, and billing signals.
The agent executes the selected capabilities and receives structured responses.
The agent organizes the structured output into a research-ready format for human review.
A qualified reviewer inspects, validates, and applies judgment before the result is used in any downstream decision.
This is an illustrative example. It does not represent live financial data, investment advice, or stock recommendations. All research outputs should be reviewed and verified by qualified humans before use in financial, trading, legal, or high-stakes decisions.
| Requirement | Manual API integrations | QVeris for financial research agents |
|---|---|---|
| Tool discovery | Developers search providers and documentation manually | ✓Agents can discover relevant capabilities from one interface |
| Schema understanding | Developers read and maintain provider-specific docs | ✓Agents inspect schema, parameters, and cost signals before execution |
| Multiple data sources | Each provider requires a separate wrapper and integration path | ✓Agents use a unified capability layer across verified providers |
| Workflow flexibility | Harder to adapt when research questions change | ✓Agents can discover new capabilities dynamically as the workflow evolves |
| Usage visibility | Usage and billing are spread across separate provider dashboards | ✓Usage can be reviewed through QVeris usage history and credits ledger |
Teams building finance apps, research assistants, screening tools, or agent-powered dashboards that need structured financial data beyond model context.
Teams that need structured workflows for company research, market monitoring, and research brief generation — without rebuilding data pipelines each time.
Product teams adding financial data workflows, company lookup, market context, or research automation to their applications through a unified capability layer.
Developers who need a flexible capability layer for financial research instead of wiring multiple financial APIs, news sources, and data providers manually.
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Use QVeris to give AI agents access to financial research capabilities for stock screening, company research, market data lookup, and structured research workflows.
