Our reps now spend their time selling instead of researching. Kernel gives us custom enrichment embedded in the CRM.
Philip Front
Director, GTM Systems & Architecture
Account data in EMEA is notoriously unreliable. With Kernel we’re improving the accuracy of critical account data, enabling us to target the right accounts.
Markus
Director Revenue Operations
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Kernel beat all other data providers on accuracy.
Josephine
Director of Sales Development
Testimonial →
Kernel is critical to our sales strategy by ensuring everyone in the revenue function is aligned and focused on the right accounts of the right size.
Greg Johnson
Chief Sales Officer
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Nailing our target accounts globally is my top priority.
Chris
Director, Global Revenue Operations and Enablement
Testimonial →
With Kernel, we can be confident that our target market is well-defined and fully researched, allowing us to prioritise accounts by need, resulting in higher sales velocity.
Alun
Head of Revenue Operations
Replicate your best sales rep’s research for every account.
Kernel improves and validates core firmographic enrichment accuracy with multiple high-context and high-fidelity data sources. Where generic enrichment uses single sources to identify headcount, revenue, location, and the age of a company, Kernel uses all public information to develop customized and accurate enrichment. Combining unstructured and structured data enables Kernel’s complex estimations and prioritization models. Kernel’s master data accuracy ensures hallucinations and inaccuracies are not added to the CRM.
Kernel Headcount: Kernel determines headcount by first checking primary sources like the company's website, PDFs / Annual reports, official registries (10Ks/10Qs), and Wikipedia. If no direct evidence is available, Kernel uses LinkedIn data as a fallback. Kernel’s AI module cross-checks the LinkedIn headcount against the company's self-reported size and adjusts the figure based on the company's industry, country, and growth rate. If no LinkedIn is available, Kernel uses other data points available about the company on the web to provide an accurate headcount figure such as registries and employment disclosures.
Kernel Revenue: When available, Kernel sources revenue figures directly from primary sources like annual reports (10Ks/10Qs), official filings, company website, news, and Wikipedia. If a number is not publicly reported, Kernel uses a custom estimation model that analyzes factors like industry benchmarks, company type, web traffic, and headcount to produce an estimate.
Before Kernel
After Kernel
Custom Account Enrichment
Kernel translates publicly available data points into CRM fields and builds precise estimation models for non-public characteristics. Kernel mirrors the intuitions of experienced sales reps at scale, crawling all public data sources to retrieve niche insights. Data sources like regulatory databases or company reports enable higher-context enrichment.
RevOps leaders use Kernel tier accounts based on granular ICP criteria. Kernel’s AI Custom Enrichment integrates firmographics, technographics, and non-standard enrichment fields into complex models that customize account data enrichment based on business-specific requirements.
Kernel’s estimation engine processes multiple high-context data sources to extract custom data points and build estimates for insights like cloud spending, technology adoption patterns, and organizational characteristics. In the cloud spend example, technology signals such as AI/ML team composition, tooling sophistication, and cloud service adoption patterns are used to estimate privately held information.
Sales rep validation provides continuous feedback loops where prospecting insights correct estimation inaccuracies and flag contradictory intelligence. Kernel’s custom enrichment relies on transparency and iterative improvement. The feedback integration ensures that theoretical models align with practical sales intelligence to maintain estimation reliability and business relevance.
Before Kernel
After Kernel
Custom Verticals
Kernel's custom vertical classifications enable RevOps leaders to expand CRM vertical pick lists beyond generic categories to reflect precise ICP criteria. The system addresses the limitations of NAICS, LinkedIn, and standard classifications that are too broad and force sales reps to work through generic account lists.
The classification engine crawls company company LinkedIn profiles and websites for deeper context, while leveraging Kernel's database to identify and group businesses under customized sub-vertical categories.
Kernel starts with the main vertical identification, followed by sub-vertical refinement, business model determination, and company type specification. Each classification level includes transparent reasoning that references specific company characteristics, product offerings, partnerships, and regulatory relationships to justify the categorization decision. Kernel applies both positive classification for target segments and anti-vertical exclusions to filter out adjacent entities that may appear similar but fall outside the ICP scope.
The classification process integrates structured data extraction with contextual analysis of business operations, enabling accurate segmentation across complex industry landscapes. Each account receives detailed reasoning documentation that explains the classification logic, supporting both RevOps decision-making and sales rep understanding of account categorization.
Before Kernel
After Kernel
Prioritization & Reasoning
Prioritization and Reasoning transforms how RevOps leaders tier accounts by combining AI-powered analysis with human expertise. The process begins with custom Kernel research agents that automatically discover high-signal data points specific to your ICP, moving beyond basic firmographics to capture nuanced indicators.
The module establishes a foundation of logic-based ICP rules that capture clear qualification criteria, such as "If company has 500+ employees AND shows 30%+ headcount growth, then classify as Tier 1." This rule-based foundation is then enhanced by flexible AI reasoning that analyzes all available data points, including internal CRM notes, recent pipeline activity, and strategic documents, to make nuanced tiering decisions that human logic alone might miss.
Every account receives a tier ranking (1-5, from Target to Ignore) accompanied by human-readable reasoning that lists specific positive and negative factors influencing the decision. Built-in guardrails ensure logical constraints are never violated—for example, "If company is in excluded industry, it can never be Tier 1"—while SDR feedback mechanisms allow real-world insights to continuously refine the model's accuracy.
Rep feedback creates a self-improving prioritization engine that becomes more precise over time.
Before Kernel
After Kernel
Close the gap between GTM strategy slides and CRM lists.
Kernel turns strategy documents like Revolut's 2024 Annual Report into structured, prioritized CRM lists that RevOps teams can activate to generate pipeline efficiently.
How RevOps leaders apply Kernel to outbound sales:
Activate new verticals, new products, or new markets efficiently.
Cut generic bloated account lists to focus outbound on ICP
Eliminate manual research and list building.
Kernel builds ICP account lists, covering your entire TAM in under 30 days.
Focus on the niche data points that guarantee a prospect has the problem you solve. Take the initiative with custom enrichment whilst your competitors work through long lists of generic accounts.
Recent M&A
Concentration of knowledge workers
Recently hired CISO
Recent M&A
Concentration of knowledge workers
Recently hired CISO
Subscription model
Sales model
Number of customers
Recently funded, but not listed on Crunchbase
Subscription model
Sales model
Number of customers
Engaged in import/export activities
Target group ("Who they sell to")
Engaged in import/export activities
Target group ("Who they sell to")
Offers shipping insurance
Employee, jobs, and office distribution
International expansion initiative
Recent market expansions
Offers shipping insurance
Employee, jobs, and office distribution
Digital Payments & Fintechs
Traditional Banking
Investment Management
Digital Payments & Fintechs
Traditional Banking
Investment Management
HealthTech
ClimateTech
EdTech
PropTech
MarTech
HealthTech
ClimateTech
EdTech
PropTech
MarTech
Corporate Law
Intellectual Property Law
Family Law
Criminal Defense Law
Corporate Law
Intellectual Property Law
Family Law
Criminal Defense Law
Custom verticals.
Segment broad industry descriptions to ensure pipeline generation efficiency. Map out opaque vertical descriptions like "Software development" into niche sub-verticals like EdTech or Martech.
Deploy AI on accurate data foundations.
A clean CRM with accurately mapped corporate hierarchies is a prerequisite for deploying AI at scale. Apply the same enrichment criteria for accounts in your CRM to sourcing net-new accounts from outside of your CRM.
AI Cleaning & Hierarchies
Fix your master data, de-duplicate accounts using AI and build corporate hierarchies with custom definitions to match your GTM strategy. Once the CRM is clean, Kernel keeps it that way.
Map your entire TAM and apply complex filters to find companies that truly match your ICP criteria. Add net-new accounts to your CRM without creating duplications.
Kernel removes the complexity of deploying AI in enterprise CRMs
Test Kernel’s custom data enrichment with a free proof of concept. Benchmark Kernel's core firmographics against your current provider, test more complex custom data insights, or segment account lists with custom vertical classifications.
Today:
Book a demo and define your granular ICP criteria.
Next:
Share a sample list of accounts for Kernel to customize.
Review:
Review the customized account list returned by Kernel.
Deploy:
Roll out custom account enrichment across all accounts in the CRM.
With Kernel, we can be confident that our target market is well-defined and fully researched, allowing us to prioritise accounts by need, resulting in higher sales velocity.
Nailing our target accounts globally is my top priority.
Chris
Director, Global Revenue Operations and Enablement
Kernel Guarantees
30-Day Implementation
48-Hour Resolution
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