Real-time AI Data Serving

Deliver Fresh, Low‑Latency Data to Your AI Systems

Online inference needs fresh context and consistent performance. We engineer real‑time data serving stacks that keep your models supplied with the right features, at the right time.

Core Capabilities

• Streaming ingestion via Kafka/Kinesis/Pub/Sub with exactly‑once semantics.
• Online feature computation and lookups (Feast/Redis/ClickHouse).
• Smart caching (TTL + invalidation) for sub‑50ms p95 read paths.
• Multi‑region HA with active‑active failover and traffic steering.
• Backpressure + autoscaling policies tuned to load patterns.
• Observability: RED metrics, distributed tracing, SLO dashboards.

Reference Read Path

1. Request → Edge cache → Online store → Vector/feature fetch.
2. Optional enrichments (profiles, entitlements) via sidecar.
3. Response assembly with guardrails and circuit‑breaking.

SLAs We Target

• p95 latency: < 50ms (cache hit), < 150ms (cache miss)
• Availability: 99.9%+
• Data freshness: ≤ 1 minute for streaming features

Plan your rollout. Schedule a consultation to define SLOs and architecture.