01
Real-time Pipeline Tuning
The hot path was profiled and rebuilt for throughput:
- Pipelines optimized end to end
- Backpressure handled gracefully
- Peak-load behavior made stable
Case Study · AdTech
Afront:scale without the bill.
A programmatic advertising network with real-time optimization. We tuned the pipelines and bidding logic so transaction volume could grow — while the infrastructure bill did not.
Senior-Only EngineersOnboard in 1–3 WeeksYou Own All IP & Code
Apex in numbers
Results snapshot
0%
Infra cost growth at scale
24h
Response SLA
4
Core technologies
95%
Sprint predictability
About the project
What Afront is.
Afront runs programmatic advertising with real-time optimization: bids are placed, won and measured in milliseconds, and every percent of efficiency shows up directly in margin.
Success created the problem. Throughput growth put the platform's stability at risk, and the infrastructure bill was climbing faster than revenue. The usual answer — throw more servers at it — would have quietly eaten the business. We took the other path: make the existing system radically more efficient.
What we did
The work behind the result.
02
Bidding Logic Optimization
The core auction logic got faster and cheaper per transaction:
- Bidding paths streamlined
- Wasted compute eliminated
- Latency budgets enforced
03
Observability for Critical Services
The team now sees problems before advertisers do:
- Metrics on critical services
- Alerting tied to business impact
- Incident response shortened
Project gallery
Inside the platform.
A look at the ad network our team scaled — tap any frame to view it fullscreen.
Technology
The stack that shipped it.
A Vue front end over a Node.js core, with Kafka moving the events and ClickHouse answering the analytical questions in real time.
VueNode.jsClickHouseKafka
When you need it
Built for moments like these.
If any of these sound familiar, a short discovery call is usually enough to scope the team, the plan and the first milestone.
- Scaled bidding throughput without increasing infrastructure cost
- Preserved platform reliability through transaction volume growth
- Kept cost efficiency as a design constraint, not an afterthought
- Gave the team observability across every critical service
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