Custom RAG Development
Build custom Retrieval-Augmented Generation applications around documents, business data, knowledge bases, APIs, databases, and existing software systems.
RAG DEVELOPMENT SERVICES
RAG Development Services forAI Grounded in Your Data
Buztak Labs develops custom Retrieval-Augmented Generation systems that connect AI models with business documents, knowledge bases, databases, websites, APIs, structured data, unstructured data, and other approved information sources.
Our RAG development services cover RAG architecture, knowledge-base integration, document processing, embeddings, semantic search, hybrid retrieval, reranking, citations, permission-aware retrieval, evaluation, application integration, and production deployment.
01
RAG Development
Retrieval-Augmented Generation systems
02
Knowledge Bases
Business documents and data
03
Hybrid Retrieval
Semantic and keyword search
04
Production RAG
Evaluation, security and integration
WHAT IS RETRIEVAL-AUGMENTED GENERATION?
Connect language models withyour own knowledge
Retrieval-Augmented Generation, commonly called RAG, is an AI architecture in which relevant information is retrieved from external or organization-specific knowledge sources and provided to a language model as context before the model generates an answer.
RAG is useful when an AI application needs to work with information that may be private, frequently changing, organization-specific, too large to place directly into every prompt, or outside the model's original training data.
A RAG system does not automatically make every AI answer correct. Quality depends on source quality, document processing, retrieval strategy, embeddings, search, permissions, model behaviour, evaluation, and application architecture.
RAG in simple terms
1
User asks a question
A user submits a question or task.
2
System retrieves
Relevant information is found from approved sources.
3
Context is provided
Retrieved information is supplied to the AI model.
4
AI generates
The model creates a response using the available context.
5
Sources can be shown
The application can expose source information where required.
RAG DEVELOPMENT SERVICES
Custom RAG development from architecture to production
RAG is not one fixed architecture. The right implementation depends on the type of data, user questions, retrieval requirements, integrations, security model, scale, latency, cost, and expected application experience.
Our RAG development approach can cover consulting, architecture design, data ingestion, knowledge-base development, retrieval engineering, RAG chatbot development, enterprise search, evaluation, optimization, and application integration.
RAG Application Development
Develop production-ready knowledge assistants, enterprise search, document question answering, AI copilots, and business-specific AI applications.
Enterprise RAG Development
Design enterprise RAG systems for internal knowledge, document intelligence, enterprise search, customer support, compliance workflows, and business applications.
RAG Chatbot Development
Create AI chatbots and conversational assistants that retrieve relevant information from approved business sources before generating contextual answers.
AI Knowledge Base Development
Turn documents, websites, databases, wikis, product information, support content, and internal business knowledge into searchable AI knowledge systems.
Document RAG Development
Build document-based RAG systems for PDFs, Word documents, presentations, reports, manuals, contracts, policies, research papers, and other business files.
Semantic & Hybrid Search
Combine semantic vector retrieval, keyword search, metadata filtering, and hybrid retrieval strategies to improve information discovery.
RAG Evaluation & Optimization
Evaluate retrieval quality, relevance, grounding, citations, latency, failure cases, and answer quality to improve production RAG systems.
RAG CONSULTING & STRATEGY
Start with the knowledge problem,not the technology
Some AI applications need RAG. Others may require traditional search, structured database queries, fine-tuning, an AI agent, or a combination of technologies.
RAG consulting helps identify the information users need, where that information lives, how frequently it changes, what retrieval method fits the problem, and how the solution should integrate with the existing product.
01
Use-case discovery
A focused component of planning a practical RAG implementation.
02
Knowledge-source audit
A focused component of planning a practical RAG implementation.
03
RAG feasibility assessment
A focused component of planning a practical RAG implementation.
04
Architecture planning
A focused component of planning a practical RAG implementation.
05
Retrieval strategy selection
A focused component of planning a practical RAG implementation.
06
Security and permission planning
A focused component of planning a practical RAG implementation.
07
Evaluation strategy
A focused component of planning a practical RAG implementation.
08
Integration planning
A focused component of planning a practical RAG implementation.
RAG DATA SOURCES
Connect AI with the informationyour business already owns
Many organizations already have valuable knowledge spread across documents, websites, support systems, databases, internal applications, cloud storage, and business APIs.
A production RAG system should not simply index everything. It should identify useful sources, source ownership, freshness, permissions, synchronization requirements, retrieval patterns, and how the information should be used.
•PDF documents
•Word documents
•PowerPoint presentations
•Excel and structured files
•Websites
•Knowledge bases
•Company wikis
•Notion and similar knowledge systems
•CRM records
•Support tickets
•Product documentation
•Internal databases
•SQL databases
•Cloud storage
•Business APIs
•Research documents
•Policies and manuals
•Contracts and reports
•Application data
•Structured and unstructured data
HOW RAG WORKS
From source data to grounded AI answers
A production RAG application is a pipeline rather than a single vector database connection. Each stage can affect the quality, speed, reliability, cost, and maintainability of the final AI response.
01
Ingest
Connect approved documents, websites, databases, APIs, cloud storage, knowledge bases, business applications, and internal systems.
02
Extract & Process
Extract, clean, normalize, classify, and prepare information before it enters the retrieval pipeline.
03
Chunk
Break source content into meaningful sections while preserving headings, tables, metadata, relationships, and useful context.
04
Embed
Convert appropriate content into vector representations so semantically related information can be discovered during retrieval.
05
Index
Store vectors, metadata, keywords, relationships, and other retrieval information in suitable search or vector infrastructure.
06
Retrieve
Retrieve relevant information using semantic search, keyword search, hybrid retrieval, metadata filters, SQL, APIs, or other appropriate methods.
07
Rerank
Where appropriate, rerank retrieved candidates so the most useful information receives priority before generation.
08
Generate
Provide relevant retrieved context to the language model so it can generate a response grounded in available information.
09
Evaluate
Measure retrieval quality, answer relevance, groundedness, citations, failure cases, latency, and other application-level signals.
RAG RETRIEVAL STRATEGIES
Retrieval quality matters as much as generation
A common RAG implementation mistake is to focus heavily on the language model while treating retrieval as a simple vector lookup. The information selected for the model directly affects the usefulness of the generated response.
Depending on the application, retrieval can combine semantic search, keyword matching, metadata filters, hybrid search, reranking, structured queries, or agent-driven retrieval.
Vector Search
Find information based on semantic similarity when the user's wording differs from the wording used in the source material.
Keyword Search
Match exact terms, identifiers, product names, codes, technical phrases, and other information where lexical matching matters.
Hybrid Search
Combine semantic and keyword retrieval so the system can handle both conceptual questions and exact-match searches.
Metadata Filtering
Restrict retrieval using department, document type, date, tenant, category, region, permissions, or other metadata.
Reranking
Reorder retrieved candidates before generation so the most relevant information receives greater priority in the final context.
Agentic Retrieval
Allow AI workflows to select or combine retrieval tools and knowledge sources when a question requires multiple steps.
RAG ARCHITECTURE
A RAG application is more than a vector database
Production-oriented RAG applications typically involve multiple technical layers. Architecture should be selected around the application's data, retrieval patterns, security, integrations, performance, cost, and operating environment.
Knowledge Sources
Documents, websites, databases, APIs, tickets, wikis, cloud storage, CRM systems, product documentation, and other approved business information.
Ingestion Pipeline
Connectors and synchronization workflows collect information and keep the RAG knowledge layer aligned with approved source systems.
Document Processing
Extraction, parsing, cleaning, chunking, metadata enrichment, table handling, and other preparation required before retrieval.
Embedding Layer
Embedding models convert suitable content into representations that support semantic similarity and retrieval.
Vector & Search Layer
Vector databases, search engines, PostgreSQL with vector capabilities, or hybrid search infrastructure selected around the use case.
Retrieval Layer
Retrieval logic determines which documents, passages, records, or other knowledge should be selected for a question or task.
Reranking Layer
Optional reranking improves the ordering of retrieved candidates before context reaches the language model.
LLM Generation
A language model generates the response using retrieved context, application instructions, safety rules, and output requirements.
Application Layer
Web applications, mobile apps, chatbots, dashboards, APIs, enterprise software, search interfaces, and AI copilots expose the RAG experience.
RAG ARCHITECTURE PATTERNS
Choose the RAG pattern around the problem
Competitor pages increasingly discuss advanced retrieval patterns. We include them here as architecture choices to evaluate rather than promising that one pattern fits every project.
Basic RAG
A straightforward retrieval-and-generation pipeline for focused knowledge access and question answering.
Advanced RAG
Adds stronger preprocessing, metadata, retrieval tuning, reranking, context management, and evaluation.
Hybrid RAG
Combines semantic vector retrieval with lexical or keyword search when both meaning and exact terms matter.
Agentic RAG
Allows an AI workflow to plan retrieval across tools or sources for multi-step questions and tasks.
Multimodal RAG
Extends retrieval workflows to appropriate combinations of text, images, tables, or other supported data types.
GraphRAG / Knowledge Graphs
Can be considered when relationships between entities and multi-hop questions are central to the use case.
Corrective / Adaptive RAG
Retrieval can be validated, refined, or adjusted when the first retrieval pass is insufficient.
Real-Time RAG
Useful where source updates, synchronization, freshness, or near-current information are important requirements.
RAG QUALITY, SECURITY & TRUST
Build for grounded answers,not just impressive demos
A RAG prototype can be created quickly, but production quality requires more than connecting documents to an LLM. Retrieval quality, source quality, access controls, evaluation, failure handling, synchronization, and application behaviour all matter.
RAG applications can also be designed to expose source citations, enforce permissions, measure answer quality, and define behaviour when relevant evidence cannot be found.
01
Grounded Responses
Design generation workflows so responses are based on retrieved context rather than encouraging unsupported answers.
02
Source Citations
Where traceability is required, retrieved documents, passages, pages, links, or other source information can be shown with generated answers.
03
Permission-Aware Retrieval
Design retrieval around application permissions so users only receive information they are authorized to access.
04
Evaluation Sets
Use representative questions and expected outcomes to evaluate retrieval and answer quality before and after system changes.
05
Human Review
For sensitive workflows, include human review where business rules require approval before an action or response.
06
Monitoring & Observability
Monitor retrieval failures, unsupported answers, latency, cost, usage patterns, source synchronization, and other production signals.
RAG VS FINE-TUNING
RAG and fine-tuning solvedifferent problems
RAG is commonly used when an application needs to retrieve current or organization-specific information at query time. Fine-tuning is a different technique that changes model behaviour by training a model on additional examples.
Depending on the product, these approaches can be considered separately or together. The choice should be based on information requirements, behaviour requirements, data, cost, latency, evaluation requirements, and operational needs.
1
Use RAG when
The application needs access to external, private, changing, or organization-specific information.
2
Consider fine-tuning when
The main requirement concerns model behaviour, style, formatting, or another task where additional training examples are appropriate.
3
Consider both when
The application needs specialized model behaviour while also retrieving external business knowledge at runtime.
RAG USE CASES
Where RAG can turn business knowledge into AI experiences
RAG is particularly useful when users need natural-language access to information that already exists across business documents, systems, databases, knowledge bases, APIs, or other approved sources.
Enterprise Knowledge Assistants
Give employees a natural-language interface for finding information across internal documentation, policies, procedures, knowledge bases, and approved business systems.
Customer Support RAG
Ground customer-facing assistants in product documentation, FAQs, policies, manuals, troubleshooting information, and approved support content.
Document Question Answering
Allow users to ask questions about contracts, reports, manuals, research papers, policies, technical documentation, and other large documents.
AI-Powered Enterprise Search
Combine keyword and semantic retrieval to help users find relevant information across large and distributed business knowledge collections.
Sales Knowledge Assistants
Help sales teams retrieve relevant product information, documentation, specifications, approved messaging, and other sales knowledge.
Research Assistants
Retrieve relevant information from defined research sources and organize it into contextual answers, summaries, comparisons, and structured outputs.
Internal Helpdesk
Create AI assistants that help employees find answers from internal IT, HR, operations, onboarding, process, and policy documentation.
Product Knowledge Systems
Build AI interfaces around product specifications, documentation, feature information, support content, and other product knowledge.
Legal & Compliance Knowledge
Create controlled retrieval workflows for approved policies, contracts, regulatory documentation, internal guidelines, and compliance knowledge.
AI Copilots
Connect AI copilots with business knowledge so users can retrieve relevant information while working inside a broader application workflow.
Database-Connected AI
Connect AI experiences with structured business information using SQL, APIs, metadata filters, retrieval tools, or combinations of these approaches.
Document Intelligence
Combine document processing and retrieval to make large collections of business files easier to search, question, compare, and use.
RAG TECHNOLOGY STACK
Technology selected around the use case
There is no single RAG stack that is correct for every application. Models, databases, search technologies, embeddings, APIs, infrastructure, and application frameworks should be selected around the project's requirements.
Language Models
Integration with suitable commercial or open-source language models according to context needs, latency, cost, and deployment requirements.
Vector Databases
Vector-enabled infrastructure selected according to scale, search requirements, metadata needs, existing architecture, and operational requirements.
PostgreSQL & pgvector
Use PostgreSQL with vector capabilities where keeping application and retrieval data within a familiar database architecture is appropriate.
Search Infrastructure
Search technologies can be incorporated where keyword, semantic, hybrid, filtering, or large-scale retrieval requirements justify them.
Embedding Models
Embedding models can be selected according to language coverage, semantic retrieval requirements, domain characteristics, quality, latency, and cost.
Reranking Systems
Reranking can be incorporated where candidate ordering needs additional relevance refinement before context is passed to generation.
Application APIs
RAG applications can expose APIs for websites, mobile applications, internal tools, dashboards, enterprise applications, and other software.
Cloud & Private Infrastructure
Deployment architecture can be designed around security, data residency, scaling, integration, and operational requirements.
AI Agent Integration
RAG retrieval can become a knowledge layer for AI agents that need information before selecting tools, workflows, APIs, or approved actions.
RAG DEVELOPMENT PROCESS
From knowledge audit to production RAG
A successful RAG application starts with the information and questions it needs to handle, not simply with a choice of vector database or language model.
01
RAG Consulting & Discovery
Identify the business problem, users, questions, information sources, expected outputs, existing applications, constraints, and measurable goals.
02
Data & Knowledge Audit
Examine where the required knowledge exists, how it is structured, how frequently it changes, who can access it, and how it can be connected.
03
Architecture Design
Define ingestion, synchronization, processing, retrieval, storage, language models, permissions, application logic, evaluation, and integrations.
04
RAG Prototype
Build a focused prototype using representative data and questions to validate retrieval quality and the intended user experience.
05
Retrieval Optimization
Refine chunking, metadata, embeddings, search strategy, retrieval parameters, reranking, prompts, and context assembly.
06
Application Integration
Connect the RAG system to the required website, mobile application, chatbot, dashboard, API, CRM, database, or enterprise software.
07
Evaluation & Testing
Use representative questions and edge cases to evaluate retrieval, grounding, answer quality, permissions, failure handling, and application behaviour.
08
Production Deployment
Deploy the validated system with appropriate infrastructure, access controls, monitoring, synchronization, and operational workflows.
PRODUCTION RAG DELIVERABLES
Build more than a RAG demo
A production RAG project can include the technical and operational assets required to understand, test, deploy, maintain, and improve the retrieval system.
The exact deliverables depend on scope, data sources, integrations, security requirements, and the application environment.
01
Knowledge-source and data-flow design
02
Ingestion and synchronization workflows
03
Document processing and chunking configuration
04
Embedding and indexing configuration
05
Retrieval and reranking strategy
06
Evaluation questions and benchmark set
07
Citation and permission design
08
Application/API integration
09
Deployment and monitoring guidance
10
Post-launch optimization roadmap
RAG DATA SYNCHRONIZATION
Keep AI knowledge aligned withchanging business information
Business knowledge changes. Product documentation is updated, policies change, new support information appears, databases receive new records, and websites evolve.
A production RAG architecture can therefore include ingestion and synchronization workflows that detect source changes, process updated content, update indexes, preserve metadata, and make approved information available to the retrieval layer.
1Detect approved source changes
2Process updated content
3Rebuild or update relevant indexes
4Preserve source metadata
5Apply access permissions
6Evaluate retrieval after changes
7Expose current approved knowledge
RAG & AI AGENTS
Give AI agents access torelevant business knowledge
RAG can become part of a larger AI agent architecture. An agent may need to retrieve company knowledge before deciding which workflow, tool, API, or action should be used.
Retrieval provides relevant information while the wider application determines what the AI is allowed to do with that information.
Example agent + RAG workflow
1Receive the user's objective
2Understand the task and available context
3Retrieve relevant business knowledge
4Evaluate the retrieved information
5Select an approved tool or workflow
6Generate or execute the required step
7Request human approval when required
8Return the result with relevant source context
RAG DEVELOPMENT CAPABILITIES
Retrieval-Augmented Generation capabilities
RAG applications can combine multiple capabilities depending on information sources, retrieval requirements, application architecture, security, and business workflow.
RAG developmentRAG development servicesRetrieval-Augmented GenerationCustom RAG developmentRAG application developmentEnterprise RAG developmentRAG chatbot developmentAI knowledge base developmentDocument RAGEnterprise AI searchSemantic searchHybrid searchVector searchMetadata filteringRerankingEmbedding pipelinesDocument processingKnowledge-base integrationDatabase-connected RAGAPI-connected RAGStructured data retrievalUnstructured data retrievalAgentic RAGRAG evaluationGrounded generationSource citationsPermission-aware retrievalHuman-in-the-loop workflowsAI application integrationRAG optimization
WHY BUZTAK LABS
RAG connected to the widerAI product ecosystem
RAG rarely exists in isolation. A production AI application may also require a website, mobile application, chatbot, APIs, databases, automation, CRM integration, voice interfaces, AI agents, or other software components.
Buztak Labs approaches RAG as part of the wider application rather than treating retrieval as a standalone technical experiment.
This allows the retrieval layer to be designed around the actual product workflow, user experience, data sources, and integrations.
1Custom RAG application architecture
2Document and knowledge-base integration
3Semantic and hybrid retrieval
4AI chatbot integration
5AI agent integration
6Database and API integration
7Business workflow automation
8Web and mobile AI applications
9Enterprise AI architecture
RAG KNOWLEDGE HUB
Go deeper with our RAG topic cluster
Our RAG service page is the commercial hub. These supporting guides cover the concepts, architecture decisions, and implementation questions users commonly research before choosing a RAG development approach.
What Is RAG?
Understand Retrieval-Augmented Generation, its architecture, benefits, limitations, and where it fits in modern AI.
Read the guide →
How RAG Works
Follow the retrieval-augmented generation workflow from source ingestion through retrieval, context, generation, and evaluation.
Read the guide →
RAG Chatbot Development
Explore how RAG can power chatbots that answer from approved business documents and knowledge sources.
Read the guide →
RAG vs Fine-Tuning
Understand when retrieval, fine-tuning, or a combination of both may be appropriate for an AI application.
Read the guide →
Vector Database for RAG
Learn how vector databases support semantic retrieval and how infrastructure choices affect RAG architecture.
Read the guide →
Hybrid Search vs Vector Search
Compare semantic vector retrieval and keyword-based retrieval, and understand when hybrid search is useful.
Read the guide →
EXPLORE AI DEVELOPMENT
Build the wider AI ecosystem around RAG
RAG can work alongside AI agents, chatbots, automation, generative AI, computer vision, and broader AI application development.
AI Development Company
Explore broader AI development capabilities across intelligent software, automation, generative AI, computer vision, and enterprise applications.
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AI Agent Development
Build task-oriented AI agents that can use retrieval, tools, APIs, databases, and defined business workflows.
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AI Automation
Connect AI capabilities with repeatable business processes and software automation workflows.
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AI Automation Development
Develop custom AI-powered automation systems around business processes, applications, APIs, and data.
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Generative AI Development
Build applications using generative AI for text, images, audio, video, knowledge workflows, and business applications.
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AI Chatbot Development
Create conversational AI applications for websites, applications, customer support, knowledge access, and business workflows.
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Computer Vision Development
Build AI-powered visual intelligence solutions for image analysis, video analytics, monitoring, inspection, and computer vision workflows.
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FREQUENTLY ASKED QUESTIONS
RAG development questions
Common questions about RAG development services, Retrieval-Augmented Generation, enterprise RAG, RAG chatbots, AI knowledge bases, databases, documents, search, citations, permissions, evaluation, and AI applications.
What is RAG development?+
RAG development is the process of building AI applications that retrieve relevant information from approved external knowledge sources and provide that context to a language model before generating a response. RAG can connect AI applications with documents, databases, websites, APIs, knowledge bases, and other business data.
What does RAG stand for?+
RAG stands for Retrieval-Augmented Generation. The retrieval component finds relevant information from an external knowledge source, and the generation component uses that retrieved context to produce a response.
What are RAG development services?+
RAG development services can include RAG consulting, architecture design, data ingestion, document processing, chunking, embeddings, vector search, hybrid retrieval, reranking, knowledge-base integration, RAG chatbot development, evaluation, application integration, deployment, and ongoing optimization.
What is a RAG development company?+
A RAG development company designs and develops applications that combine retrieval systems with language models so AI applications can work with external or organization-specific knowledge. A complete RAG implementation can include data ingestion, retrieval, security, evaluation, application integration, and production deployment.
What is the difference between RAG and a chatbot?+
A chatbot describes the user interaction experience, while RAG describes an architecture that can supply external information to an AI model. A chatbot can use RAG so that its answers are grounded in documents, databases, websites, or other approved knowledge sources.
Can RAG work with PDFs and documents?+
Yes. RAG systems can be designed to process PDFs, Word documents, presentations, manuals, reports, policies, contracts, research material, and technical documentation, depending on the extraction and processing requirements.
Can RAG connect to databases?+
Yes. RAG applications can integrate with databases and other structured data systems. Depending on the use case, information can be accessed through vector search, SQL, APIs, metadata filters, retrieval tools, or combinations of these approaches.
Can RAG use company websites and internal knowledge bases?+
Yes. A RAG system can be designed to retrieve information from approved websites, internal knowledge bases, wikis, cloud storage, product documentation, support systems, and other sources.
What is an AI knowledge base?+
An AI knowledge base is a structured or searchable collection of information that an AI application can retrieve when answering questions or performing tasks. It can include documents, websites, databases, product information, support content, policies, internal knowledge, and other approved sources.
Does RAG eliminate AI hallucinations?+
RAG can reduce unsupported responses by giving the language model relevant external context, but it does not guarantee that every generated answer will be correct. Retrieval quality, source quality, prompts, model behaviour, application rules, evaluation, and other safeguards influence the final result.
What is hybrid search in RAG?+
Hybrid search combines different retrieval methods, commonly semantic vector search and keyword search. This can help a RAG application handle both meaning-based questions and exact terms such as product codes, names, identifiers, or technical phrases.
What is reranking in a RAG pipeline?+
Reranking is a retrieval-stage technique that reorders candidate results according to their relevance to the user's query. It can help the generation layer receive a more focused set of relevant context.
Can RAG systems provide citations?+
Yes. A RAG application can retain source metadata and display document names, links, passages, page references, or other source information alongside generated answers when the underlying data and interface support it.
Can RAG systems respect user permissions?+
Yes. RAG retrieval can be designed with permission-aware filtering so that the information available to a user reflects the application's access-control model. The exact implementation depends on the source systems and security architecture.
Does RAG require fine-tuning an AI model?+
Not necessarily. RAG and fine-tuning solve different problems. RAG is commonly used to provide external knowledge to a model, while fine-tuning can be used for certain behaviour, formatting, or domain-specific model adaptation requirements.
Can RAG be integrated into an existing application?+
Yes. RAG functionality can be integrated into websites, mobile applications, customer support systems, dashboards, internal tools, CRM platforms, enterprise applications, and other software through appropriate APIs and application architecture.
How long does RAG development take?+
The development timeline depends on the data sources, document volume, integrations, permissions, retrieval requirements, evaluation needs, application scope, and deployment environment. A focused proof of concept can be substantially smaller than a production enterprise RAG system.
What is the difference between RAG and fine-tuning?+
RAG provides external information to a model at query time, while fine-tuning changes model behaviour through additional training. RAG is commonly considered when an application needs current or organization-specific knowledge, while fine-tuning may be appropriate for certain behaviour, style, formatting, or task adaptation requirements.
RAG
BUILD A RAG SYSTEM
Turn your business knowledgeinto an AI-accessible system
Tell us what information your AI needs to access, where that information currently exists, what questions users need to ask, and which applications the system needs to connect with. We can help design and develop a practical RAG-powered application.