Technology

Product-driven technology choices.

Nexisy uses modern technologies according to product requirements. Architecture and tools are selected for scalability, reliability, security, performance and maintainability — not for their own sake.

The stack

What our products are built with.

The exact stack differs per product. These are the building blocks we work with across the portfolio.

Cloud & Infrastructure

  • Cloud platforms
  • Containerised workloads
  • CI/CD pipelines
  • Monitoring and observability

AI & Retrieval

  • LLM services
  • RAG architectures
  • Vector databases
  • Embedding and ranking pipelines

Applications & APIs

  • Modern web applications
  • REST and service APIs
  • Microservices
  • Background processing

Data Platforms

  • Relational and document stores
  • ETL and streaming pipelines
  • Analytics models
  • Reporting layers

Identity & Security

  • Authentication and authorisation
  • Role and tenant isolation
  • Secret management
  • Secure configuration

Engineering Practice

  • Automated testing
  • Code review
  • Versioned releases
  • Documentation as part of delivery
Principles

How decisions get made.

01

Product-driven, not technology-driven

Architecture and tools are selected for the problem in front of us. Scalability, reliability, security, performance and maintainability decide the stack.

02

Separate products, shared standards

Each product keeps its own authentication, billing, data and dashboards. What they share is engineering standards, not a tangled codebase.

03

Grounded intelligence

Where AI is used, it works from retrieved, trusted context so that answers can be traced back to real source material.

Architecture

How a Nexisy RAG system is put together.

Retrieval comes before generation. Source material is ingested and indexed, the relevant passages are retrieved for a question, and only then is an answer generated from that context.

01

Ingest

Documents and knowledge sources are collected, parsed and chunked.

02

Index

Chunks are embedded and stored in a vector index alongside their metadata.

03

Retrieve

A question is matched semantically against the index to find relevant context.

04

Generate

The answer is produced from retrieved context so it stays grounded in real sources.

Technical conversation

Want the architecture detail?

We are happy to walk through how a specific platform is designed, deployed and operated.

Contact NexisySee the products