The Investment Research Data Problem

Why Traditional Investment Research Is Broken

Investment research has a dirty secret: analysts spend 60% of their time finding and integrating data, and only 40% actually analyzing it.^[3] Every data source — real-time prices, SEC filings, earnings transcripts, analyst consensus, news sentiment — requires its own API key, its own SDK, its own authentication, and its own maintenance contract. A typical research team maintains 5-10 separate data integrations. When an API changes or a new source becomes relevant, it's days of engineering work to adapt.

The Rise of Agentic Investment Research

2026 has seen an explosion of AI-powered investment research tools. KX + NVIDIA launched Agentic AI Blueprints for capital markets at GTC 2026, compressing research cycles from hours to minutes.^[1] Perplexity Finance now integrates Morningstar, PitchBook, and Daloopa data with 40+ live finance tools.^[2] RBC Capital Markets deployed "Aiden Quick Takes" — 14 specialized agents that reduce research time from hours to minutes.^[1]

But every one of these tools solves the application layer — they're designed for human analysts to use through a GUI. None of them address the infrastructure layer that AI agents need to autonomously discover and call financial data. That's where QVeris fits.

What to Look for in an AI Investment Research Platform

Before evaluating any platform, define what your AI investment research agent actually needs. Here are the five criteria that separate infrastructure from applications:

QVeris was designed from day one to meet all five criteria. Application-layer tools (Perplexity Finance, AlphaSense, Bloomberg) were designed for human analysts. They're complementary, not competitive — QVeris is the infrastructure layer beneath them.

How QVeris Powers AI Investment Research Agents

QVeris is a capability routing network that sits between your AI agent and the financial data it needs. Instead of integrating 10 separate APIs, you integrate one — and your agent discovers the capabilities it needs conversationally.

Investment Research Agent: Real-World Workflows

Here are three concrete agent workflows you can build with QVeris today. Each demonstrates how the AI investment research agent pattern works in practice.

Try it yourself: Any of these workflows can be built in under 30 minutes with a free QVeris account. No per-API contracts, no negotiations with data vendors — just one API key and natural language discovery.

QVeris vs Traditional Investment Research Tools

The key insight: QVeris does not compete with application-layer tools. It powers the layer beneath them. Here's how the stacks compare:

Infrastructure Layer vs Application Layer

Getting Started: Build Your First Investment Research Agent

Here's how to build a simple AI investment research agent in Python using QVeris. This agent discovers and fetches S&P 500 market data with about 20 lines of code.

Prerequisites

• Python 3.9+
• QVeris API Key (free from qveris.ai)
• QVeris Python SDK: pip install qveris-sdk

Code Example

# Step 1: Initialize the QVeris client
from qveris import QVerisClient
client = QVerisClient(api_key="your-api-key")

# Step 2: Discover financial capabilities
capabilities = client.discover(
    query="S&P 500 current market data top movers"
)
# Returns: ranked capabilities with cost (~1 credit), 
# latency (~180ms), and success rate (99.8%)

# Step 3: Inspect the best match
sp500_cap = capabilities[0]
inspection = client.inspect(sp500_cap.id)
# Preview: schema, cost, latency, reliability — always free

# Step 4: Call and get structured data
result = client.call(sp500_cap.id, params={
    "symbols": ["SPY", "QQQ", "IWM"],
    "fields": ["price", "change", "volume"]
})
# Returns: structured JSON with full audit trail
print(result.data)

Copy this code, paste it into your editor, and run it. Your first AI investment research agent starts here. For the full agent workflow (morning briefing, earnings analysis, risk monitoring), check the QVeris documentation.

Frequently Asked Questions

Common Questions About AI for Investment Research