Requirement-Led Architecture
System structure defined around workflows, users and operational needs.
SOLUTION FRAMEWORK
Packaged OutcomeAI Integration & RAG
Qmntra integrates AI assistants, RAG workflows and intelligent product features into existing or new software by connecting models with the right knowledge, data, APIs and business workflows.
SOLUTION ARCHITECTURE
AI Integration Built Around Your Product, Data and Workflows.
We start with the use case, available knowledge, user workflow and existing product architecture before defining how AI should be integrated into the system.
Maintainable Codebase
Clear application boundaries, reusable patterns and readable implementation.
Integration Readiness
APIs, data flows and third-party integrations designed as part of the overall system.
Deployment Discipline
Environment configuration, release checks and deployment processes handled as part of delivery.
SYSTEM TOPOLOGY
AI & RAG System Topology
A practical AI integration connects knowledge sources, retrieval mechanisms, prompt context, intelligence models and product interfaces into a cohesive system.
- 01STAGE 01
Knowledge & Data Layer
Documents, business knowledge, application data and approved information sources used by the AI workflow.
- 02STAGE 02
Retrieval Layer
Search, retrieval and context selection used to bring relevant information into the AI experience.
- 03STAGE 03
Context & Orchestration
Prompt context, workflow rules, application logic and controls that shape how the AI feature behaves.
- 04STAGE 04
AI Intelligence Layer
Language models and AI services used for generation, summarization, classification, reasoning or other defined tasks.
- 05STAGE 05
Product & Workflow Integration
AI capabilities connected into web, mobile, SaaS or internal business workflows where they support the intended product use case.
ENGAGEMENT DELIVERABLES
What We Deliver for AI Integration & RAG
A structured engineering engagement connecting models with your business data, workflows and product interfaces.
01
Knowledge Source Integration
Connect documents, application content, databases or approved knowledge sources required by the AI experience.
Deliverables:
- Structured document & knowledge ingestion
- Application data source connections
- Knowledge refresh & update workflows
02
Retrieval & Context Engineering
Design retrieval, context selection and prompt workflows that supply the right information to the AI feature.
Deliverables:
- Semantic search & context selection
- Prompt context framing & token budgeting
- Relevance filtering & metadata tagging
03
AI Assistant & Workflow Integration
Embed AI assistants, guided interactions and AI-assisted workflows into the existing or new product experience.
Deliverables:
- Embedded assistant UI & conversational flows
- In-workflow AI suggestions & task aids
- Structured response formatting & rendering
04
Tool & API Orchestration
Connect AI workflows with APIs, business systems and product tools where actions or additional context are required.
Deliverables:
- Function calling & tool execution pipelines
- Third-party system & API connections
- Secure payload handling & validation
05
Human Review & Control Paths
Add approval, review or fallback steps where the use case requires human oversight or controlled actions.
Deliverables:
- Approval queues & manual review checkpoints
- Confidence thresholds & graceful fallback paths
- Action logging & audit trails
06
Evaluation & Iteration
Review AI outputs, user flows and system behavior against the intended use case and improve the implementation over time.
Deliverables:
- Output quality review & test scenarios
- Latency, token usage & error tracking
- Iterative prompt & retrieval refinement
DELIVERY FRAMEWORK
A Practical Path from Use Case to AI Integration.
We move from use-case definition and knowledge mapping through integration, validation, deployment and continued iteration.
01
Discover & Architect
Define the use case, users, knowledge sources, workflows and AI integration architecture.
02
Build
Engineer the retrieval, AI and application workflows in focused, reviewable stages.
03
Validate
Test outputs, context quality, integrations and user flows against the intended use case.
04
Deploy & Evolve
Release the AI capability into the intended product environment and improve it as data, requirements and usage patterns evolve.
SOLUTION ENGAGEMENT
Planning an AI Integration or RAG Experience?
Tell us what the AI should help users do, what knowledge or systems it needs to work with, and where it fits into your product. We’ll help define a practical implementation approach.