FOMO, Term Sheets and the Coffee Test: Two MENA Seed Playbooks
Key points
Key takeaways from FWDstart interview with MilkStraw AI CEO Jawad Shreim and Orbii co-founder Nauman Ali (September 2026):
Fundraising starts at the first handshake. Both founders treat investor relationship building as a continuous job that starts before a round officially opens, with coffees, intros and fit checks first, capital second.
Preparation turns fundraising into science. Jawad spent four months priming close to 100 VCs across MENA, Europe and the US with a monthly letter-to-himself journal, while Nauman used a structured traction-first newsletter to a curated wish list and treated inbound replies as a qualifier for a call.
Stack the sprint and create FOMO. MilkStraw closed its seed round over 45 days with eight calls a day, a hard closing date of 10 September and a countdown that pushed speed, knowing that funds talk to each other.
Oversubscription triggers repricing. Orbii set out to raise $2m and closed at around $3.6m anchored by a global lead, raising the valuation only after exceeding target to be fair to earlier believers.
Fight liquidation preferences. Jawad's strong advice to founders is not to give liquidation preference, because stacked preferences leave founders with nothing at an exit,and he argues that preferred shares are unfair to founders who risk 5-10 years of their lives.
Publish your own term sheet. Orbii put its crafted term sheet template in its data room so investors reacted to it rather than presenting a blind one, while Nauman scrutinises side letters carefully and advises founders to put their own asks in them too, such as five intros or a bi-weekly cadence.
Time the announcement for inbound. Orbii held its announcement back until after summer, two weeks before the Money 2.0 event where it won Startup of the Year, timed with new product releases, while MilkStraw announced incoming January when people return to their feeds, with funding news driving customers, status and even a flood of CVs.
Cap AI spend at a salary. Both budget AI tooling per discipline at what they would pay a person's salary, run real-time cost agents to tune subscription versus API usage,and test open-source models such as GLM to keep lean teams of around 10-11 people.
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