Delivery model

Nimble delivers web search results, extracted content, and research outputs wherever agents and data teams need them:

To AI agents in real time via the MCP server, SDKs, and REST API
Inside the data platform via native Snowflake and Databricks integrations that call Nimble from SQL
To data stacks via async delivery to S3/GCS, or managed delivery
As continuously refreshed datasets via scheduled Web Search Agents and Extraction Templates

Integrations and destinations

Agent and LLM frameworks: MCP (Claude, ChatGPT, Cursor, and other MCP hosts), LangChain, OpenAI and Anthropic SDK tool use
Snowflake: Marketplace Native App, NIMBLE_SEARCH/NIMBLE_EXTRACT UDFs, NIMBLE_AGENT_RUN UDTF, and Cortex Code MCP
Databricks: Marketplace install with a Unity Catalog connection, and nimble_search, nimble_extract, and nimble_agent_run SQL table functions
Other data platforms: BigQuery, S3, GCS, Azure (Microsoft Fabric, Azure Data Factory), AWS (S3, SageMaker, Redshift)
Vector and BI: Pinecone, Power BI, Tableau

Implementation paths

API and SDK (self-serve)

Python and JavaScript SDKs, CLI, and REST for Search, Extract, Extraction Templates, Map, Crawl, and Web Search Agents. Start free with 5,000 requests a month.

Native warehouse integrations (self-serve)

Install from the Snowflake or Databricks Marketplace and call Nimble from SQL in notebooks, dbt models, and scheduled jobs.

Web Search Agents

Configure an agent's skill, goals, sources, effort, and output schema, or start from the agent gallery. Run it on demand or on a schedule.

Managed data services

Nimble's experts configure and operate the whole pipeline (collection, normalization, product matching, quality checks, and delivery) and stream data into your stack. Pricing is scoped with Nimble's sales team. See Web Scraping with Nimble.

How a request becomes usable output

Search finds candidate sources, guided by learned source preferences
Browse past search results into subpages, filters, and pagination
Extract clean content or typed fields
Validate with citations, confidence, schema enforcement, deduplication, and PII masking
Deliver to the agent or the destination

Onboarding and support

1:1 product onboarding and Technical Account Manager access for larger customers, arranged with Nimble's sales team
Priority response with SLAs on custom arrangements
Custom data normalization, enrichment, and product matching across sources

Enterprise options

Custom concurrent sessions, higher rate limits and burst capacity, throughput SLAs for peak events, bundled product discounts, and multi-year pricing protection. Contact sales.

Example pattern: enriching warehouse data

Customer systems remain the source of internal truth. Nimble adds external web context, such as pricing, availability, company facts, and news, so warehouse tables become continuously updated rather than static snapshots. With the native integrations, this runs as a SQL join against Web Search Agent output. A Nimble blog post on Snowflake and Upriver describes this pattern.

Nimble Integrations, Implementation, and Delivery