Vantage
Powers sub-80ms vector-native search across 54M e-commerce SKUs where inventory and pricing change faster than batch indexes can rebuild.
Engineering highlights
The core architectural breakthroughs.
High-scale engineering demand distilled into four verified production milestones.
Full inventory indexed with custom HNSW vector partitions in Go
Sub-80ms p95 latency held through Cyber Monday traffic surges
Debezium Kafka streaming updates vectors without read-lock pauses
Zero-result natural language queries slashed from 14.8% to 0.8%
Executive overview
The engineering challenge & solution.
Shoppers search with natural conversational intent ('boho floral summer dress with sleeves under $80 that ships immediately'). Traditional keyword search yielded zero results for complex queries, while batch-built vector search engines lagged 4 hours behind real warehouse stock, causing customers to buy out-of-stock items and resulting in high return rates.
We engineered an asynchronous hybrid search platform combining custom HNSW vector partitions in Go with Elasticsearch inverted indexes. Real-time PostgreSQL database mutations are captured via Debezium CDC and streamed through Kafka into an in-memory vector indexing layer with zero read locking.
Engineering governance
The three non-negotiables.
These are the architectural constraints that shaped every boundary — no trade-offs or compromises permitted.
Sub-80ms p95 Latency at 10k QPS
Serve semantic search queries during Black Friday traffic surges without exceeding 80ms p95 latency.
Sub-60s Index Freshness
Reflect warehouse stock and flash-sale price updates in the vector search index in under 60 seconds via CDC.
Zero Degradation During Indexing
Live read performance must not degrade while thousands of product vectors are concurrently written.
Incremental Vector Index Updates at 10,000 Price & Stock Mutations/Second Without Read Lock Contention
Standard vector indexes (like Faiss or pgvector) require global locking or expensive graph restructuring when updating vectors at high velocity, causing search queries to spike to 600ms latency.
We partitioned the 54M SKU index into immutable memory-mapped segments and an active append-only write buffer. Vector similarity scoring executes concurrently across static segments while a lightweight bloom filter checks the write buffer for real-time inventory adjustments. Segments are merged asynchronously in the background.
Request lifecycle
How data moves through Vantage.
Production verification
Measured outcomes in production.
Every metric below was captured under real production traffic and audited against historical baseline data.
Operational resilience
Automated safety guardrails.
Systems fail in production. Here is how Vantage survives network partitions, upstream timeouts, and anomalous inputs without human intervention.
Automatic Read-Replica Autoscaling
KEDA autoscaler adds replica pods when QPS spikes above 8,000.
Fallback Lexical Engine
If vector inference times out under extreme load, queries fall back to BM25 in 15ms.
Zero-Downtime Blue-Green Swap
Major index re-embeddings swap atomic memory pointers without dropping traffic.
Next step
Building a system with this level of demand?
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