They entered the US market with no brand, no partners, and no margin for mistakes. Instead of chasing “one more channel”, they spent a year running brutal research, 150+ calls per quarter. The conclusion was uncomfortable; the product sold, but there was no scalable way to grow sales.

So they rebuilt the product into an AI agent. One that connects directly to real business data, explains what’s happening across the funnel, and takes action inside ad platforms.

This episode is also about becoming an AI-first company in practice. Every team, marketing, product, analytics, and sales, works inside Cursor as a shared system. Knowledge base, onboarding, strategy, and daily decisions live in one place and default to AI.

This episode focuses on Eveline Ogorodnikova, Head of Marketing at Elly Analytics and Plurio. As the first marketer in the team, Eveline led Elly through four years of market expansion, failed scaling attempts, and a strategic shift from analytics dashboards to an AI-native performance marketing agent.

Eveline shares what actually worked, what didn’t, and how to run an AI-first company day-to-day:

• Why they made 150+ calls per quarter, and still couldn’t scale revenue
• How interview funnels outperformed classic demo funnels in outbound
• Why dashboards stopped working, and why “answers + actions” became the only scalable model
• How the team rebuilt performance marketing around an AI agent that optimizes campaigns based on revenue, not clicks
• What it really means to build an AI agent that connects to trusted business data, explains changes across the funnel, and takes action inside ad platforms without human micromanagement
• How every team works inside Cursor as an operating system: onboarding, strategy, research, decisions, and context all live in one place
• Why Cursor replaced onboarding docs, internal wikis, and most sync meetings
• How AI changes roles inside the company, fewer manual operators, more strategic thinkers
• Why AI-first companies still need humans, but in completely different jobs than before

If you’re building a complex B2B SaaS, selling to marketers or founders, and feel stuck between “the product works” and “growth doesn’t scale”, this episode will hit close to home.

Watch on YouTube: https://www.youtube.com/@Digital-Hunch

Or listen wherever you get your podcasts.

Learn more about Elly Analytics: https://ellyanalytics.com/

Connect with Eveline on LinkedIn: https://www.linkedin.com/in/eveline-ogorodnikov/

Follow Renata Zinnatullina on LinkedIn: https://www.linkedin.com/in/renatazinnatullina/

Visit our site: https://digital-hunch.com

They entered HR tech at the worst possible moment: the remote-work boom was over, the category was overcrowded, and every keyword was already taken. Yet BuddiesHR still managed to grow, because they tested every hypothesis so radically that, at one point, they even built a competitor to themselves.

This episode focuses on J.Y. Delmotte, the co-founder of BuddiesHR. He explains how they chose to bootstrap after raising $6M in VC funding.

J.Y. shared his sharpest insights — what they did, why they did it, and what results it led to:

• how they entered one of the most overcrowded HR markets in 2023 — when the remote-work boom was over, Slack apps looked interchangeable, and every relevant keyword was already dominated
• why they stayed a two-founder team in a market where everyone hires early — and how this constraint forced them to design a cleaner, faster GTM
• why they spoke to more than 40 HR leaders before writing a single line of code
• how a tiny birthday-automation app became their entry point — and why this lightweight product helped them break through marketplace noise
• how cold outreach collapsed for them from day one
• how Slack Marketplace became their strongest early channel — and why this ecosystem rewarded focus, not breadth
• how they crafted a meme first, a culture-driven tone of voice that speaks only to their best HR buyers, something radically unusual in the corporate HR tools space
• why their AI SEO strategy backfired — thousands of programmatic pages that hurt domain health — and how switching to topic clusters revived their organic traffic
• how their first Google Ads attempt resulted in ~$500 per signup with no ROI, and why their later shift to competitor-keyword campaigns and “X vs BuddiesHR” pages finally started showing early positive signals
• how they improve messaging by watching real visitors interact with the site and letting a 10-member ICP board critique the copy
• how they divide marketing and product between two founders — and why this structure prevents slowdowns even with zero full-time hires
• how ChatGPT became a practical tool for editing, clarity, and risk-control — and why they use it selectively to avoid brand damage
• why they rely on small, fast experiments instead of big, resource-heavy bets — and how this mindset keeps their growth steady while staying fully bootstrapped

Connect with J.Y. on Linkedin. Learn more at BuddiesHR.com

This episode focuses on Sara Archer, Chief Revenue Officer at ChartMogul — a subscription analytics platform used by over 6,000 SaaS companies. Sara joined in 2018 as employee number one on the commercial side, couldn't build a forecast for the CEO on day two because the CRM data was useless, and has spent seven years inheriting more responsibility — from rebuilding the sales stack to now running both sales and marketing as a unified function. She believes demand generation is the hardest non-technical problem in SaaS and that most marketing systems that look healthy on paper are actually dead weight.

ChartMogul built and shipped a revenue recognition product at the customers' request. The sales team dreaded every demo. Support tickets piled up. The product domain — accounting compliance — didn't match anyone's expertise. They decommissioned it. Years later, they built CRM capabilities inside ChartMogul instead, and the difference was immediate: it was fun to sell, customers adopted it naturally, and it made sense as an expansion lever. The lesson wasn't "don't build a second product" — it was that choosing the wrong problem set splits a small engineering team across two intellectual domains and erodes the quality of everything.

On the AI front, Sara's team uses it heavily for data analysis — turning 25 raw sales call transcripts into an objection report in hours, compressing case study production from a week to two and a half hours. But they tried an AI email response tool for product questions and shut it off. It could answer the technical question but couldn't understand why the customer was asking — what business problem sat behind the query. Sara calls this "layers of theory of business" that AI can't yet replicate. She also flagged the "AI tourist" phenomenon from ChartMogul's data: users who sign up for AI products experimentally, with no intent to recur, inflating churn rates across the category.

Sara shares what actually shifted their results — and what she'd rebuild from day one:

↳ What separates the 3.5% of SaaS companies that reach $20M — adaptability and willingness to reinvent, not a better strategy

↳ Why her first move at ChartMogul was rebuilding the CRM — and whether that was the right call or just her comfort zone

↳ The revenue recognition product mistake — how a second product split engineering focus and created a domain expertise gap

↳ How to know it's the wrong product: sales dreads the demo, support tickets take longer, and retention drops

↳ When to move from founder-led sales — and why you should always hire two reps, not one

↳ Why adding sales at low price points creates friction — and how to reverse-engineer the buying process instead

↳ AI for commercial teams — case studies in 2.5 hours, automated call coaching twice a day, and objection trend reports from raw transcripts

↳ Where AI fails in sales — it answers the question but doesn't understand the business reason behind it

↳ The "AI tourist" problem — why experimental signups inflate churn and what to separate in your metrics

↳ Why she dismantled marketing that looked healthy — conferences, content, panels — because it wasn't moving trial numbers

↳ Pricing as a muscle — review it every two to three months, even if the answer is "do nothing"

↳ The simplest scaling advice: listen to three customer calls and the problem becomes obvious

This episode focuses on Sabih Ahmed, Director of Demand Generation at SalesScreen — a sales gamification platform built for revenue teams. Sabih came from B2C marketing, where continuous testing, creative iteration, and audience obsession weren't a methodology — they were just how things worked. When he moved into B2B during COVID, he found a world running on playbooks, intent tools, and the kind of patience for results that B2C would never tolerate.

He spent five years inside SalesScreen rebuilding how demand generation actually works. What he found is that most B2B teams are optimizing the wrong thing: they chase intent signals, automate outreach, and run bottom-of-funnel campaigns at people who aren't close to buying.

In his view, the fix isn't a better stack. It's doing the slow work most founders skip — understanding not just who fits your ICP on paper, but how they behave, what they're actually looking for, and when they're ready to move. Everything else — creativity, AI, channel strategy — only works once that foundation is real.

Sabih shares what actually shifted their results, and what he'd rebuild from day one:

↳ Why B2B marketing defaults to reactive, and why that's a behavior problem, not an AI problem

↳ What the 5% vs. 95% model actually means

↳ Why G2 intent signals aren't "ready to buy" signals, and the campaign failure that proved it

↳ What Sabi borrowed from B2C: weekly testing cycles, hook-first copy, and why creativity outperforms any playbook

↳ Why he killed TOFU/MOFU/BOFU, and what Awareness, Consideration, Conversion looks like when rebuilt around engagement signals

↳ How ad likes and LinkedIn video completion rates became more useful than pricing page visits

↳ Why you can't control a buyer journey, and what "control the controllables" actually means in practice

↳ The ICP mistake most B2B companies make: firmographics only, and how adding a behavioral layer changed their conversion rate

↳ How to use AI as an advisor, not an executor, and why the real failure is always the input, not the output

↳ Why strategy documents don't change execution, and where exactly the connection between vision and campaign breaks down

↳ Why small wins are the only realistic path from $3M to $10M, and how to start with auditing what already worked