RAG Development Services for AI You Can Trust
Off-the-shelf LLMs do not know your contracts, processes, or customers. Kanerika builds RAG systems that ground every answer in your own data, so your AI gives accurate responses.
Efficiency gain from AI
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Get Started with RAG Devlopment Solutions
Build Context-Aware AI with Expert RAG Solutions
We design the retrieval, ranking, and grounding systems that turn a base model into an enterprise application.
RAG Architecture Design
- Design retrieval flows around real user queries
- Select embedding models for enterprise data needs
- Architect hybrid search across structured and unstructured data
Data Ingestion Pipelines
- Connect structured and unstructured data sources
- Chunk and prepare documents for accurate retrieval
- Maintain pipelines for continuous content updates
Retrieval Engineering
- Configure vector databases for enterprise scale
- Tune relevance through hybrid search and reranking
- Optimize retrieval accuracy across real user queries
LLM Orchestration Layer
- Integrate open-source and commercial foundation models
- Design prompts and orchestration for grounded responses
- Coordinate retrieval, reasoning, and tool-use layers
Evaluation & Guardrails
- Measure retrieval and answer quality continuously
- Detect hallucinations before responses reach users
- Apply policy guardrails on all the sensitive topics
Deployment & Oversight
- Deploy RAG to cloud or on-premise environments
- Monitor accuracy, latency, and retrieval performance
- Maintain systems with retraining and tuning cycles
Engineering RAG Solutions for Enterprise-Scale AI
We build RAG-powered AI systems that connect business knowledge with scalable retrieval and response workflows.
Data Preparation
- Consolidate all knowledge sources
- Clean and structure business data
- Create searchable vector embeddings
Retrieval Engineering
- Build semantic search pipelines
- Configure intelligent document retrieval
- Optimize contextual response accuracy
AI Integration
- Connect retrieval with LLM workflows
- Enable context-aware AI responses
- Deploy scalable enterprise AI applications
RAG Implementations Delivering Business Impact
See how RAG helps organizations operationalize AI with trusted data.
Transforming Legacy QlikView Reporting into Real-Time Power BI AnalyticsÂ
Impact:
- 70% Reduced reporting maintenance
- 80% Faster data refresh & reporting cycles
- 40% Lower infrastructure & licensing costs
Enhancing Brand Compliance and Approval Workflows with Conversational AI​
Impact:
- 35% Higher Brand Compliance Rate
- 60% Lower Approval Turnaround Time
- 70% Less Manual Effort Per Brand QueryÂ
Enhancing Compliance Oversight with an AI-Powered Regulatory Management Platform​
Impact:
- 40% Faster Regulatory Response
- 60% Less Manual Compliance Work
- 5X Better Audit Traceability
Tools and Technologies
Built using modern AI, vector search, cloud, and enterprise data technologies.
INNOVATE
Diverse Industry Expertise
Why Choose Kanerika for RAG Development Services?
Kanerika builds production-ready RAG systems grounded in your enterprise data, secured for regulated industries
Kanerika has built and deployed enterprise RAG-powered AI, including DokGPT and Karl, both running in production today.
Kanerika’s RAG builds connect to your existing enterprise data sources, including Fabric, Synapse, and SQL platforms,
Role-based access, audit trails, and data masking are part of the architecture from day one, not retrofitted later.
Empowering Alliances
Our Strategic Partnerships
The pivotal partnerships with technology leaders that amplify our capabilities, ensuring you benefit from the most advanced and reliable solutions.
Frequently Asked Questions (FAQs)
01What is RAG and how is it different from standard LLM implementation?
Standard LLM integration uses the model’s baked-in training knowledge — static, potentially outdated, with no access to your internal data. RAG retrieves relevant content from your documents or databases at query time and feeds it to the model as context. The model generates a response grounded in what your organization actually knows, with citations traceable to specific source documents.
02When should we use RAG instead of fine-tuning?
Use RAG when your data changes frequently, when compliance requires source traceability, or when document volume is large. Use fine-tuning when you need the model to adopt a specific tone or domain vocabulary and the underlying data is relatively static. Many enterprise production systems combine both.
03How long does a RAG engagement take?
A focused use case — single document type, defined query scope — typically takes 8 to 12 weeks from architecture through production deployment. Multi-source, cross-system, or regulated-industry deployments run 3 to 6 months. Kanerika’s advisory phase (4 to 6 weeks) produces a production blueprint and effort estimate before the build starts.
04How does Kanerika handle data security in RAG deployments?
Role-based access controls are built into the retrieval layer — users only retrieve what they’re authorized to see. Every retrieval event is logged for audit. PII detection and redaction happens before document ingestion via Susan (kanerika.com/product/ai-agent-for-pii-redaction/). Azure deployments run within your existing Microsoft security perimeter with private endpoint configuration.
05Can the RAG system integrate with Microsoft Teams or existing enterprise apps?
Yes. DokGPT deploys via Microsoft Teams and WhatsApp. Custom RAG systems expose retrieval capabilities as APIs consumable by Salesforce, ServiceNow, SAP, SharePoint, or any API-connected enterprise application.
06How do you keep the RAG system accurate as documents change?
Incremental indexing pipelines update the vector index as source documents change — no full re-indexing required for most updates. We also implement retrieval freshness scoring and configure document expiry rules for time-sensitive content. Post-deployment monitoring tracks retrieval drift and triggers alerts when accuracy degrades.
Deploy Context-Aware AI with RAG
Partner with Kanerika to build enterprise-grade RAG applications .