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.

• Confidence is graded by fixed rules: a primary source (an official filing, investor-relations page, or company site) or corroboration across two or more different domains grades high; a single secondary source grades medium; no usable citation grades low. Each grade comes with the reasoning behind it. Trust docs

• In enrichment runs, values you already supplied are marked pre_existing and excluded from grading, and a cited null is a verified absence (for example, a company with no public funding history), not a missing value.

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 grades, and accuracy against known values. Accept high values automatically, spot-check medium, and route low to an analyst.

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.