Ocean.io

@ocean.io lookup the company profile for stripe.com and summarize their employee count, funding stage, and primary tech stack

Ocean.io returned a matched profile for Stripe. The company employs approximately 8,200 people globally and is classified as a late-stage private company with over $2B in total funding. Their primary tech stack includes React, Node.js, Ruby, and AWS infrastructure. The lookup consumed 0.05 credits from your account balance. This data was last refreshed by Ocean.io 14 days ago, so recent organizational changes may not yet be reflected.

This read-heavy example shows how Ocean.io enriches a single company from a domain. The MCP returns structured firmographic and technographic data that's useful for account research or lead qualification. Be aware that lookup costs 0.05 credits per successful match, and stale data is possible if Ocean.io hasn't recently crawled the target.

@ocean.io gather missing data for these 50 prospect domains we scraped from a conference attendee list, then show me which ones triggered a crawl

Ocean.io processed all 50 domains. 31 companies already had complete profiles in their database (no action taken). 12 domains returned partial data and were queued for a provider-side crawl to fill gaps—this crawl is irreversible and will update Ocean.io's records within 24-72 hours. 7 domains were invalid or unresolvable. The gather call itself was free, but any subsequent enrichment of the 12 newly-queued companies will cost 0.1 credits each once crawling completes.

This write-or-action example demonstrates Ocean.io's ability to backfill missing company data at scale. The key trade-off: triggering a crawl is a one-way operation that affects Ocean.io's global dataset, not just your account. Use this when you need fresh data on under-indexed companies, but understand you're committing Ocean.io's infrastructure to a background job.

@ocean.io search for Series A SaaS companies in Austin with 20-100 employees using React, then draft a one-sentence outreach angle for each of the top 5 results

Ocean.io returned 23 companies matching your filters. Here are personalized outreach angles for the top 5: (1) Acme Analytics—'Your React-based dashboard architecture suggests you value fast iteration; we help Series A teams ship 40% faster.' (2) CloudVault—'As a 35-person security SaaS, you're at the inflection point where compliance automation pays for itself in weeks.' (3) Streamline HR—'React shops like yours often struggle with legacy payroll APIs; our unified integration cuts onboarding time in half.' (4) DataPulse—'Series A companies in your cohort typically double headcount in 18 months—our talent intel keeps your pipeline full.' (5) FinOps Pro—'Austin SaaS founders tell us spend visibility is their #1 blind spot; we surface it in real time.' Each lookup cost 0.05 credits.

This synthesis example pairs Ocean.io's filtered search with the AI's reasoning to generate context-aware outreach. It showcases how firmographic, technographic, and geographic filters combine to surface highly targeted prospects. The AI then uses those enriched attributes to craft relevant messaging. Note that search results cost 0.05 credits per returned company, so broad queries can consume quota quickly.

When Ocean.io beats manual prospecting for a 2-person sales team

A two-person SDR team at a B2B SaaS startup needs 200 qualified leads per week. Ocean.io's Lookup Companies tool pulls firmographic data at 0.05 credits per match—cheaper than hiring a VA and faster than LinkedIn scraping. The team uses Switchy to store search filters (industry, headcount, tech stack) as reusable prompts, then runs weekly enrichment batches. The break-even is around 4,000 lookups per month; below that, a spreadsheet and manual research is often faster. Above that threshold, Ocean.io's autocomplete and taxonomy tools let the SDRs refine targeting without leaving the AI workspace. If your team closes one deal per 500 contacts and Ocean.io shaves two hours off list-building each week, the credit spend pays for itself in month one.

Why Ocean.io works for post-sale org-chart tracking at scale

A 6-person customer success team manages 80 enterprise accounts and needs to track job changes, new hires, and department growth at each customer. Ocean.io's Enrich Person and Enrich Company tools surface org-chart shifts that signal expansion or churn risk. The CS team uses Switchy to run monthly enrichment sweeps on a saved list of customer domains, flagging accounts where a champion left or a new VP joined. Each enrichment costs 0.1 credits, so 80 companies per month is 8 credits—negligible compared to the revenue impact of catching a churn signal early. The trade-off: if your ACV is under $10k, manual LinkedIn checks are probably enough. But once you're managing six-figure accounts and can't afford to miss a stakeholder change, Ocean.io's API-driven refresh cadence beats spreadsheet archaeology every time.

When Ocean.io's taxonomy beats scraping for a product team's TAM sizing

A 4-person product team at a vertical SaaS company needs to size a new market segment—specifically, how many mid-market logistics companies in the US use Salesforce and have 50-200 employees. Ocean.io's Get Data Field Values tool exposes the taxonomy (industry codes, tech stack filters, headcount brackets) without burning credits, and Lookup Companies returns the actual list at 0.05 credits per match. The team uses Switchy to iterate on filter combinations in natural language, then exports the final dataset for a board deck. The ceiling: if you need real-time data or sub-50-employee granularity, Ocean.io's crawl latency (via Gather Missing Company Data) can add days. But for one-time TAM research where you need 500-2000 records and can tolerate a 48-hour refresh, Ocean.io's structured taxonomy saves a week of manual scraping and gives you a defensible data source for the slide deck.