Introduction

Investment teams need web signals that are current, complete, and defensible. Nimble provides web search and retrieval for research workflows:

• Web Search Agents answer research questions and build datasets with per-claim citations.

• Search gives analysts and agents real-time web context.

• Extraction Templates return normalized data from major marketplaces, search engines, and maps.

All of it runs under enterprise governance.

Signals and research tasks

• Pricing, promotions, and availability across marketplaces and D2C sites, by region and ZIP code, for demand and margin signals

• Hiring and job postings, for expansion, contraction, and function or geography mix

• Filings, announcements, and disclosure updates, for event-driven strategies and risk screens

• Reviews, ratings, and forum chatter, for sentiment and quality momentum

• Search and AI visibility: Google rankings, AI Overviews, ChatGPT and Gemini answers, as category-momentum signals

• Company and sector research briefs with citations, for diligence and idea generation

How it works

Live vs. cached data

Why self-learning matters for recurring research

Funds run the same research repeatedly: weekly KPI checks, coverage updates, event monitoring. Web Search Agents remember which sources and retrieval paths produced the right data. In Nimble's repeat-use benchmark (company research, market analysis, GTM, and social monitoring), Nimble improved +4.4 points from run 1 to run 3, versus a +0.1 average for Exa and Parallel (+0.2 and 0.0 respectively), and was the only system to improve on every run. Recurring tasks also become up to 50% cheaper over time.

Compliance, governance, and auditability

• Public web data only, with no login-gated or paywalled sources, which supports MNPI screening. See Buy-Side Alt-Data Compliance.

• Nimble acts primarily as a processor, with DPAs available. SOC 2 Type II and GDPR/CCPA alignment. Zero Data Retention on enterprise plans. Trust Center

• Every Web Search Agent output includes the sources consulted, citations, and confidence, so you can trace any datapoint back to where it came from.

Delivery

API responses, native Snowflake and Databricks integrations that run Nimble directly from SQL, or delivery to BigQuery or S3/GCS in JSON, CSV, or Parquet. For hands-off feeds, Nimble's managed data services configure and run the whole pipeline.

Proof points

• DRACO deep-research benchmark: #1 overall across 100 tasks in 10 domains (Nimble x-high: 74.0% overall, 71.6% factual accuracy). Benchmarks

• Alta: >99% job success at millions of pages per day and 3–4× deeper context. Case study

Implementation checklist

1. Define signals, research questions, cadence, and geographies.

2. Define output schemas; configure Web Search Agents and Extraction Templates.

3. Sign the DPA; confirm data residency for your destinations.

4. Run a pilot; review citation coverage, confidence, and accuracy against known values.

5. Schedule recurring runs and wire outputs to models and dashboards.

FAQ

• Do you collect non-public data? No. Public web data only.

• Can every datapoint be traced? Yes. Web Search Agent outputs include per-claim citations with verbatim excerpts and graded confidence.

• How fast is time-to-value? Most teams run a pilot within days.