ML Data Platforms
Feature stores & vector indexes for AI.
AI teams bleed time on data plumbing. We stand up feature stores, embedding pipelines and vector indexes as first-class data products — versioned, monitored and owned by the platform team.
<10 ms
Online feature read latency (p99)
100%
Training-serving feature parity
1B+
Vectors indexed in production
Full
Dataset lineage & versioning
What we ship
Capabilities
The data plumbing AI actually needs — feature stores, training-serving parity, vector indexes and embedding pipelines wired into your lakehouse.
- 01
Online & offline feature stores
Feast or custom feature stores with training-serving parity — the same feature values at training time and serving time, every time.
- 02
pgvector / Qdrant / Milvus indexes
Vector indexes designed for your embedding dimensions and retrieval SLOs — HNSW tuning, filtering, and replication configured for production.
- 03
Embedding & retrieval pipelines
Batch and real-time embedding pipelines that backfill historical data, stay current with new records and handle schema changes without downtime.
- 04
Training-set lineage & versioning
Every training dataset version tracked — what rows it contained, what time it was cut, which model was trained on it — reproducible by anyone.
Outcomes
- Consistent features across training and serving
- Vector retrieval at production latency
- Reproducible training datasets
Tech we use
In the field
- 1
Credit scoring feature store
300+ features serving 50k credit decisions per day — training-serving parity confirmed, model performance gap closed by 8 points.
- 2
Swahili semantic search
1.2B document chunks indexed in pgvector — sub-20 ms retrieval for a legal research platform.
- 3
Recommendation engine
User and item embeddings updated in real time, served from online store at 5 ms p99 — CTR up 22% vs. batch-refreshed baseline.
How we deliver
Our delivery process
Every engagement follows the same rigorous four-stage approach — so you know exactly what to expect, and when.
- Step01
Feature audit
We inventory every feature your models use today and map training-serving skew — usually the first time anyone has done this.
- Step02
Store design
Online store (Redis / DynamoDB) + offline store (lakehouse tables) designed for your feature cardinality and freshness requirements.
- Step03
Vector index sizing
HNSW parameters, segment sizes and replication factors tuned to your corpus size and QPS targets — tested under realistic load.
- Step04
Pipeline operationalization
Embedding jobs scheduled, backfill completed and monitoring in place — feature platform handed to your ML team with runbooks.
Ready to get started?
Build ml data platforms for your product
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