gtmagency
gtmagency
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Previously ran growth for 500+ SaaS companies through their product launches. Our team has also worked with companies backed by
This playbook maps Kerno's ideal customer profile across account, prospect, and persona dimensions, then lays out 5 signal-based outbound plays. Each play fires on a specific buying signal so your outreach lands the moment a prospect is most likely to act.
| Dimension | Qualified | Prioritization |
|---|---|---|
| Geography | US, UK, Ireland, Canada, Australia, Western Europe | Tier 1: US and UK. Tier 2: Ireland, Canada, Western Europe |
| Employee Count | 5 to 500 employees, with 3 or more backend engineers | Tier 1: 10 to 150 (high AI adoption velocity). Tier 2: 150 to 500 (scaling eng teams with CI pain) |
| Industry / Vertical | B2B SaaS, AI-native product companies, developer tooling, fintech, healthtech | Tier 1: AI-native SaaS and dev tooling. Tier 2: Fintech and healthtech with strict regression tolerance |
| Key Qualifying Signal | Active use of Cursor, Claude Code, GitHub Copilot, or similar AI coding agent in their engineering workflow | Tier 1: Teams publicly referencing AI coding agents in job posts, LinkedIn posts, or repos. Tier 2: Teams hiring for AI-augmented engineering roles |
| Tech Stack | Backend APIs (Node.js, Python, Django, FastAPI, Rails), PostgreSQL or similar DB, GitHub or GitLab, CI/CD pipelines (GitHub Actions, CircleCI) | Tier 1: REST API backends with PostgreSQL and GitHub Actions. Tier 2: Any backend stack with active CI/CD usage |
| Revenue / Funding | Pre-seed to Series B ($500K to $30M raised), or bootstrapped with 5 or more engineers | Tier 1: Seed to Series A with recent funding. Tier 2: Bootstrapped product teams with engineering velocity focus |
| Intent Signals | Hiring backend engineers, posting about AI coding agents, complaining about broken CI or slow PR reviews on LinkedIn or X, recent funding announcement | Tier 1: Active hiring for senior backend or QA roles plus public AI coding tool mentions. Tier 2: Recent funding with engineering team growth |
| Dimension | Qualified | Prioritization |
|---|---|---|
| Seniority | C-Suite, VP, Director, Senior Manager, Staff Engineer | Tier 1: CTO and VP Engineering at 10 to 150-person companies. Tier 2: Director of Engineering and Staff Engineers at 150 to 500-person companies |
| Primary Titles | CTO, VP of Engineering, Head of Engineering, Co-Founder CTO | Co-Founder CTO and VP Engineering at AI-native or early-stage SaaS companies are the highest-priority decision makers |
| Secondary Influencers | Staff Engineer, Senior Backend Engineer, Engineering Manager, Head of Platform, Head of DevOps | Engineering Managers and Staff Engineers who own CI pipelines and code quality standards |
| LinkedIn Keywords | AI coding, Cursor, Claude Code, vibe coding, agentic engineering, LLM-assisted development, GitHub Copilot, backend quality, CI/CD, integration testing, shipping fast | Profiles mentioning Cursor or Claude Code alongside backend engineering or CI/CD topics signal immediate fit |
| Persona | Level | KPIs | Related Challenges | Intent Signals | Related Benefits |
|---|---|---|---|---|---|
| Co-Founder CTO | C-Suite | Engineering velocity, time to ship, production incident rate, team size efficiency | AI agents ship code faster than the team can verify. PR reviews are a bottleneck. Production regressions damage customer trust and slow growth. | Posting about AI coding tools on LinkedIn, hiring backend engineers, referencing vibe coding or agentic workflows | Kerno closes the verification gap so the team ships AI code with confidence, reducing production incidents and unblocking merge queues without adding headcount |
| VP of Engineering | VP | PR cycle time, CI pass rate, on-call burden, developer productivity score | Broken CI runs caused by AI-generated code erode team morale. PR review queues slow release cycles. Regressions discovered post-merge are costly to fix. | Posting about CI failures, slow PR reviews, or AI coding adoption challenges. Hiring for QA or platform engineering roles. | Kerno reduces CI failures and post-merge regressions by validating AI code changes against the live stack before merge, shrinking PR review burden and on-call fallout |
| Engineering Manager | Director/Manager | Sprint velocity, defect escape rate, code review throughput, team satisfaction | Junior engineers using AI agents introduce unpredictable side effects. Manual code review cannot scale with AI-generated output volume. | Job posts requiring experience with AI coding tools. LinkedIn activity around code quality or developer experience. | Kerno gives every engineer the same high-quality verification loop, standardizing output quality across the team regardless of AI tool or experience level |
| Staff or Senior Backend Engineer | Manager/IC | API reliability, test coverage, regression rate, time spent debugging post-merge issues | AI-generated code changes break unrelated endpoints silently. Writing and maintaining integration tests manually is slow and falls behind code changes. | Active on GitHub discussing AI coding tools. Commenting on posts about Cursor, Claude Code, or testing strategies. | Kerno automatically computes the blast radius of every change, catches regressions in the agent session, and self-heals test coverage as the codebase evolves |
| Head of Platform or DevOps | Director/Manager | CI pipeline reliability, deployment frequency, mean time to recovery, infra cost per release | AI coding agents generate higher PR volume, overloading CI pipelines and increasing flaky test rates. Verification gaps slow deployment confidence. | Hiring for CI/CD or platform reliability roles. Posting about GitHub Actions, deployment reliability, or flaky tests. | Kerno integrates into existing CI pipelines and reduces flaky, noisy test runs by validating only what changed, improving pipeline reliability and deployment confidence |
We identify engineering teams actively shipping with AI coding agents before they hit the CI and regression pain Kerno solves. We reach out to the CTO or VP Engineering with a message anchored to the specific AI tool they are using.
Job Post Scrape
LinkedIn Activity Scan
Tech Stack Detection
GitHub Signal Scan
Enrich Company Profile
Find Decision Makers
Verify EmailsIdentify Buying Committee
Email
LinkedInHiring for backend quality or platform roles signals a team scaling AI-assisted development and feeling the verification gap. We contact the CTO or VP Engineering before they invest in headcount Kerno can partially replace.
Job Board ScrapeLinkedIn Jobs MonitorApollo Job AlertsEnrich Account DataFind Engineering LeaderVerify Contact EmailEmailLinkedInWe scrape LinkedIn posts from engineering leaders expressing pain around AI code quality or CI reliability and engage them with a direct, problem-aware outreach sequence. These prospects have already articulated the problem Kerno solves.
LinkedIn Post Scrape
Trigify Signal Monitor
Common Room Social ListenEnrich Post AuthorFind Work EmailQualify Company FitIdentify Buying CommitteeLinkedInEmailFreshly funded engineering teams accelerate hiring and AI tool adoption, creating immediate demand for runtime verification. We reach the CTO or VP Engineering within 72 hours of announcement while velocity and quality are top of mind.
Crunchbase Funding AlertLinkedIn News MonitorExa News ScrapeEnrich Company and StackFind Engineering LeadersVerify Decision Maker EmailIdentify Buying CommitteeEmailLinkedInEngineering leaders actively engaging with Kerno's adjacent competitors are in-market for runtime verification or AI testing. We capture this intent signal and reach out with a Kerno-specific differentiator message before a competitor closes the deal.
Competitor Page Follower ScrapeLinkedIn Engagement ScrapeTrigify Competitor MonitorEnrich Prospect ProfileFind Verified EmailQualify Company ICP FitBuying CommitteeEmailYou'll have qualified demos booked in your calendar within 45 days: infrastructure, sequences, and live campaigns installed for you.
You don't pay a retainer. Everything we build belongs to you from day one: data, playbooks, and infrastructure.
If by day 45 you aren't seeing qualified demos booked in your calendar, we keep working completely for free until you do.
We build and install a fully automated, signal-based outbound system, outbound, ads, and content, in a 45-day sprint. No retainer, and you own everything. Demos booked in 45 days or we work free until they are.
