Qohere-constraint satisfaction engine

Qohere Constraint satisfaction engine reduces SSP Constraint Management Costs by 40%

8/6/2025

How a Constraint Satisfaction Engine Super-charges an SSP’s Bottom Line

Stop losing $0.5-2 M a year on constraint violations. A purpose-built Constraint-Optimization Engine (COE) lifts satisfaction from 97-98 % to 99.7 %, keeps P99 latency under 10 ms, and pays for itself in 3-4 months.

1 Why Violations Really Hurt

Mid- to large SSPs bleed $500 K – 2 M/yr in penalties, make-goods and ops overhead.
Five usual culprits and their typical miss rates:
- PMP guarantees 2-3 %
- Frequency caps 1-2 %
- Competitive separation 0.5-1 %
- Brand-safety rules 0.3-0.5 %
- Budget caps 1-1.5 %

2 Why Rules & ML Alone Stall

ML hits 97-98 % but needs months of history and still chokes on edge cases; rules add +15-20 ms and soak ~200 h/yr in manual tune-ups.

3 Inside the COE

Three-stage pipeline: preprocessing → parallel checks with 80 % cache hit → graph-based conflict resolution (< 10 ms).
Smart math tricks: constraint-graph decomposition cuts complexity 70 %; adaptive LRU + prefetch caching.
Accuracy & speed: 99.7 % satisfaction, 85 % fewer violations, < 10 ms P99 latency

4 Hard-Cash ROI

Annual savings: $300-550 K across penalties, make-goods, ops time.
License: $25-100 K/yr → break-even in 3-4 months, 250 % three-year ROI.

5 Zero-Friction Roll-out

Phase Week Key actions Assessment 1 Violation audit & ROI calc Integration 2 REST API / Prebid.js hook-up Pilot 3 5 % traffic, auto-tuning Roll-out 4 Gradual ramp, success tracking

One node handles 50 K QPS; no infra changes needed; install in 2-3 days.

Constraint debt is real money—far beyond “just ops noise.”
Speed matters: every extra millisecond steals auction wins.
Optimization beats heuristics: tailored math + caching crush generic ML/rules.
Read White Paper on Qohere - Constraint satisfaction engine.