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RAG Development Services

Your LLMs are only as good as the data they access. We build retrieval-augmented generation systems that connect AI to your enterprise knowledge - reducing hallucinations by 70-90% and delivering answers your teams can actually trust, with source citations.

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• 70-90%
  Hallucination Reduction
  (Databricks/Anthropic)
• 67%
  Of GenAI in Production
  Uses RAG (McKinsey)
• $5K
  Starting POC for Enterprise
  RAG
• 4.9/5.0
  Clutch Rating
  (34 Reviews)

RAG Solutions We Build

Six retrieval-augmented generation systems purpose-built for enterprise knowledge workflows - from document search to autonomous reasoning.

Enterprise Knowledge Search

Connect LLMs to internal docs, wikis, and policies. Your teams ask questions in natural language and get accurate answers with source citations - no more digging through SharePoint or Confluence.

Customer Support RAG

AI support that answers from your actual documentation - not generic training data. One client achieved 94% accuracy on 50,000+ policy documents, reducing ticket resolution time by 41%.

Legal & Contract Analysis

Extract clauses, compare terms, and flag risks across thousands of documents in seconds. RAG-powered contract intelligence that turns legal review from weeks to hours.

Medical & Clinical RAG

HIPAA-compliant retrieval from medical records, clinical guidelines, and drug databases. Physicians get evidence-based answers at the point of care with full citation trails.

Financial Document Intelligence

Parse earnings reports, regulatory filings, and market research with compliance-ready retrieval. Financial analysts get synthesized insights instead of reading hundreds of pages.

Agentic RAG

RAG systems that don't just retrieve - they reason, decide, and act. Combine retrieval with autonomous agent capabilities for multi-step research, analysis, and decision support workflows.

67% of enterprises using GenAI in production already rely on RAG.

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Why Most RAG Systems Fail in Production

Gartner reports 56% of enterprises cite hallucination as the #1 barrier to AI deployment. Industry data shows 40-60% of RAG projects fail to reach production - not because the technology is flawed, but because teams underestimate 5 critical engineering challenges.

• 56%
  Cite hallucination as #1 AI barrier (Gartner)
• 40-60%
  RAG projects fail to reach production
• 94%
  Accuracy on 50K+ documents
• 300+
  Daily queries in production

Chunking Strategy

Wrong chunking = wrong answers. Semantic, hierarchical, and hybrid approaches are needed for different content types.

Embedding Quality

Choosing between OpenAI, Cohere, and domain-specific models matters more than most realize.

Retrieval Precision

Vector search alone isn't enough. Hybrid search (semantic + keyword + metadata filtering) is required for production accuracy.

Context Window Management

Fitting the right information in limited context windows without losing critical details.

Evaluation & Monitoring

You can't improve what you can't measure. Automated quality tracking in production is non-negotiable.

Our RAG Development Process

Six phases from data audit to production monitoring - with measurable accuracy benchmarks at every step.

1. Discovery & Data Audit
   Assess your documents, data quality, and use case requirements. Identify the right data sources and define accuracy targets. 1 week.
2. Architecture Design
   Choose chunking strategy, embedding model, vector database, and retrieval approach. Design for your specific data types and query patterns. 1 week.
3. RAG Pipeline Build
   Implement the full ingestion, indexing, retrieval, and generation pipeline. Hybrid search, metadata filtering, and source citation included. 2-4 weeks.
4. Evaluation & Tuning
   Measure accuracy, latency, and relevance using Ragas and custom benchmarks. Optimize retrieval precision and answer quality. 1-2 weeks.
5. Security & Compliance
   RBAC, encryption at rest and in transit, audit trails. HIPAA and SOC 2 compliance as needed. On-premise deployment available. 1 week.
6. Deploy & Monitor
   Launch with production monitoring, drift detection, and quality alerts. Continuous accuracy tracking and automated evaluation in production. Ongoing.

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Technology Stack

LLMs

• GPT-4O, CLAUDE, GEMINI, COHERE, MISTRAL

Vector Databases

• PINECONE, WEAVIATE, QDRANT, CHROMA, PGVECTOR

Embeddings

• OPENAI ADA, COHERE EMBED, SENTENCE TRANSFORMERS, DOMAIN FINE-TUNED

Orchestration

• LANGCHAIN, LLAMAINDEX, HAYSTACK, CUSTOM PIPELINES

Infrastructure

• AWS, GCP, AZURE, DOCKER, KUBERNETES

Monitoring

• LANGSMITH, RAGAS, CUSTOM DASHBOARDS

RAG Development Cost

Transparent pricing based on real projects - not vague ranges designed to get you on a sales call.

RAG POC

• $5K - $10K
• 1 - 2 weeks
• Single data source
• Basic retrieval pipeline
• Accuracy benchmarks
• Feasibility report

Production RAG

• $10K - $25K
• 3 - 6 weeks
• Full RAG pipeline
• Hybrid search (semantic + keyword)
• Production monitoring
• Source citations included

Enterprise RAG

• $25K - $45K
• 6 - 12 weeks
• Multi-source retrieval
• RBAC & compliance
• Advanced evaluation
• HIPAA / SOC 2 ready

Agentic RAG

• $45K - $75K
• 8 - 16 weeks
• RAG + autonomous agents
• Multi-step reasoning
• Decision & action workflows
• Enterprise-grade monitoring

67% of enterprises using GenAI in production already rely on RAG (McKinsey).

Case Studies

AI Agent for Supply Chain Optimization

A web and mobile platform to make supply chains more efficient, responsive, and cost-effective. Delivers real-time tracking, flexible route planning, and automated driver assignments.

• Country: USA
• Timeline: 12 months
• Team Size: 8 team members
• Tech Stack: NetworkX, Pyomo, Scikit-learn, PyTorch, Prophet

Transforming Healthcare Conversations

DrTalks needed a way to help doctors connect with patients online - not through generic chatbots, but with intelligent conversations backed by real medical expertise. We built them an AI-powered chat widget that healthcare providers can embed on their websites, giving patients instant access to accurate information while capturing valuable leads for medical practices.

• Country: USA
• Timeline: 2025 – Ongoing
• Team Size: 6 team members
• Tech Stack: React, TypeScript, OpenAI, Next.js, PostgreSQL

Why Companies Choose Softermii for RAG

Frequently Asked Questions

What is RAG and why does it matter?

RAG (Retrieval-Augmented Generation) connects LLMs to your actual data, so they answer based on facts rather than training data. Instead of an LLM guessing or hallucinating, it retrieves relevant documents from your knowledge base and generates answers grounded in that information. This reduces hallucinations by 70-90% and provides source citations so users can verify every answer.

How much does RAG development cost?

From $5K for a proof of concept to $75K for enterprise agentic RAG. A production single-source system typically costs $15K-$25K. Enterprise multi-source systems with compliance requirements run $30K-$45K. We recommend starting with a $5K-$10K POC to validate accuracy on your actual data before committing to a full production build.

How long does RAG implementation take?

1-2 weeks for a POC with a single data source. 3-6 weeks for a production system with hybrid search and monitoring. 6-12 weeks for enterprise multi-source systems with RBAC, compliance, and advanced evaluation. 8-16 weeks for agentic RAG with autonomous reasoning capabilities.

What data sources can RAG connect to?

Virtually any data source - PDFs, Word documents, wikis, databases, APIs, Confluence, SharePoint, Google Drive, Slack archives, email, Notion, and more. We handle both structured data (databases, spreadsheets) and unstructured data (documents, images with OCR, audio transcripts). The key is choosing the right chunking and indexing strategy for each data type.

How do you handle data security?

Encryption at rest and in transit, role-based access control (RBAC), comprehensive audit logs, and optional on-premise deployment where your data never leaves your infrastructure. We build HIPAA-compliant and SOC 2-compliant systems. Vector databases are configured with tenant isolation, and we implement document-level access controls so users only retrieve what they're authorized to see.

RAG vs. fine-tuning - which should we choose?

RAG is the right choice when your data changes frequently, you need source citations, or you want answers grounded in specific documents. Fine-tuning is better for teaching an LLM domain-specific language, tone, or behavior patterns. Most enterprises need RAG first - it's faster to implement, easier to update, and provides verifiable answers. We add fine-tuning when needed for specialized vocabulary or output formatting.

How do you measure RAG accuracy?

We use automated evaluation frameworks including Ragas and custom benchmarks tailored to your use case. Key metrics include faithfulness (does the answer match the source?), relevance (did we retrieve the right documents?), answer correctness, and latency. In production, we run continuous evaluation with quality alerts so accuracy is monitored 24/7, not just at launch.

Do we own the code?

Yes. 100% code and IP ownership. You own the entire RAG pipeline - ingestion, indexing, retrieval, generation, and monitoring code. You can deploy on your own infrastructure, modify as needed, and maintain full control of your data and systems. No vendor lock-in, no licensing fees on code we build for you.