Problem
What was broken or inefficient?
Controlled-environment agriculture produces massive operational data, but teams need actionable intelligence rather than raw telemetry.
AI and data infrastructure for controlled-environment agriculture.
Led TerraGrow as a founder/operator, building agritech data systems that connected AI, IoT, cultivation intelligence, dashboards, and business operations.
Role
Founder, CTO, product and engineering leader
Timeline
2018 - 2024
Users
3M+ daily data points processed
Problem
Controlled-environment agriculture produces massive operational data, but teams need actionable intelligence rather than raw telemetry.
Solution
TerraGrow organized agritech data into platform workflows that supported operations, insight, and executive decision-making.
Feature Breakdown
The strongest work sits at the intersection of product judgment, technical architecture, and operating constraints.
High-volume telemetry pipelines
Operational dashboards
AI-assisted cultivation intelligence
Cloud-hosted data systems
Team scaling and delivery operations
Stakeholder and investor-facing product narrative
Outcomes
Lessons
Recruiter Takeaway
Mohammed brings founder-level ownership: he can raise, hire, architect, ship, and translate complex systems into business outcomes.
These are public-safe architecture layers: enough to show leadership judgment without exposing sensitive implementation detail.
Agriculture signals are modeled for operational analysis rather than passive reporting.
Cloud architecture supports high-volume ingestion, dashboards, and system growth.
Technology decisions were tied to hiring, fundraising, demos, and business milestones.
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