Ecommerce
Semantic product search at scale
80M product vectors. IVFPQ on EC2 inference cluster. Managed cost prohibitive at this volume.
↓ 89% inference cost vs managed service
Most teams should use Pinecone, Weaviate or pgvector. FAISS is the right choice when you have dedicated GPU or CPU compute, need fine-grained control over index type (IVF, HNSW, PQ, IVFPQ), want to build a retrieval system that's a first-class component of your architecture - or need to run vector search at a scale and cost point that managed services can't match.
Three things we sign up to before we write a line of code. All measurable. All agreed upfront.
Flat, IVF, HNSW or IVFPQ - chosen based on your scale, latency SLO, memory budget and accuracy requirement. We benchmark your data and query pattern before recommending. The wrong index type costs 4x latency or 8x memory.
FAISS handles the ANN search. The production retrieval system needs more: metadata pre-filtering before ANN search, post-retrieval reranking with a cross-encoder, freshness monitoring, and a deletion/tombstone policy. We build the full stack - FAISS is the retrieval core.
faiss-gpu enables GPU-accelerated search for IVF and Flat indexes, with significant throughput improvements for large-scale batch search. We configure GPU acceleration for clients with NVIDIA A100 or V100 instances who need billion-vector search or high-concurrency query throughput.
We see the same failure modes every engagement. Our delivery model is built to avoid all of them.
Most engagements start with one of these six people. The pitch is calibrated to the metric they're judged on.
Across industries. Each with a specific mechanism and a specific metric.
80M product vectors. IVFPQ on EC2 inference cluster. Managed cost prohibitive at this volume.
Vertex Vector Search + Gemini · CMEK · DLP at retrieval layer · freshness monitoring
400k patient vectors. Flat index. Exact retrieval required for clinical safety. On-premise GPU.
500M media item vectors. IVFPQ on GPU cluster. Rights-sensitive no external service.
8M filing vectors. HNSW. On-premise no cloud egress for MNPI-adjacent data.
20M code chunk vectors. IVF. Custom post-processing for language/framework filter.
2B item vectors. IVFPQ. GPU cluster. Zero managed-service cost at this scale.
50M paper chunk vectors. HNSW for high recall. Runs alongside GPU inference cluster.
5M internal document vectors. IVF. Self-hosted confidential documents, no cloud egress.
Benchmark Flat, IVF, HNSW and IVFPQ on your actual data and query mix. Recall-latency-memory tradeoff analysis. Index type recommendation with parameter configuration.
FAISS index build. GPU acceleration setup if required. Pre-filtering integration. Ingestion pipeline with freshness monitoring. Reranker integration. Eval harness.
Production deployment. Sharding config if required. Drift monitoring. Index rebuild pipeline. Runbooks for your engineering team.
We don't force technologies. We choose the stack that best fits your AI workflows, scalability goals, integrations, and long-term product vision.
Carefully crafted AI-powered platforms designed to deliver real business impact, seamless user experiences, and intelligent automation across industries.
Deep teams with industry context - not generalists googling compliance acronyms. Each industry below has 30+ shipped projects and a partner who knows the regulator.
Telemedicine, EHR/EMR, claims automation, clinical decision support. HIPAA, HL7/FHIR, GDPR. Active partnerships with 14 hospital networks.
Core banking, neobank, payments, lending, KYC, fraud. PCI DSS, RBI sandbox, Open Banking, ISO 20022. We've shipped to Tier-1 banks in 4 countries.
Headless commerce, marketplace, omnichannel, AR try-on, AI recommendations. Shopify Plus, BigCommerce, custom. 22+ storefronts live with avg +34% AOV.
Last-mile optimisation, TMS, WMS, fleet IoT, route prediction, real-time tracking. Shipped to UPS, Alod and 11 other logistics operators.
OTT platforms, content recommendation, real-time encoding, multi-DRM, distribution at network scale. Sony Pictures, Hello Baby Direct and more.
LMS, adaptive learning, AI tutors, government portals. Shipped UKIERI for the British Council and 6 state-government education portals.
Real names, real companies, real numbers. Video on the left, written notes on the right - choose whichever feels more honest.
Although regulations prevented the site's launch, it met all requirements in terms of form and function. Fullestop's project plan charted a clear course to completion. The team's flexible, diverse talent pool enabled them to manage each stage of the project with consistent levels of skill.
Weekly demos, no surprises, and they push back when we're wrong. That last part is rare. Cut our cloud bill 47% in the first audit.
We constantly come up with top-tier resources and breathtaking
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FAISS provides raw, unmediated performance and is the computational core for massive search systems. It allows for the specialized, low-level engineering needed to achieve sub-millisecond responses in the most demanding enterprise applications.
It enables the backend for real-time recommendation systems that operate over massive catalogs. FAISS executes nearest-neighbor searches across billions of vectors in milliseconds, vital for large-scale e-commerce and streaming platforms.
Semantic caching is an intelligent layer using FAISS to index query embeddings. It instantly identifies similar requests to serve a cached response, significantly reducing operational costs and the need for costly LLM API calls.
The core value is architecting the optimal index for your needs. This involves meticulous tuning to achieve the ideal balance between search speed, recall (accuracy), and hardware cost for the application.
We specialize in architecting systems that use FAISS's GPU support for unparalleled query throughput. Our process minimizes CPU-GPU data transfer bottlenecks, enabling true millisecond-latency search on massive indexes for real-time applications.
We follow a structured, agile process: from Discovery and Feasibility PoC to Solution Architecture. The process culminates in rigorous testing, MLOps deployment, and continuous monitoring for sustained operational value.
