Retrieval-Augmented Generation (RAG) Systems for Real-Time, Contextual AI
Our RAG (Retrieval-Augmented Generation) Systems Service delivers context-rich, real-time responses by combining powerful language generation with dynamic retrieval from trusted data sources. This hybrid architecture overcomes the limits of static LLMs by grounding outputs in relevant, up-to-date enterprise knowledge—making it a high-trust solution for decision support, customer service, research automation, and knowledge management.
Tools & Tech for RAG (Retrieval-Augmented Generation) Systems Service
What It Is
RAG Systems integrate LLMs with a retrieval layer that pulls information from curated databases, internal documents, APIs, or external knowledge hubs at inference time. The result is more accurate, traceable, and tailored responses compared to generic LLM outputs.
Customisable for enterprise needs, RAG Systems can:
- Plug into internal knowledge bases (e.g., SharePoint, Notion, Confluence, custom DBs)
- Be tuned for specific domains (legal, finance, health, customer support)
- Include access controls, relevance scoring, and version tracking
Integrate with pipelines for live data ingestion and content lifecycle automation
Value Across the Customer Journey
Awareness & Research
Accurate content generation that reflects up-to-date facts, helping prospects and clients make faster, informed decisions.
Consideration
Offers context-aware product explanations, pricing comparisons, and support material with citations.
Purchase & Onboarding
Delivers instant onboarding guides, policy breakdowns, and intelligent Q&A that reduce customer effort.
Retention & Loyalty
Creates AI-driven assistants that evolve with the customer, always retrieving the latest knowledge from a single source of truth.
Support & Advocacy
Handles complex queries using real-time documentation, reducing support backlog and increasing resolution rates.
Awareness & Research
Accurate content generation that reflects up-to-date facts, helping prospects and clients make faster, informed decisions.
Consideration
Offers context-aware product explanations, pricing comparisons, and support material with citations.
Purchase & Onboarding
Delivers instant onboarding guides, policy breakdowns, and intelligent Q&A that reduce customer effort.
Retention & Loyalty
Creates AI-driven assistants that evolve with the customer, always retrieving the latest knowledge from a single source of truth.
Support & Advocacy
Handles complex queries using real-time documentation, reducing support backlog and increasing resolution rates.
Value to Business Departments
Marketing
RAG delivers accurate, SEO-friendly content based on live brand or product data, powering campaign copy, briefs, and thought leadership at scale with minimal hallucination risk.
Sales
Equips reps with instant access to playbooks, product details, and competitor insights. Boosts proposal creation and buyer enablement with accurate information.
Accounts (Finance)
Supports internal finance tasks by referencing policies, contracts, billing, and procurement in real time—reducing risk and manual review.
Service / Product Delivery
Ensures agents, partners, or technicians access the latest SOPs, checklists, and compliance data during delivery—on demand, in any interface.
Operations
Automates documentation-heavy tasks, enhances knowledge transfer, and maintains process integrity by syncing AI with source-of-truth systems.
HR
Builds AI HR assistants to answer policy questions, create tailored onboarding plans, and summarise benefits—securely and consistently.
Support
Augments tier-1 to tier-3 support with intelligent, explainable responses based on live product documentation and customer-specific data.
Internal vs. External Value Use
Internal Use
- Enterprise copilots grounded in internal docs
- Decision support tools for legal, compliance, or operations
- Automation of knowledge-intensive internal requests
External Use
- Customer-facing chatbots with grounded, current information
- Dynamic FAQ engines and AI-powered help centers
- Product comparison tools that update in real-time
Value Protection, Enhancement & Creation
| Value | Description |
|---|---|
| Value Protection | Reduces hallucinations and misinformation by grounding AI in verifiable sources, ensuring regulatory and reputational safety. |
| Value Enhancement | Increases trust and user adoption of AI tools by delivering more accurate, relevant responses with transparent sources. |
| Value Creation | Accelerates time-to-insight and enables new services such as AI knowledge portals, research assistants, and contextual sales tools. |
Value Protection
Reduces hallucinations and misinformation by grounding AI in verifiable sources, ensuring regulatory and reputational safety.
Value Enhancement
Increases trust and user adoption of AI tools by delivering more accurate, relevant responses with transparent sources.
Value Creation
Accelerates time-to-insight and enables new services such as AI knowledge portals, research assistants, and contextual sales tools.
Elevating RAG with AI & Tools like Keboola and Make.com
Keboola
By integrating with Keboola, RAG pipelines can be enriched with clean, transformed, and real-time data from multiple sources (ERP, CRM, analytics tools). This ensures that what RAG retrieves is always high-quality, structured, and compliant.
Make.com
Using Make.com, enterprises can orchestrate automated data flows, trigger retrieval actions, or route AI outputs into Slack, email, dashboards, or CRMs—turning RAG into a live-thinking organ across the business.
Together
These integrations turn RAG from a smart system into a self-updating, real-time reasoning engine across the enterprise stack.
Keboola
By integrating with Keboola, RAG pipelines can be enriched with clean, transformed, and real-time data from multiple sources (ERP, CRM, analytics tools). This ensures that what RAG retrieves is always high-quality, structured, and compliant.
Make.com
Using Make.com, enterprises can orchestrate automated data flows, trigger retrieval actions, or route AI outputs into Slack, email, dashboards, or CRMs—turning RAG into a live-thinking organ across the business.
Together
These integrations turn RAG from a smart system into a self-updating, real-time reasoning engine across the enterprise stack.