Tenor
AI workers that attribute the outcome back to the token spend
About Tenor
Your AI spend becomes accountable labor: every AI worker gets a job, persistent context, a budget, guardrails, and outcomes you can measure. Copilots made individuals faster. Agent builders made automations easier to create. Both leave the organization unchanged: same roles, same capacity, same org chart, and leadership still sees AI as a software expense, not labor. Tenor makes AI enter the company as labor instead: every employee manages a team of AI workers built around the recurring work of their role, each with a defined job, boundaries, a budget, and a record of what it delivered. Employees provide the judgment. AI workers provide the execution. Leadership finally sees what AI spend returns.
Founders
Amgad Al-Zamkan
Founder
Co-founder @ Tenor. Previously at Amazon, studied CS at UC Berkeley and TU Munich. As a grad student, Amgad's research focused on optimization objectives in recommendation algorithms and shaping adaptive decision systems.
Hamze Al-Zamkan
Founder
Co-Founder @ Tenor. Giving AI a place in the org chart to scale companies without adding headcount. As President of TUM.ai, he scaled it into Europe's largest student-led AI initiative. Spent his university years at TUM and CDTM working at venture-backed startups to apply those learnings to a company of his own.
Muhtasham Oblokulov
Co-Founder
Co-founder @ Tenor, building the future of AI-native work. Early contributor to DeepSeek and co-author of the highly cited StarCoder series and CodeClash with Stanford University. Previously deployed large-scale AI systems across global reinsurance markets.