AI SERVICES · VECTOR DATABASE MANAGEMENT
The retrieval layer is where AI systems live or die.
RankSaga designs, deploys, and operates the vector databases that sit underneath production AI. Index architecture, hybrid retrieval, sharding, eviction, and the operational posture that keeps recall and latency stable as your corpus grows.
An AI system can only generate from what it retrieves. The vector database is the layer where that retrieval succeeds or fails, and it is the layer most enterprise teams reach for last and tune the least.
WHY THIS MATTERS
The index is a system, not a configuration screen.
A production vector database is not a feature flag. It is a stateful system with throughput limits, a memory footprint, an indexing strategy, a sharding posture, and a failure mode under load that almost nothing in your existing observability stack will catch by default.
RankSaga treats the vector layer the way an experienced platform team would treat any other production datastore. We architect for the corpus you will have in eighteen months, not the one you have today. We build the indexing pipelines, the hybrid retrieval logic, and the eval harness that tells you when recall has quietly degraded after a re-indexing job.
We work across managed engines like Pinecone and Weaviate Cloud and self-hosted engines like Milvus, Qdrant, and pgvector inside customer VPCs and on-premise environments. Engine choice is driven by residency, latency, cost, and the integration constraints of your existing stack.
WHAT WE SHIP
Six concrete pieces of work.
01 / Capability
Index Architecture
Index design tuned for your corpus shape and query pattern. Dimensionality, distance metric, HNSW parameters, IVF posting lists, sharding keys, and the trade-offs between recall, latency, and memory footprint.
02 / Capability
Hybrid Retrieval
Dense vector search combined with sparse retrieval (BM25, SPLADE) and metadata filtering. The hybrid layer that catches the queries pure semantic search misses and the queries pure keyword search misses.
03 / Capability
Ingestion Pipelines
Batch and streaming ingestion. Chunking strategy, embedding generation, deduplication, change-data-capture against your source systems, and re-indexing flows that do not take the system down.
04 / Capability
Operational Posture
Replication, sharding, backup and restore, monitoring, and alerting. Integration with the observability stack your platform team already runs, not a parallel pane of glass.
05 / Capability
Migration & Engine Selection
Migration between engines (Pinecone to Qdrant, FAISS to Milvus, in-process to managed) without retrieval-quality regression. Engine selection driven by your residency, cost, and latency constraints.
06 / Capability
Recall & Latency Eval
The eval harness that measures recall@k, latency at p50 / p95 / p99, and the regression suite that catches a quality drop the moment a re-index, embedding swap, or schema change introduces it.
HOW WE OPERATE
Embedded with your platform team.
01 / Step
Audit the Existing Layer
We measure what is there. Recall on a representative query set, latency under realistic load, index size and growth trajectory, and the failure modes that have already shown up in production.
02 / Step
Design and Migrate
We design the target architecture against your residency, latency, and cost constraints. We build the migration with shadow indexing and parallel reads so the cutover is reversible.
03 / Step
Operate and Tune
We stay deployed. Monitoring, recall-regression detection, capacity planning, and the next round of tuning as the corpus grows and the query pattern shifts.
STACK POSTURE
Engines and patterns we deploy.
Managed engines
Pinecone, Weaviate Cloud, Vertex Vector Search, Azure AI Search.
Self-hosted engines
Milvus, Qdrant, Weaviate, pgvector, OpenSearch k-NN.
Indexing
HNSW, IVF, IVF-PQ, ScaNN. Metric and parameter selection driven by recall / latency / memory trade-off measurement, not defaults.
Hybrid retrieval
Dense + BM25 / SPLADE, reciprocal rank fusion, learned sparse retrieval, and metadata pre-filtering.
Deployment surfaces
Customer VPC, on-premise Kubernetes, Azure / AWS / GCP managed regions, and air-gapped enclaves where required.
Observability
Recall@k regression, latency percentiles, index health, ingestion lag. Wired into the customer's existing monitoring stack.
PROOF
The retrieval work that backs the BEIR result.
RankSaga's published BEIR benchmarking work tunes the layer below the model. Embedding choice, index parameters, hybrid retrieval, and re-ranking together drove up to 51 percent improvement in retrieval quality across multiple datasets. The same approach scales into customer production environments.
RANKSAGA · BEIR BENCHMARK · 2026
- ·Up to 51 percent retrieval lift on BEIR datasets through index and embedding optimisation.
- ·Open-source RankSaga-Optimised-E5-v2 model on HuggingFace.
- ·Production deployments in customer VPC, on-premise, and air-gapped environments.
- ·Same engineering team that operates AI systems in live ADF deployment.
RELATED CAPABILITIES
Where the vector layer connects.
Adjacent
Embedding Model Optimisation →
The embeddings the index stores. Fine-tuning to your domain corpus for measurable retrieval lift.
Adjacent
Semantic Search & Retrieval →
The retrieval system that sits on top of the index. Chunking, re-ranking, query understanding.
Adjacent
Retrieval-Augmented Generation →
The application layer that consumes retrieval. Grounded generation, attribution, audit.
QUESTIONS
What customers ask before we start.
Which vector database should we use?+
It depends on your residency posture, your cost structure, and the rest of your stack. Pinecone and Weaviate Cloud are excellent if managed-cloud is acceptable and the egress story works. pgvector is the right answer surprisingly often when the corpus fits and the team already operates Postgres. Milvus and Qdrant dominate when self-hosting in a VPC or on-premise. We measure against your constraints rather than recommending an engine in the abstract.
Do we need a vector database at all? Can we use Postgres + pgvector?+
For corpora under roughly ten million vectors with moderate query throughput, pgvector is often the right answer, particularly if the operations team already runs Postgres at scale. We help size the decision honestly rather than recommending a dedicated engine because it is the visible choice.
How do you handle re-indexing without downtime?+
Shadow indexing. The new index is built in parallel against live ingestion, traffic is gradually shifted with parallel reads to compare recall, and only then is the cutover made. Rollback is a config change rather than a recovery operation.
Can you migrate us off an engine we are already using?+
Yes. Engine migration is a common engagement. We run the migration with the same shadow-indexing pattern, validate retrieval quality on a held-out evaluation set, and only cut over once the new engine matches or beats the old one on the metric that matters to your application.
How do you measure that retrieval is actually working?+
Against a labelled evaluation set and a held-out query log from production. We instrument recall@k, MRR, and the application-level metric (task completion, citation correctness) and run them as a regression suite on every change. The eval harness is something you own at the end of the engagement.
ENGAGE
If retrieval is the bottleneck, we want to hear about it.
Most production AI failures we see trace back to the retrieval layer. If your recall is unmeasured, your latency is unstable, or you are not sure your index is sized correctly, an engagement starts with a measurement.