When should businesses use RAG?

When the information received from AI is not trustworthy, and you need accurate answers based on your reliable enterprise data, you can use RAG.

How much does RAG development cost?

A fully-stacked RAG system costs anywhere between $10,000 and $50,000; however, a custom enterprise-grade system can cost up to $140,000.

How long does it take to build a RAG solution?

A basic prototype can be built in a few days, MVPs in a week, but full-fledged enterprise systems can take up to 14 weeks.

Which vector databases do you recommend?

We recommend Pinecone, pgvector, Milvus, Qdrant, and Chroma. The selection of each depends on project scope, use case, and infrastructure.

Can RAG integrate with our existing systems?

Yes. The real-time database sync, capability to combine semantic vector search with traditional keyword search, and querying dynamically via APIs make it easy to integrate with existing systems.

Can RAG access real-time business data?

Yes, RAG can access real-time business data when integrated with live data pipelines, APIs, or real-time embedding updates.

Can RAG replace traditional enterprise search?

No, RAG is a layer that sits on top of traditional retrieval backbones. It can synthesize ranked document lists, files, and links, but keyword search still excels at exact-match lookups.

What business outcomes can we expect from implementing RAG?

You can experience fewer hallucinations and pull exact information from verified document sources, thus enabling operational efficiency and cost savings.

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RAG Development Services for Accurate Hallucination-Free Enterprise AI

Improve customer satisfaction, prevent AI hallucinations, and enhance AI accuracy by training LLMs on proprietary, secure enterprise-specific data.

Improve customer satisfaction, prevent AI hallucinations, and enhance AI accuracy by training LLMs on proprietary, secure enterprise-specific data.

Harness the power of your enterprise’s vast knowledge source through LLMs enhanced with retrieval-augmented generation.

Off-the-shelf generative AI tools have failed to live up to the hype, as they are trained on limited information and produce generic responses. At SHALIGRAM, our RAG development services enable organizations to deliver fine-tuned, cited responses to employees/customers while maintaining retrieval quality and production reliability.

Top consulting firm McKinsey’s research emphasizes RAG as a foundational bridge for the trust gap that enterprises face. We help organizations build RAG systems that scale reliably in real-world complexity.

Challenges we solve with RAG development services

AI hallucinations

We build the RAG data ingestion and grounding infrastructure from scratch, and enforce citations to reduce hallucinations.

Disconnected business knowledge

We develop a unified retrieval layer that connects fragmented data silos without the need to rebuild the existing tech stack.

Information overload

We perform data audits, purge obsolete files, categorize user questions, and ensure high-precision retrieval.

Legacy knowledge systems

We implement non-invasive ingestion and data-remediation pipelines that bridge old infrastructure with modern vector search.

Slow decision-making

We help remove decision bottlenecks, unify critical data, and verify facts by deploying knowledge hubs.

Low employee productivity

Our experts build knowledge assistants and intelligent agentic workflows that eliminate the hours employees spend looking for information.

Retrieval-Augmented Generation (RAG) development services

RAG consulting

We operate at the intersection of business strategy, data governance, and high-level enterprise architecture by focusing on legacy bridging, preventing expensive pilot failures, calculating ROI, and defining corporate guardrails. We calculate the total cost of ownership for RAG infrastructure, prioritize corporate pipelines, and create guidelines to manage “content drift”

Custom RAG application development

We design AI pipelines for your enterprise that safely connect LLMs to your internal, proprietary company data, tailored to specific data and use cases. Custom RAG implementation helps avoid the expense of repeatedly retraining or fine-tuning underlying language models, while protecting proprietary intellectual property and complying with governance frameworks.

Enterprise RAG solution development

We specialize in building highly scalable, secure, and compliant RAG systems tailored to minimize hallucinations and ensure intellectual property safety, while increasing employee productivity. Our core development phase includes advanced data engineering, pipeline setups, and hybrid search and vector architecture development.

AI knowledge base development

We help you achieve context-aware information retrieval through our services that include a rigorous, end-to-end framework designed for your enterprise’s scalability. We identify specific high-value areas, catalog unstructured data sources, and construct automated connectors to ingest data periodically, and implement re-ranking models to surface only the most accurate sources.

Enterprise AI assistant development

We save employee time spent searching through fragmented databases by engineering permission-aware corporate intelligence systems. We follow a structured development lifecycle, engineering pipelines, advanced retrieval infrastructure, and custom agentic architectures to manage the flow of user queries and program specialized sub-agents.

RAG integration

We create reliable, context-aware applications by integrating the RAG retrieval engine with active enterprise directories. We deploy ETL connectors, set up and tune enterprise vector databases, and integrate hybrid search and reranking algorithms to pull the most relevant data points instantly.

Our RAG capabilities that build intelligent AI ecosystems

Intelligent document retrieval

We use a combination of advanced parsing, hybrid search, and precise context reranking to retrieve documents.

Semantic search

We go beyond simple vector matching to ensure contextual relevance and high retrieval precision.

Hybrid search

We build, deploy, and scale specialized architectures and bridge the gap between keyword precision and contextual awareness through hybrid search.

Multi-document reasoning

We implement agentic RAG patterns to close information gaps spread across separate documents.

Real-time knowledge updates

We engineer specific real-time knowledge update architectures to remove lags that result in stale responses.

Source citation

We develop retrieval engines that maintain a metadata chain-of-custody, and every chunk of text is mapped to its precise source.

RAG solutions we build

Our enterprise knowledge assistant solutions are engineered across distinct functional layers to ensure security, high retrieval precision, permissions compliance, and continuous data synchronization.

Our numbers reflect our commitment

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Enterprise use cases by industry

Healthcare

Our services focus on using custom medical tokenizers and chunking methodologies to ensure that complex scientific relationships, drug interactions, and medical acronyms maintain their contextual meaning. We build RAG models that map internal clinical data lakes alongside public research databases and flag low-certainty retrievals to protect clinical integrity.

Clinical knowledge assistant
Medical research & literature search
Patient support chatbot
Hospital SOP & policy assistant
Healthcare documentation search
Medical coding & compliance assistant

Manufacturing

We empower manufacturing industries that grapple with vast archives of outdated engineering blueprints, standard operating procedures (SOPs), and IoT telemetry through RAG systems capable of processing multimodal inputs—such as CAD files, scanned maintenance logs, and schematics.

Production knowledge assistant
Equipment maintenance & troubleshooting assistant
Quality control documentation search
Standard operating procedures (SOP) assistant
Engineering documentation assistant
Supplier & procurement knowledge search

Fintech

Our solutions are designed for the high-velocity data retrieval and extreme accuracy needs of payments apps, digital lending platforms, and trading platforms. We integrate real-time API and database connectors alongside RAG retrieval to surface multi-currency balances and transaction data.

Financial research assistant
Investment knowledge platform
Regulatory compliance assistant
Fraud investigation knowledge search
Customer financial support assistant
Internal policy & documentation assistant

Logistics and supply chain

We develop RAG systems capable of handling highly multimodal inputs and spatial telemetry. We establish Vision-LLM RAG networks for reading unstructured shipping documentation, and develop RAG agents that guide decisions in case of supply chain breakdowns.

Shipment & logistics knowledge assistant
Warehouse operations assistant
Supply chain documentation search
Vendor & procurement knowledge base
Customs & compliance assistant
Fleet operations knowledge assistant

BFSI

We help banks, insurers, and NBFCs deal with legacy core-banking systems, mainframe infrastructure, and intense regulatory audits with BFSI-specific RAG implementations. Our experts develop retrieval algorithms that prevent data leakage by enforcing permission-aware retrieval that filters restricted documents out of the retrieval set before any content reaches the LLM.

Banking knowledge assistant
Insurance claims support assistant
Loan & mortgage documentation assistant
KYC & compliance assistant
Customer service AI assistant
Risk & policy knowledge platform

Real estate

We help real estate companies manage lease portfolios, multi-jurisdictional zoning laws, and property underwriting. We turn this data into high-speed financial and transactional intelligence that flags unusual termination clauses, renewal options, or rent escalation metrics.

Property knowledge assistant
Lease & contract intelligence
Real estate document search
Property management AI assistant
Client inquiry assistant
Legal & compliance knowledge base

Retail and eCommerce

We enable conversational commerce and real-time product catalog & inventory sync along with hyper-personalized recommendations based on past purchases. We integrate RAG with order management systems for shipping updates, tracking, and returns.

Product knowledge assistant
Customer support AI assistant
Inventory & catalog search
Return & refund policy assistant
Vendor knowledge management
Internal operations knowledge base

Food and beverages

We deploy RAG architectures to solve the complex operational, compliance, and supply chain challenges in the F&B industry, and connect LLMs with proprietary ERP, IoT, and regulatory databases.

Food safety & compliance assistant
Recipe & product knowledge search
Restaurant operations assistant
Supply chain documentation assistant
Quality assurance knowledge base
Employee training assistant

Oil and gas

We help the oil and gas sector to connect disparate silos across drilling, production, and supply chain procurement using intelligent routing and knowledge graphs. Our experts build pipelines that process messy data, seismic logs, and enforce role-based access controls.

Technical documentation assistant
Equipment maintenance knowledge base
Health, safety & environment (HSE) assistant
Regulatory compliance assistant
Field operations knowledge search
Engineering standards & SOP assistant

Our RAG development process

Discovery and assessment

We conduct data audits to check data quality, map use cases, and define KPIs.

Data preparation

We connect enterprise data repositories, perform document conversions, and eliminate duplicate versions.

Database setup

We select a multi-tier architecture and configure and organize specialized vector databases.

RAG pipeline integration

We link the database to LLMs and integrate the RAG system into enterprise apps or websites.

Testing

We minimize hallucinations through end-to-end testing and user acceptance testing.

Monitoring

We monitor systems for errors or new data gaps, and refresh the database regularly with the newest enterprise data.

Recent projects & case studies

From startups to enterprises – see how our expertise in software consulting and development in the USA has led to measurable impact across industries.

Custom CRM with live FX rates, telecoms & sales intelligence

Transfer Connex is a UK-based financial services/FinTech company that helps businesses manage international payments, foreign exchange (FX), multi-currency accounts, and business funding.

Industry

Fintech

Technologies

.Net

Challenges

Limitations of off-the-shelf CRMs
Fragmented system operations
Manual data overhead
Lack of call-to-CRM linkage

Business impact

Improved call-to-CRM linkage
High-volume transaction capacity
Accelerated deal execution
Real-time leadership insights

View case study

Custom CRM with live FX rates, telecoms & sales intelligence

Transfer Connex is a UK-based financial services/FinTech company that helps businesses manage international payments, foreign exchange (FX), multi-currency accounts, and business funding.

Industry

Fintech

Technologies

.Net

Challenges

Limitations of off-the-shelf CRMs
Fragmented system operations
Manual data overhead
Lack of call-to-CRM linkage

Business impact

Improved call-to-CRM linkage
High-volume transaction capacity
Accelerated deal execution
Real-time leadership insights

View case study

Why choose SHALIGRAM for RAG development

Building production-grade enterprise knowledge systems

We empower our clients through RAG development services rooted in rigorous data grounding and clear source citations. We are known for implementing dynamic, self-updating retrievals through intelligent enterprise knowledge systems.

Advanced retrieval engineering
Implementation of automated testing frameworks
Ability to orchestrate agentic RAG systems
Compliance with industry-specific regulations

Tired of outdated AI tools that bring up answers your team can't trust?

Deploy our RAG-enhanced enterprise systems to minimize hallucinations by grounding every response in your enterprise data.

FAQs

Trusted by businesses around the world for their digital transformation journey.

In RAG, relevant information is retrieved and added to the model’s input context at the time of query, whereas fine-tuning permanently updates the model’s weights on particular data.