Your best account research is sitting on public web pages. You're just not reading them fast enough.
Here's what happens before every sales call at most companies: Your rep opens LinkedIn. Spends 10 minutes skimming the prospect's profile. Checks the company's homepage. Googles for recent news. Scans a press release. Copies a few bullet points into their notes. Walks into the call with a surface-level understanding that sounds exactly like every other vendor.
Now multiply that by 15 calls a week. That's 150 minutes of manual research — per rep — producing the same shallow insights your competitor's rep found in the same 10 minutes.
Meanwhile, the prospect's website, blog, job postings, case studies, investor updates, and product changelog are all sitting in the open. Rich, specific, actionable intelligence that would make your outreach and conversations dramatically better. But nobody has time to read it all.
Account research at scale is the largest unsolved bottleneck in B2B sales. Not because the information doesn't exist — because no human can process it fast enough.
Today, that changes.
nRev now integrates directly with Firecrawl. Any website, any page, any public data source gets scraped, structured, and fed into your revenue workflows automatically. From raw web page to actionable account brief. From "I Googled them" to "I know their tech stack, their hiring velocity, their latest product launch, and exactly which pain point to lead with."
No browser tabs. No copy-paste research. No generic prep.
Why Web Data Is the Missing Layer in Your GTM Stack
Your sales stack has contact data (Apollo, RocketReach). It has intent signals (Bombora, G2). It has CRM data (Salesforce, HubSpot). But it's missing the richest, most specific source of account intelligence that exists: the public web.
Think about what's freely available on a prospect's website alone: their product positioning (what they think their problem is), their hiring pages (where they're investing), their blog (what they care about), their case studies (who they sell to), their leadership team (who you're selling to), and their changelog (how fast they move).
Now think about what's available across the web: press releases announcing funding rounds, conference talks revealing strategy, G2 reviews exposing pain points, job descriptions listing their tech stack, industry reports naming them as players.
All of this is public. All of it is relevant. None of it is in your CRM.
The research gap is real: Reps spend 5-6 hours per week on pre-call research, and still walk into meetings under-prepared. The solution isn't "research harder" — it's automating the extraction of web intelligence and piping it directly into workflows.
Most teams can't use web data because their process looks like this:
Rep opens browser → Visits 4-5 pages → Skims for relevant info → Copies into notes → Forgets half of it → Delivers generic pitch
With nRev + Firecrawl, the same intelligence is gathered in seconds:
nRev triggers Firecrawl on target URL → Firecrawl extracts clean, structured content → nRev parses for relevant signals → nRev compiles account brief → Rep gets full intelligence package before the call
Same information. Different speed. Different depth.
What Firecrawl Brings to the Table
Firecrawl is the web data API built for the AI era. It doesn't just scrape HTML — it converts any website into clean, structured, LLM-ready data.
Universal Extraction: Firecrawl handles JavaScript-heavy sites, dynamic content, PDFs, and single-page applications. If a human can see it in a browser, Firecrawl can extract it. No broken scrapers. No "this site uses React so we can't read it" excuses.
Structured Output: Raw HTML is useless for workflows. Firecrawl returns clean markdown, structured JSON, or extracted data fields — ready to be parsed, scored, and actioned by nRev without additional processing.
AI-Powered Parsing: Tell Firecrawl what you want in natural language. "Extract the company's mission, founding year, and leadership team from this about page." It returns structured data, not a wall of text.
Scale Infrastructure: Built-in proxy rotation, anti-bot handling, rate limiting, and caching. Firecrawl handles the messy infrastructure of web scraping so your workflows don't break when a site changes its layout.
The difference between Firecrawl and building your own scraper? Reliability and format. Firecrawl is the only web data API designed to output content that AI systems can actually reason about. nRev uses that structured output to power intelligent revenue workflows.
What nRev Adds: From Web Page to Revenue Action
Firecrawl extracts the data. nRev decides what it means for your deal — and acts on it.
Trigger-Based Scraping: nRev doesn't scrape randomly. It fires Firecrawl when it matters: before a scheduled call, when a new account enters your pipeline, when a prospect visits your website, or on a recurring schedule for target accounts. The right data at the right moment. See how connections work →
Intelligent Parsing: Raw web content is noisy. nRev's AI reads Firecrawl output and extracts the signals that matter for sales:
- From a careers page → hiring velocity, department investment, tech stack mentions
- From a product page → positioning, ICP, competitive landscape
- From a blog → strategic priorities, thought leadership themes, recent milestones
- From a press release → funding, partnerships, executive changes
Contextual Assembly: nRev combines Firecrawl data with your existing stack — CRM records, Apollo contact data, intent signals — into a unified account brief. No more toggling between 6 tabs.
Multi-Channel Action: Web intelligence triggers downstream plays:
- Pre-call briefings delivered to Slack 30 minutes before meetings
- Personalized outreach drafted using specific insights from the prospect's own content
- CRM fields updated with tech stack, company stage, and competitive intel
- Account scoring adjusted based on web signals (hiring = growth = higher priority)
Five Playbooks That Turn Web Data into Deals
Playbook 1: The Automatic Account Brief (Win Every First Call)
The Scenario: Your AE has a discovery call at 2pm with a VP of Marketing at a mid-market fintech. She's done six of these calls today. She'll spend 8 minutes googling the company, skim their homepage, glance at a LinkedIn profile, and walk in with the same prep as everyone else.
The nRev Workflow:
Trigger: nRev detects a calendar event — meeting with external attendee in 30 minutes
Scrape: Firecrawl hits 4 URLs in parallel: company homepage, about page, blog (last 3 posts), and careers page
Parse: nRev extracts key intelligence:
- Company: B2B fintech, Series B, 120 employees, raised $28M
- Product: expense management platform for mid-market companies
- Hiring: 3 engineering roles, 2 sales roles — scaling both product and GTM
- Blog: Recent post about "building for enterprise" — they're moving upmarket
- Tech stack (from job descriptions): Salesforce, Outreach, Snowflake
Compile: Assemble into a structured briefing delivered to Slack:
- "Meeting Brief: [Company] — B2B fintech, Series B ($28M), 120 employees. Moving upmarket (blog signals enterprise push). Scaling sales team (2 open roles). Running Salesforce + Outreach. Lead with pipeline visibility angle — their current stack doesn't solve the reporting gap they'll hit at enterprise scale."
Deliver: Arrives in rep's Slack DM 30 minutes before the call
The Outcome: Every first call sounds like a fifth call. Your rep leads with specific, relevant insights instead of "So, tell me about your company." Prospects notice the difference. Win rates on first calls improve 20-30%.
Playbook 2: The Competitive Intelligence Monitor (Know Before They Switch)
The Scenario: Your top 50 target accounts all use a competitor. You need to know the moment something changes — a new product complaint, a pricing update, a leadership departure — so you can time your outreach to the moment of maximum disruption.
The nRev Workflow:
Schedule: nRev runs weekly Firecrawl jobs on competitor websites, product changelogs, and pricing pages
Detect: Firecrawl's change tracking flags updates: "Competitor X increased enterprise pricing by 30%" or "Competitor Y removed feature Z from their standard plan"
Score: nRev identifies which of your target accounts are most affected by this change (current competitor customers, accounts in evaluation)
Draft: Competitive displacement outreach:
- If pricing increase: "Noticed [Competitor] just restructured their pricing. If you're re-evaluating, here's how we compare at your scale — we've helped 3 companies migrate this quarter."
- If feature removal: "Saw [Competitor] sunsetted [Feature]. If that was core to your workflow, we built [Alternative] specifically for teams in your situation."
Alert: Slack notification to competitive deal owners with change summary and drafted outreach
The Outcome: You're the first vendor to reach out when competitors stumble. Instead of hoping prospects come to you during evaluation, you create the evaluation moment.
Playbook 3: The Hiring Signal Prospector (Read Job Posts as Buying Signals)
The Scenario: A target account's careers page tells you more about their priorities than any intent data provider. They're hiring a "Head of Revenue Operations" — which means they're building the function. They're hiring "5 SDRs" — which means they're scaling outbound. Every job posting is a decoded buying signal.
The nRev Workflow:
Crawl: Firecrawl scrapes careers pages across your target account list weekly
Extract: nRev parses job postings for signal keywords: role titles, team size, tech stack requirements, seniority, department
Interpret: Map hiring patterns to buying intent:
- Hiring RevOps = building infrastructure = tool evaluation likely
- Hiring 5+ sales reps = scaling pipeline = visibility/enablement needs
- Hiring a CRO = strategic shift = new budget, new priorities
- Tech stack in job description mentions competitor = active user, potential switch target
Action: Route based on signal strength:
- New RevOps hire + no existing tool = outbound sequence with "building your stack" angle
- Scaling sales team = outbound with "pipeline visibility at scale" messaging
- Competitor mentioned in job post = competitive displacement play
Alert: Weekly digest to BDR team: "12 target accounts posted relevant roles this week. Here's the priority list with recommended outreach angles."
The Outcome: You decode buying intent from public job postings before any intent data provider flags the account. Hiring signals often precede tool evaluation by 60-90 days — you're first in line.
Playbook 4: The Prospect Website Personalizer (Outreach That Proves You Did the Work)
The Scenario: Your SDR team sends 200 cold emails per week. Open rates are fine. Reply rates are dying. The problem isn't deliverability — it's relevance. Every email sounds like it could be sent to anyone. Because it was.
The nRev Workflow:
Trigger: New prospect enters outbound sequence in CRM
Scrape: Firecrawl hits the prospect's company website — homepage, about, and one relevant product/service page
Extract: nRev pulls 3-4 specific details: what the company does, who they serve, a recent milestone or initiative, and their positioning language
Draft: Outreach that uses their own world:
- Instead of: "Hi [Name], we help companies like yours improve pipeline."
- Write: "Hi [Name], saw [Company] is expanding into mid-market financial services — that's usually when the outbound motion needs to shift from founder-led to repeatable. We helped [Similar Company] make that transition in 6 weeks."
Quality check: nRev flags if Firecrawl couldn't extract enough detail (bare-bones website, under construction) and falls back to LinkedIn-based personalization
The Outcome: Every outbound email references something specific about the prospect's business. Reply rates improve 40-60% over templated sequences. SDRs stop sounding like bots.
Playbook 5: The Event Intelligence Engine (Own the Room Before You Walk In)
The Scenario: You're sponsoring a conference next month. You have the attendee list — 500 names. You need to know which 30 are worth your AE's time and exactly what to say to each one.
The nRev Workflow:
Ingest: Upload attendee list to nRev
Enrich: Apollo/RocketReach for contact details and company basics
Deep Research: Firecrawl scrapes each attendee's company website, pulling positioning, recent news, and product details
Score: nRev ranks attendees by ICP fit + web intelligence signals (funded recently? hiring aggressively? using a competitor?)
Compile: For each top-30 prospect, generate a one-paragraph briefing card:
- "[Name], VP Sales at [Company]. Series B fintech ($18M raised Q3). Scaling outbound — 4 SDR roles posted. Currently using [Competitor] based on job descriptions. Recommended angle: migration path from [Competitor] + pipeline visibility for scaling teams."
Distribute: Briefing cards delivered to reps via Slack or Google Sheet before the event
The Outcome: Your team walks into the conference knowing exactly who to find, what to say, and why it matters to them. Every conversation is targeted. Event ROI goes from "we collected business cards" to "we booked 12 meetings."
The Difference: Browser Tabs vs. Automated Intelligence
Built for Who Actually Uses This Data
Account Executives: You're tired of walking into calls unprepared. nRev delivers complete account briefs to Slack before every meeting — built from the prospect's own website, not a generic database.
SDR/BDR Teams: Your outreach is competing with 50 other cold emails. Firecrawl-powered personalization makes every message reference something specific about the prospect's business. Stand out or get deleted.
RevOps at Scale: You manage target account lists of 500+. Manual research doesn't scale. nRev + Firecrawl automates the intelligence layer so your team can focus on selling, not Googling.
Competitive Intelligence: You need to know when competitors change pricing, features, or messaging. Firecrawl monitors their public pages and nRev alerts you the moment something shifts.
Setup: 15 Minutes to First Workflow
What you need: Firecrawl API key (free tier available at firecrawl.dev), nRev account, 15 minutes.
Step 1: Connect Firecrawl API in nRev (view connection docs →)
Step 2: Choose your first use case: pre-call briefings, outbound personalization, or competitive monitoring
Step 3: Configure scraping targets:
- For briefings: company URL pulled from CRM, triggered by calendar events
- For personalization: prospect website URL, triggered by new outbound records
- For monitoring: competitor URLs, triggered on weekly schedule
Step 4: Define parsing rules (what to extract from each page type)
Step 5: Connect output to actions: Slack alerts, email drafts, CRM field updates
Step 6: Test with a sample account and review the output
Total time: 15 minutes to first workflow. Compare to building a custom scraping pipeline with Puppeteer + GPT + Zapier: days of engineering work that breaks every month.
Read full setup documentation →
The Bottom Line
The best account research already exists on the public web. Firecrawl extracts it. nRev turns it into the briefings, personalization, and competitive intelligence your team needs to win.
Intelligence without execution is just overhead. Firecrawl + nRev closes the gap between "it's all online" and "it's in my rep's hands before the call."
You already have the information. Now put it to work.
Ready to automate your account research? Connect Firecrawl to nRev →
Questions about your specific research workflow or target account list? Our team builds your first playbook with you — free, no sales pitch.