Organising enterprise data
Enterprise data comes in both structured and unstructured formats, which makes deriving insight hard. QIE provides a robust methodology to organise it and enable the extraction of actionable insights.
Enterprise data in, structured and actionable insight out.
Qinch transforms enterprise data into structured, actionable insights using machine learning, natural language processing, computer vision and large language models — to analyse data, understand context and generate intelligent solutions.
Inside the engine
A novel architecture for knowledge retrieval.
At the core of QIE, AI agents, multiple LLMs, vector representations and graph databases work as one processor — with demonstrated improvements in retrieval accuracy, contextual relevance and information correctness.
Architecture
Agents + multiple LLMs + vectors + graphs
Each layer does what it is best at: agents decompose and check tasks, LLMs reason over language, vectors find similar meaning, and the graph holds how things actually relate.
Measured
Improved accuracy, relevance and correctness
Retrieval is scored, not assumed. QIE has shown measurable gains in retrieval accuracy, contextual relevance and information correctness against baseline approaches.
Expanding
AI web search for knowledge expansion
Internal knowledge is extended and cross-checked with AI web search, so the organisation's brain keeps growing and stays correct over time.
Focused
Topic isolation within retrieval
An effective method for isolating topics inside a knowledge retrieval system, so answers about one subject are not contaminated by another.
What the engine does
Three things QIE does for you.
AI agents for accuracy and reliability
Complex AI tasks are broken into smaller, specialised sub-tasks, each handled by a dedicated agent. These multi-modal agents are self-reflective and corrective, and incorporate deterministic features to deliver consistently accurate results.
Purposeful AI-driven applications
With enterprise data generated continuously, efficient data funnelling and learning is critical. On Qinch's technology, organisations get AI-driven applications that drive informed decisions and operational efficiency.
Evaluation
Every answer is graded on four metrics.
Metric-driven evaluation is what turns a chatbot into a system you can rely on. Each response QIE produces is assessed before it reaches a user.
Traditional models set the stage for conversational AI. QIE adds the methodology that makes answers contextually accurate, relevant and adaptable.
Relevance
The response directly addresses what the user actually asked.
Accuracy
The information in the response is factually correct.
Grounding
Claims are linked back to reliable source documents.
Adequacy
The answer is complete enough to resolve the question.
Deployment
Runs where your data already lives.
Sovereignty
Local infrastructure
All AI processing occurs within your infrastructure. Local AI appliances are available and up to 89% more cost-effective than cloud-based options.
Integration
Existing systems, as they are
Seamless integration with the applications and databases you already run, minimising downtime.
Safety
Tested for bias and safety
Qinch is an accredited member of the AI Verify Foundation and works with Project Moonshot for LLM evaluation and red-teaming.
Talk to us
See QIE running on your own data.
Tell us a little about your systems and we'll come back with how QIE would connect to them — and what a first deployment looks like.
- AI Quick Start engagements
- Our consulting approach: People, Processes, Technology
- On-premise and appliance deployment options