Internal knowledge
Corporate knowledge base Q&A
Slack-native Q&A over wiki, runbooks, Confluence. Hybrid retrieval. Citation enforcement. Freshness monitoring.
↓ 2.3 hrs/wk per IC
We pick and tune the right vector store for your retrieval use case, with hybrid search, freshness, and day-two operations handled, keeping your RAG system accurate.
Three things we sign up to before we write a line of code. All measurable. All agreed upfront.
Dense + sparse + metadata filters + reranker. The reranker (cross-encoder) re-scores the top-k retrieved chunks before they reach the LLM - catching retrieval errors that cosine similarity alone misses. We build this stack on every production RAG system.
We run a comparative benchmark on your actual document types and query patterns before recommending. pgvector if you're already on Postgres and scale is moderate. Weaviate if you need hybrid retrieval native. Pinecone if you want zero ops. FAISS if you own the compute.
Freshness pipelines with staleness alerts. Tombstone and garbage-collection policies. Embedding model version locking. Per-user permission filters for multi-tenant systems. All designed in from sprint one - not retrofitted when the index gets unwieldy.
Most failures trace to retrieval, not the model we fix the store, not just the prompt.
Whoever owns search quality we tune recall and latency against your data.
Knowledge-base Q&A, document retrieval, and semantic search running live.
Slack-native Q&A over wiki, runbooks, Confluence. Hybrid retrieval. Citation enforcement. Freshness monitoring.
12M chunk vectors. Custom pre-filtering by jurisdiction + document type before ANN search.
400k patient vectors. Exact retrieval required for clinical safety. HIPAA-aligned on-premise.
80M product vectors. IVFPQ. Managed cost prohibitive at scale self-hosted on inference cluster.
8M filing vectors. On-premise. No cloud egress for MNPI-adjacent data. HNSW for low latency.
User context + current screen → relevant docs retrieved in < 100ms for in-app copilot.
500M media item vectors. IVFPQ on GPU cluster. No external service for rights-sensitive content.
RAG over helpdesk KB, ticket history, product docs. Hybrid BM25 + dense. Freshness monitoring.
20M code chunk vectors. IVF. Custom post-processing for language/framework filter before results.
We benchmark stores on your data, then ship the one that fits your use case.
Run comparative benchmark on your document types and query patterns. Index type selection. Chunking strategy design. Freshness pipeline design. Schema / namespace design.
Index setup and configuration. Ingestion pipeline with freshness monitoring. Hybrid retrieval implementation. Reranker integration. Retrieval eval harness.
Production deployment. Freshness monitoring. Permission checks. Index lifecycle policy. Runbooks for your engineering team.
The embedding, index, and store choices matched to your retrieval pattern.
Live stores serving accurate, fresh results across real production traffic.
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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A vector store serves as a specialized memory layer in AI applications, efficiently storing and indexing high-dimensional vector embeddings. It enables fast similarity searches, integrates domain-specific data with metadata, and supports scalable, accurate retrieval-essential for enhancing AI performance in tasks like semantic search, recommendation, and contextual understanding.
It serves as the central component in RAG pipelines, grounding language model responses in private, verified knowledge. This dramatically reduces hallucinations and ensures high-fidelity content generation.
Vector stores enable Advanced Semantic Search by converting data into embeddings to understand user intent. This retrieves results based on conceptual similarity, surpassing traditional keyword matching.
They represent users and items in a shared vector space, allowing for instantaneous nearest-neighbor searches. This powers real-time recommendations, driving user engagement and increasing conversion rates.
Vector similarity search is leveraged for analytical tasks such as clustering large datasets to identify groupings, detecting anomalies or outliers, and enabling one-shot learning for classification.
Our approach emphasizes engineering excellence, ensuring systems are performant, scalable, and strategically aligned. We offer vendor-agnostic selection and optimize data ingestion and indexing for your unique trade-offs.
We follow a structured and agile process, which includes a Data Readiness and Feasibility Analysis. This involves building a rapid Proof of Concept (PoC) to validate technical viability and potential ROI upfront.
