Assaia raises $26.6M to scale AI for airport operations
Assaia closed a $26.6 million Series B led by Armira Growth with support from existing investors. The goal: accelerate AI across airside operations where staffing is tight, margins are thin, and on-time performance is under pressure.
The platform uses computer vision and machine learning to monitor and optimize aircraft turnarounds in real time. Airports, airlines, and ground handlers get earlier signals, better planning, and fewer surprises on the apron.
Why this matters to operations
Traffic is back above pre-pandemic levels, but teams aren't fully staffed. That gap shows up in delays, missed slots, and fragmented coordination.
AI offers a way to regain control without adding headcount. It tightens predictability, improves gate utilization, and reduces manual checks that slow flow.
What Assaia delivers
- Real-time visibility across turnaround events (chocks on/off, catering, fueling, cleaning, boarding).
- Prediction of disruptions before they cascade into delays.
- Automation of alerts, workflows, and handoffs between handlers and airline ops.
- Safety and compliance support through standardized event detection and audit trails.
In practice, that means fewer late pushes, smoother stand changes, and cleaner connections.
Proven at major hubs
Assaia is already deployed at New York JFK, London Heathrow, Dubai International, and Toronto Pearson. Reported outcomes include shorter turn times, stronger on-time performance, and better gate utilization in peak banks.
What's new in this round
The funding will speed global rollout and add new AI-driven modules. A portion is earmarked for the next-generation StandManager, which optimizes gate and stand assignments before aircraft land.
For operations teams, that means earlier, more reliable plans; fewer last-minute swaps; and higher stand throughput in constrained environments. As the CEO put it, "With Armira's backing, we are accelerating the rollout of new technologies and expanding our footprint to deliver measurable value in some of the world's most complex airport environments."
How to operationalize this (practical steps)
- Start with your highest-impact banks and stands; prove value in one terminal before scaling.
- Integrate core data early: AODB, flight schedules, stand rules, turnaround SLAs, resource plans.
- Agree on ownership for each alert type (airline OCC, ramp control, handler lead, gate agent).
- Define playbooks: what triggers an action, who executes, and how it's confirmed.
- Run a 30/60/90-day ROI review focused on OTP uplift, gate utilization, and delay minutes recovered.
Metrics to watch
- Turnaround variance (planned vs. actual, by fleet and stand).
- On-time performance (D0/D15) and primary delay minutes reduced.
- Gate and stand utilization, swap rate, and towing requirements.
- Ground time saved per turn and crew connection protection.
- Safety events detected, resolved, and prevented.
Risks and constraints to plan for
- Data quality: inconsistent timestamps or incomplete task scans will blunt predictions.
- Change management: handlers and gate teams need clear incentives to follow new workflows.
- Union and privacy considerations: align early on camera placement, retention, and usage policies.
- Integration lead times: budget for AODB/stand rule mapping and SOC/IT security reviews.
Bottom line
Airports and airlines are under pressure to move more metal with the same people and gates. Assaia's funding signals growing confidence that AI-driven visibility and planning can deliver that throughput without compromising safety.
If you run airside ops, the move now is to pilot on your tightest banks, wire in the data you trust, and let the numbers decide. The airports listed above are a useful benchmark for scale and complexity.
For standards and best practices in ground operations, see IATA's guidance here.
If you're building AI capability inside your ops team, explore role-based training options here.
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