Oberallmendstrasse 18, 6300 Zug Switzerland
Zaandijkerweg 8, 1521 AX Wormerveer, Netherlands
10 Midford Place London, W1T 5AG, London, United Kingdom
Yigal Alon St 98, Tel Aviv, 6789141, Israel
Two practitioners. Real deployments. No theory. The engineers behind the results — speaking directly to engineering leaders.
Senior engineering leaders responsible for engineering output, vendor strategy, and technology decisions — who need to break the cost-vs-speed trade-off without increasing headcount or budget. CTO, VP Engineering, Head of Engineering.
Engineering productivity — deliver more without growing your team or budget.
How AI-native development applied end-to-end across the SDLC enables teams to ship significantly more per engineer. We'll walk through a real case: a 400-person engineering organisation that achieved 2× output in 11 months — covering what changed in their workflow, where the gains came from, and what conditions made it possible.
How to avoid the AI technical debt trap. Faster AI-assisted development introduces a new class of quality risk — code that works today but becomes expensive to maintain tomorrow. We'll show how automated and AI-driven exploratory QA catches defects in parallel with development, and the guardrails that prevent quality degradation from silently compounding into production incidents.
AIOps — cut incident response time and cost-to-serve in production. How AI agents that identify root causes and suggest fixes are transforming production operations — reducing MTTD and MTTR materially, and freeing senior engineers from reactive firefighting. Includes a real case study on measurable improvements in operational cost and incident resolution speed.
2x
productivity gain
30-40%
reduction in development time per feature
~20%
more efficient delivery vs typical incumbent vendors
Hours→ minutes
Faster incident response resolving production issues in minutes, not hours
Reduce your time-to-market on new features and close the speed gap with AI-native competitors — without adding engineers or increasing burn rate.
Up to 2× delivery velocity.
Redirect engineering spend from repetitive, automatable tasks to high-value product work. Get roughly 20% more output from your current vendor or team investment.
~20% efficiency gain
AI-driven QA running in parallel with development catches issues before they reach production — lowering the cost of defects and reducing the risk of incidents in regulated environments.
Fewer bugs in production
AI-assisted root cause analysis shortens the time between incident detection and resolution — reducing the operational cost and reputational risk of downtime in fintech and logistics systems.
Lower MTTD & MTTR
Automating repetitive coding, testing, and ops tasks frees your senior engineers for complex, high-impact work — reducing burnout and making your team a more attractive place to work.
Higher engineering satisfaction
Walk away with the metrics, frameworks, and real-world proof points needed to justify AI investment to your CFO or board — not just a vision, but a concrete ROI argument.
Measurable ROI framework
We aren't showing you how to write prompts. We are showing you how to re-engineer your entire Software Development Life Cycle (SDLC) for a world where AI is the primary engine, not just a passenger.
Ready to scale your business?
Let's discuss how we can help you grow with AI-enabled solutions and expert engineering
