👋 Hey — Egemen here.
I think too many founders chase flashy models while ignoring the basics that actually ship. Here's my take on what's working practically right now
Here’s a snapshot of what’s on the menu today:
💡 Spotlight: How AI pricing is shaping operations today
🧠 Deep-Dive: AI Lessons for Founders Grinding
🗺️ Method: How this 8 person team autoamted 18 processes, no developer
⚾️ Catch: Every missed call is a missed opportunity
☝️ Scaled This Past Week: Kredos AI
💡 Spotlight
How AI-Era Pricing Is Reshaping Finance Operations
Usage-based and hybrid pricing models are changing how B2B companies generate revenue — and creating new headaches for the finance teams behind them.
Tabs co-founder Rebecca Schwartz and PwC Partner Amit Dhir sat down to unpack exactly what that means in practice: how pricing model decisions ripple into revenue recognition, forecasting, and financial ops — and what it takes to scale without piling on manual work.
Watch the on-demand recording to get practical frameworks, real-world examples, and a clear path to operationalizing usage-based revenue — including a forward-looking take on how AI will reshape financial workflows. If your team is navigating pricing complexity heading into the back half of the year, this is worth an hour.
Presented by
🧠 Deep-Dive: AI Lessons for Founders Grinding
40% of enterprise apps are expected to embed agents by end of 2026. Don't wait, start now and start narrow.
Pick one painful, repetitive workflow. Broad assistants usually flop.
Shadow the real job 10-20 times. Then spec the agent around clear questions: what wakes it up, what context it needs, which tools, when it escalates. Details win.
One automation first beats nine half-baked ones. Use APIs over slow browser control for better speed and reliability.
Memory and guardrails matter. Shared context plus strict rules cut unpredictable errors. Test recovery from missing data early.
High coding standards now pay off later. Agents inherit patterns. Invest in clean CI and fundamentals.
Post-training evaluation setups often beat raw model size for reliability. Build simulation environments. Enterprises reward consistent outputs. Many agent projects fail here.
Collect user interactions relentlessly. General models commoditize fast.
My blunt opinion: publicly sharing your early systems attracts better feedback and leads quicker than stealth mode. Start with n8n-style tools, ship small, debug live.
👉 Try this this week: map one internal process, build a minimal agent, measure human intervention rate (aim under 20% on repeat runs), then tighten. That's faster learning than another benchmark paper.
🗺️ Method
AlphaSignal's 8-person team automated 18 workflows. No developer.
AlphaSignal runs the most-read technical AI newsletter with an 8-person team. One salesperson installed Viktor for prospect research. Sixty-seven days later it runs 18 workflows across sales, ops, editorial, and finance: proposal builder, deal updates, competitor monitoring, P&L analytics. No developer hired.
⚾️ Catch
While your trucks are running, calls are going to voicemail.
Every missed call is a job your competitor just booked. Podium's AI Employee responds in under 2 minutes, qualifies the lead, and schedules the job — while your crew keeps working.
☝️ Scaled This Past Week
Kredos AI has successfully raised $7 million in a Series A funding round led by BMW i Ventures - it’s the scale of the week!
Kredos AI is an artificial intelligence company that builds an automated platform to optimize the customer payment collection process for large enterprises.
The system combines behavioral science and machine learning to replace generic outreach with dynamic engagement that recovers revenue while protecting long-term customer relationships.