Olostep × Openmart Case Study

Openmart partnered with Olostep to turn messy web data into reliable, structured intelligence at scale. Using Olostep’s infrastructure, render engine and parser framework, Openmart automated web data intelligence processes, cutting lead time from days to minutes while improving accuracy and coverage.

“Olostep let us move from sporadic, manual scripts to a reliable data product our teams trust. We ship faster because the data is already clean, complete, and ready to use.”

About Openmart

Openmart provides local business intelligence and AI‑powered sales agents. Teams use Openmart to search Google Maps and the open web, enrich leads with context, and sync qualified accounts to CRMs like Salesforce and HubSpot. The product focuses on:

• Finding and qualifying local businesses by category and geography
• Surfacing decision‑maker signals and contact info
• Generating concise company summaries that reps can use immediately

Challenge

Openmart’s team faced four recurring issues:

• Fragmented sources (Maps results, place pages, and company sites)
• Dynamic, JavaScript‑heavy pages with bot defenses and geo variance
• Manual reconciliation and deduplication across lists and spreadsheets
• Inconsistent schemas that slowed ranking, routing, and CRM sync

The result: slower lead delivery to reps and duplicated engineering effort.

Solution

Openmart adopted Olostep’s API for high‑quality rendering, resilient fetching, and structured extraction:

• Headless rendering with automatic retries, proxy rotation, and CAPTCHA handling
• Batch jobs via the /batches endpoint to process tens of thousands of URLs in parallel
• AI enrichment via the /answers endpoint to summarize and normalize business context
• Versioned parsers with validation to guarantee schema stability

Integrating Olostep

Openmart integrated two core Olostep capabilities:

1. Large‑scale URL processing with the Batch API for Maps place URLs and company websites
2. AI enrichment with the Answers API to generate structured summaries and sales signals

Key implementation details:

• Use /batches to queue 10k–100k URLs per job (Maps results, place details, and site pages)
• For each item, run a parser to extract normalized fields: name, category, address, website, phones, opening hours, and social links
• Call /answers with domain‑specific prompts to produce: what the business does, ICP fit score, key offerings, and next‑best‑action
• Geo‑aware fetching for city/region specificity
• Webhooks + S3 export to feed Openmart’s lead store and CRM sync workers

Results

• Faster lead delivery: fresh, qualified leads available in hours, not days
• Higher coverage: resilient crawling across dynamic Maps and website content
• Better accuracy: validated, typed fields ready for scoring and routing
• Lower ops cost: fewer custom scrapers, fewer breakages, simpler maintenance

Openmart now treats the web like a reliable data source. With Olostep, ingestion is predictable, structured, and directly connected to outcomes—from more qualified pipeline to faster, more confident outreach.

Ready to Get Started?

Test Olostep with 500 free credits, no credit card required.

Start Building →

---

References:

• Openmart — AI sales intelligence
• Olostep — Web Data API for AI
• Olostep Docs: Batch API
• Olostep Docs: Answers API

Frequently Asked Questions

How do you scrape Google Maps business data at scale?

Scrape Google Maps business data at scale using a batch processing API that handles JavaScript rendering and geo-specific results. Olostep's Batch endpoint can process tens of thousands of Maps URLs in parallel, extracting structured fields like business name, category, address, phone, hours, and reviews. The API handles retries, proxy rotation, and regional variations automatically, making it reliable for building lead databases from Maps results.

What's the best API for local business intelligence?

The best API for local business intelligence should provide reliable extraction from Google Maps, place pages, and company websites, plus AI enrichment to qualify and score leads. Olostep combines the Batch API for large-scale scraping and the Answers API for generating structured business summaries, ICP fit scores, and sales signals. This gives you both raw data and actionable intelligence without building custom scrapers.

How can I enrich sales leads with web data automatically?

Enrich sales leads automatically by scraping company websites, Maps listings, and social profiles, then using AI to generate summaries and qualification scores. Use Olostep's Batch API to process lead lists (URLs) in parallel and extract normalized fields. Then call the Answers endpoint with a sales-focused prompt to generate: what the business does, decision-maker signals, estimated company size, and next-best-action recommendations—all in structured JSON.

What tools help automate lead generation from Google Maps?

Lead generation from Google Maps requires scraping Maps search results and place pages, then enriching business data with contact info and qualification signals. Olostep provides Batch processing to scrape thousands of Maps URLs at once, extracting structured data like name, location, category, website, and phone. Combined with AI enrichment via the Answers API, you can automatically build qualified lead lists and sync them to your CRM.

How do you build a sales intelligence platform with web scraping?

Build a sales intelligence platform by combining large-scale web scraping with AI-powered enrichment. Use Olostep's Batch API to scrape Google Maps, company websites, and social profiles at scale—extracting structured data like contact info, business categories, and attributes. Then use the Answers API to generate summaries, ICP fit scores, and buying signals. This gives your sales team qualified leads with rich context instead of raw data.