Debugging agents meant replaying sessions by hand. ML updates took a day to validate. Now every step is traceable in real time.
A Qatar-based super-app with 8,000+ merchant partners and 7,000+ drivers, running production AI agents for shopping assistance and automated catalog management across the Gulf Cooperation Council.
As AI agents and ML models became central to the product, the team had no real-time visibility into how they behaved in production. Engineers...
“Our goal is to make Snoonu the most intelligent and reliable super app in the region, and AI is at the center of how we are getting there.”
On-demand food delivery, grocery shopping, and e-commerce super-app in Qatar.
Observability and security platform for cloud-scale monitoring and analytics.
Snoonu's Production monitoring is part of this use case:
Related implementations across industries and use cases
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Agent failures at 1M ops/day meant engineers stitching logs, traces, and code to diagnose. Now every decision links to a commit in one view.
Novus's agent drew on analytics or code—never both—invisible to the team. Traces caught it; 60% of problems now caught before customers.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Agent failures at 1M ops/day meant engineers stitching logs, traces, and code to diagnose. Now every decision links to a commit in one view.
An aging homegrown CRM held back 1,700 agents. Now, an AI co-pilot helps them draft faster, higher-quality emails from a single screen.
Managers spent hours manually calling applicants, losing many to delays. Now, an AI agent screens and auto-schedules interviews via text.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Debugging agents meant replaying sessions by hand. ML updates took a day to validate. Now every step is traceable in real time.
A Qatar-based super-app with 8,000+ merchant partners and 7,000+ drivers, running production AI agents for shopping assistance and automated catalog management across the Gulf Cooperation Council.
As AI agents and ML models became central to the product, the team had no real-time visibility into how they behaved in production. Engineers...
“Our goal is to make Snoonu the most intelligent and reliable super app in the region, and AI is at the center of how we are getting there.”
On-demand food delivery, grocery shopping, and e-commerce super-app in Qatar.
Observability and security platform for cloud-scale monitoring and analytics.
Snoonu's Production monitoring is part of this use case:
Related implementations across industries and use cases
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Agent failures at 1M ops/day meant engineers stitching logs, traces, and code to diagnose. Now every decision links to a commit in one view.
Novus's agent drew on analytics or code—never both—invisible to the team. Traces caught it; 60% of problems now caught before customers.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Agent failures at 1M ops/day meant engineers stitching logs, traces, and code to diagnose. Now every decision links to a commit in one view.
An aging homegrown CRM held back 1,700 agents. Now, an AI co-pilot helps them draft faster, higher-quality emails from a single screen.
Managers spent hours manually calling applicants, losing many to delays. Now, an AI agent screens and auto-schedules interviews via text.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.