Perimattic Vector Database Development and RAG Services

Enterprise Vector Database Development Services on AWS

Perimattic helps mid-market and enterprise engineering teams build production-grade AI applications through Vector Database Development Services on AWS. We design, deploy, and manage vector databases that power semantic search, Retrieval-Augmented Generation (RAG), recommendation engines, AI agents, and generative AI applications.

Why Perimattic

Operating since 2017, Perimattic brings deep expertise in cloud infrastructure and AI application development. Our team works across the full vector database ecosystem - including Amazon OpenSearch Service (vector search), PostgreSQL with pgvector, Milvus, Weaviate, ChromaDB, Neo4j, Qdrant, and Pinecone integrations - to match the right technology to your workload requirements.

Engagement Phases and Deliverables

Our structured delivery model ensures predictable outcomes:

Phase 1 - Discovery and Architecture (1-2 weeks) We assess your data landscape, query patterns, and scale requirements. Deliverables include a vector database technology selection report, reference architecture document, and capacity plan.

Phase 2 - Implementation and Integration (3-6 weeks) We deploy your vector database infrastructure on AWS, build embedding generation pipelines, integrate with your LLM or search layer, and configure security controls. Deliverables include deployed infrastructure, RAG pipeline code, and integration documentation.

Phase 3 - Optimization and Handoff (1-2 weeks) We tune indexing parameters, optimize query latency, establish monitoring dashboards, and deliver operational runbooks. Deliverables include performance benchmarks, alerting configuration, and a production readiness report.

Ongoing Managed Services (optional) Continuous monitoring, scaling support, index maintenance, and incident response for production vector database infrastructure.

Core Outcomes We Deliver Faster retrieval - Optimized indexing and query configurations to minimize p99 search latency for similarity queries at scale Higher accuracy - Embedding strategy design and hybrid search tuning to maximize recall and precision for your domain Seamless scaling - Infrastructure designed to handle growing vector collections without degradation, leveraging AWS auto-scaling and distributed architectures Production readiness - Security hardening, access controls, encryption in transit and at rest, VPC isolation, and comprehensive monitoring Reduced time-to-production - Structured delivery phases that move from architecture to live deployment in weeks, not months

Use Case Example

A mid-market SaaS company needed to add semantic document search across millions of customer-uploaded files. Perimattic designed a RAG pipeline using Amazon OpenSearch with vector search capabilities, built an embedding generation workflow processing documents at ingestion, and deployed the solution within a private VPC with role-based access controls. The result was a production search system handling concurrent queries across a large document corpus with sub-second response times.

Prerequisites

To engage with Perimattic, buyers should have an active AWS account, a defined use case for vector search or RAG, and data available (or a plan to generate embeddings). Our team handles infrastructure provisioning, but access credentials and organizational security approvals are the buyer's responsibility.

Security Approach

All deployments follow AWS security best practices including encryption at rest and in transit, VPC network isolation, IAM-based access controls, and audit logging. Customer data remains within the buyer's AWS account and VPC - Perimattic does not retain or transfer customer data outside the engagement environment.

Get Started

Contact us to schedule a discovery call where we assess your vector database requirements and recommend an architecture tailored to your workload.