ScalaCode is a renowned RAG development service provider that has delivered 500+ innovative solutions for 1000+ clients across 45+ countries. From data preparation and vector engineering, customer retrieval and pipeline architecture, system integration and orchestration, to compliance, we specialize in managing a client’s end-to-end RAG lifecycle while ensuring accuracy and compliance.
ScalaCode’s suite of RAG development services covers a wide range of services, including custom RAG app development and RAG strategy consulting to deployment. Our expertise delivers measurable ROI while solving real-life, industry-specific business challenges.
We start RAG development by accessing enterprise data, breaking silos, and converting documents into vector embeddings using databases such as Pinecone, Quadrant, or Milvus. Building decoupled pipelines, such as LangChain and LlamaIndex, sends queries to the appropriate LLM while allowing the client to choose from different models, such as OpenAI and Anthropic.
ScalaCode begins custom RAG app development by building a tailored AI solution where an LLM accesses the client’s knowledge base and internal documents to collect information using ETL pipelines and store it in a vector database. Hybrid search and reranking send relevant data to the LLM. This results in a production-ready, enterprise RAG solution for clients.
Our RAG AI chatbot development process begins with connecting the LLM with enterprise and real-time data using ingestion pipelines and vector indexing, which is stored in vector embeddings. Semantic/hybrid research delivers the most relevant and accurate answer to the LLM. Integrating the app with APIs, implementing role-based access controls (RBAC), and protecting sensitive data ensures security and top-notch performance.
We develop RAG (Retrieval-Augmented Generation) pipelines by extracting data from structured and unstructured sources, such as PDFs, schematics, tables, and legal documents, using deep document parsing techniques. Contextual processing enables AI to understand the meaning and context. Documents are further split into smaller chunks, enhanced with metadata, and stored in vector databases to be used by RAG systems when required.
To understand context and improve retrieval, we combine lexical keyword (BM25) and semantic (vector) search, while using cross-encoder reranking and metadata, which keeps information relevant and accurate. Implementing Graph RAG, Agentic RAG, and Multimodal RAG architectures facilitates better information retrieval and multi-source data processing.
Includes deployment of real-time input/output filters to edit/remove personal identifiable information (PII) before sending/reaching LLM APIs. We build and manage the Retrieval Augmented Generation (RAG) infrastructure while ensuring compliance with HIPAA, GDPR, SOC 2, and PCI-DSS standards. LLMS are configured in a way that users can verify claims by accessing clickable and audible source links with the exact page numbers of documents.
When building RAG (retrieval-augmented generation) systems, we use frameworks such as Ragas, TruLens, or DeepEval to determine whether answers are accurate, relevant, and match the context. Embeddings, prompts, and a knowledge base are fed with the latest enterprise data, resulting in reliable and source-based answers.
For domain-specific RAG model development, we fine-tune the embedding models, combine vector searches with knowledge graphs, and re-rank the data to meet diverse industry needs. We finish by implementing data chunking, adding relevant metadata and optimizing retrieval for various formats/languages while ensuring regulatory compliance.
ScalaCode’s multi-language and multi-modal RAG solutions use cross-lingual embeddings like mE5 and vision-language models like CLIP to collect different data types, convert them into vectors, and store them in a single database. Queries in different languages/formats pass through semantic retrieval pipelines that understand context (not just meaning) to the LLM (large language model), which generates final responses.
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Retrieval is not limited to a single operation at the start of the process. After evaluating user intent, agentic controllers split questions into sub-queries and run more retrieval attempts. It determines whether the context is relevant, and if not, it performs additional retrieval attempts. Best for complex, multi-part, or comparative queries.
Combines hybrid sparse-dense search, metadata filtering, and cross-encoder reranker, along with context compression. This is the default 2026 enterprise baseline. Delivers 20 to 40 percent more accuracy when compared to native RAG.
Modular RAG breaks the RAG pipeline into decoupled and separate components, and a routing/orchestration layer decides which model to use for a particular query. This ensures smooth A/B testing and updates without modifying the whole RAG pipeline.
Hybrid search combines sparse search (exact keywords) and Dense search (query meaning and context), while Reciprocal Rank Fusion (RRF) combines these searches to deliver the most relevant results. This approach is best suited for enterprise knowledge bases and legal/technical documents where exact match and intent matter.
Knowledge graphs in Graph RAG organise information by viewing people as connected entities (products, documents, concepts, etc), instead of relying on keyword matching (semantic search). This helps understand relationships between entities and answer complex queries.
A small language model creates a summary of smaller document sections before embedding them. The summaries are added to the start of the document chunk, facilitating better understanding and creation of vector representations. Results in 35% to 50% reductions in retrieval failure rates.
Corrective Retrieval-Augmented Generation (CRAG) is a retrieval validation approach that verifies whether the retrieved content is relevant and accurate. Only then does it send it to the LLM. If the content does not meet the set standards, generation is halted or refined to improve accuracy.
Using unified multimodal embedding models such as Cohere Embed v4 and ColPali convert text, images, tables, and other formats into a vector format that preserves the meaning and structure of the original document. This simplifies the extraction of information from technical documents, clinical research papers, and other sources.
Unlock the full potential of RAG systems by combining them with AI capabilities such as Gen AI, AI and ML, conversational AI, and more.
When powered by RAG, conversational AI generates natural, context-aware, and multi-turn interactions, resulting in personalized user experiences.
Do you need experienced RAG and LLM engineers for your next project? ScalaCode has a dedicated team of 250+ specialists that can integrate with your existing team, tools, processes, and more to deliver meaningful outcomes. Every specialist has a minimum of 18 months of RAG-in-production experience.
Most RAG (Retrieval Augmented Generation) prototypes work great in demos or controlled environments, but they fail in real-world scenarios. The reasons include poor chunking, weak evaluation, zero re-rankers, no metadata filtering, and lack of observability. ScalaCode’s 7-step RAG development approach addresses these issues before deployment.
We conduct a knowledge source audit and data profiling by identifying the company’s data sources, their volume, how much they change, constraints, if any, sensitive data (PII), and more. Based on this, we develop strategies to collect, organize, and retrieve structured and unstructured data, which reduce the possibilities of incomplete/incorrect data.
Understanding that a common data chunking method is not feasible, we split documents based on content type and structure to determine the ideal method (fixed-size, semantic, or hierarchical). Evaluating various models such as OpenAI, Cohere, Voyage, Nomic, and BGE-M3 helps us find the right model that suits the client’s business needs.
To minimize fuzzy AI results and boost precision, we implement metadata fields such as document type, author, revision, IDs, confidentiality levels, and more. When a RAG engine tries to retrieve information, it uses this metadata to filter documents and get the best results.
Our AI system analyzes each query type and chooses the best retrieval method, such as hybrid search, vector search, query expansion, HyDe, and others, while query classification and routing happen in less than 50 milliseconds. This ensures accurate and relevant results.
Once we retrieve the most relevant documents, we use a cross-encoder, or an LLM-based re-ranker, to determine the appropriate content. Next, a content compressor filters unnecessary information and retains refined information, which is then passed on to the LLM. This helps generate the most relevant response for the user’s query while using fewer tokens.
We give strict instructions to AI, such as avoiding guesswork (in the absence of information) and sticking to a particular tone and format while answering queries. The AI system’s validation flags inaccurate/inappropriate results and generates responses rooted in factual evidence.
Our RAG systems track every single step from data retrieval, answers, and their relevance. Dashboards look out for issues such as failed retrievals, irrelevant and low-confidence responses. Weekly automated tests measure system performance against pre-defined rules (golden set) and generate alerts if results fall below a certain threshold.
Our team of skilled and experienced engineers focuses on retrieving the most relevant information using advanced techniques such as BM25, HyDE, ColBERT, and GraphRAG. The retrieval quality will impact AI responses greatly. The LLM layer will use the retrieved information to generate accurate and relevant answers. Our focus is more on retrieval than blindly trusting LLMs.
Based on the industry-specific terminology, document types, and search patterns, ScalaCode alters its chunking strategies, reranking, and prompt structure to meet client goals. This is critical in industries (eg, healthcare and legal) where regulatory compliance and precision matter.
Choose from different deployment options for your AI solution, such as private, on-premises, isolated (air-gapped networks), or your unique encryption keys (BYO-KEY. Besides meeting unique security needs, we ensure compliance with standards such as SOC 2, Type II, HIPAA, GDPR, and others.
Our carefully developed AI-powered RAG systems will generate factual answers using sources (and mention them) such as enterprise documents, real-time data, or graph nodes. Users can click on the links and check the authenticity of the results.
For every project, our team creates a benchmark dataset (golden set evaluation) with 200+ questions and answers. Domain experts can view and approve AI systems using this dataset. The dataset is used to determine the AI system’s accuracy, relevance, and reliability. The AI solution will be deployed only after it passes this litmus test.
Enterprise RAG development services deliver the maximum ROI in industries that require a deep knowledge base, compliance, real-time data, and those that operate in regulated environments. Below are the segments where ScalaCode has delivered cutting-edge RAG deployments.
Our Retrieval-Augmented Generation (RAG) tailored for financial services helps AI systems access 50,000+ pages of market reports, answer compliance queries using FINRA, SEC, and MiFID II rules and generate credit memos from 10k filings and simplify internal audits. Combining hybrid vector search, metadata filtering, and source citation tracking, our RAG systems generate accurate responses with verifiable references.
ScalaCode’s Enterprise Healthcare RAG development services integrate EHRs (electronic health records), medical knowledge bases, and industry guidelines to build RAG systems. Using hybrid vector-lexical search, PHI redaction, and HIPAA-compliant architecture, we have built secure co-pilots that align with healthcare workflows.
Our legal and compliance-focused RAG development services use GraphRAG architectures, helping companies leverage AI for analyzing contracts, research copilots, and internal libraries. This connects legal documents, precedents, regulations, and their interconnected relationships to facilitate evidence-based decision-making. Also, we implement RBAC (role-based access control) that allows access to authorised users only.
ScalaCode’s industrial RAG development services have successfully deployed RAG that connects equipment manuals, SOPs, and maintenance logs, facilitating accurate and meaningful responses. Integrating Edge AI, vector databases, and zero-latency guides makes it accessible to factory floor workers even without internet connections, resulting in smooth operations.
Our enterprise RAG solutions break silos and gather information from multiple sources, including SharePoint, Confluence, and Google Drive, and store it in a secure AI engine. Over the years, we have built customer AI support agents, automated ticket-resolution systems, and secure knowledge bases with robust access-based security controls. Many of our partners have cut support ticket volumes by 30% to 55%.
Our E-commerce and retail-based RAG solutions connect AI with product catalogs, review databases, and inventory systems while using hybrid search, multimodal, and real-time data retrieval. We have built several AI-powered shopping assistants, merchandising copilots, and review summarization engines that have helped clients boost sales and conversion rates.
For insurance-specific RAG platforms, we connect the LLMs to policy administration systems, claim files, and administration systems while following underwriting guidelines. This has helped insurance companies automate several tasks such as claim processing and fraud-detection workflows while delivering accurate and evidence-backed results.
In the software development landscape, ScalaCode has developed RAG systems that integrate seamlessly with developer tools, version control platforms, and CI/CD pipelines, giving LLMs access to industry-specific and enterprise data. Our solutions have accelerated code reviews, provided developer support, and streamlined daily tasks by providing them with data from code repositories, technical documentation, and other trusted sources.
Let’s look at ScalaCode’s 5 engagement models for enterprise RAG development.
| Engagement Models | What it Covers | Deliverables | Cost | Tentative Timeframe |
|---|---|---|---|---|
| Discovery & Architecture Sprint | Fixed-scope audit of your knowledge sources, competitive benchmark, reference architecture, cost model, and phased roadmap. | An implementation-ready blueprint, whether you build with us or in-house. | $5k - $15k | 2 to 4 weeks |
| Pilot Build | Production-grade RAG pilot on one narrow use case, with evaluation use, observability, and stakeholder acceptance testing. Includes 2 iterations based on SME feedback. | A working system you can demonstrate to the board with real metrics. | $15k - $40k | 4 weeks minimum |
| Full-Production | End-to-end RAG system for enterprise-scale knowledge bases. | Production-ready, SOC 2-aligned enterprise RAG system featuring automated multi-source ETL pipelines, RBAC-secured hybrid search retrieval, a white-label interface with APIs, an MLOps evaluation dashboard, and complete runbook documentation with 90 days of post-launch on-call support. | $60k - $70k | 3 to 6 months |
| Dedicated RAG Team | A dedicated squad (RAG architect, retrieval engineer, MLOps engineer, prompt engineer, QA) embedded with your team for 6+ months. We scale up or down based on your roadmap. Ideal for organizations building RAG as a platform capability, not a point solution. | Embedded cross-functional AI engineering squad delivering a scalable, multi-tenant enterprise RAG platform with custom data pipelines, advanced retrieval architectures, guardrails, and automated evaluation harnesses. | $25k - $500k | Depends on project size and duration. |
| Managed RAG Operations | We operate your RAG system post-launch: model upgrades, index refreshes, evaluation monitoring, retrieval drift detection, cost optimization, security patching. SLA-backed. | Covers model updates, index refreshes, evaluation monitoring, drift detection, security patching, and cost optimization. This is under a strict service level agreement (SLA). | $6k - $15k per month | Depends on contract period. |
The above costs and time-frame are just to give a brief idea. It is advisable to consult a RAG expert for an accurate estimate.
ScalaCode’s tech stack is intentionally model-agnostic and vendor-neutral. We deploy enterprise-ready components across the entire AI lifecycle, choosing the best models, infrastructure, and frameworks. This is based on performance, compliance, and sovereignty standards.
Representative anonymized outcomes from recent ScalaCode RAG engagements.
Compliance Q&A assistant over 80k+ pages of regulation. Answer accuracy 91.4% on golden-set benchmark. 62% reduction in compliance analyst research time.
Medical literature co-pilot over PubMed + 40k internal study reports. Retrieval precision lifted from 52% to 88% after switching from naive RAG to hybrid + rerank + GraphRAG.
Policy Q&A assistant deflecting tier-1 support tickets. 47% ticket deflection in month 3, rising to 58% by month 6 after reranker fine-tuning.
Technician co-pilot over equipment manuals and maintenance logs. Mean time to resolution down 34%; first-time fix rate up 22%.
GraphRAG-based product knowledge assistant for internal sales and CS. Sales rep ramp time cut by 40%, deal-desk response time cut by 65%.
With RAG application development, companies can connect AI models directly to their private and internal data sources. This gives them accurate, real-time, and verifiable answers with clear source citations. Overall, it reduces AI hallucinations and removes the need for expensive and time-consuming model retraining.
Choosing a RAG development company is not easy. The best way to choose a RAG development company for a project is to evaluate it based on its track record. How many RAG systems has it deployed? Check for retrieval expertise and data compliance knowledge. Don’t forget to check industry certifications. The company must focus on continuous evaluation against the specific dataset to eliminate hallucinations and maintain output accuracy.
RAG is an architecture that pairs a large language model with a retrieval system over your own data. At query time, the system fetches relevant chunks from your knowledge base, passes them to the LLM as context, and generates an answer grounded in your sources. Enterprises use RAG to make LLMs accurate on proprietary data, reduce hallucinations, enable citation-backed responses for regulated use cases, and avoid the cost and complexity of fine-tuning large foundation models.
Yes, choosing RAG is a better option for enterprise companies than fine-tuning an LLM. This is because RAG enables companies to use AI models to access real-time and updated business data while eliminating the need to retrain AI models even when the data changes. The AI system does not depend solely on what it has learned over time. It can search for information in enterprise documents, knowledge bases, and other sources before answering queries.
The most notable industries that use RAG development heavily are healthcare, legal, finance, manufacturing, and customer service. This is because companies in these sectors require vast amounts of data and documentation to conduct their operations. Additionally, these industries are also subject to strict legal and regulatory requirements. With RAG, AI systems can obtain accurate information from reliable sources, minimizing the chances of fines.
Yes, it is safe to use RAG systems with private enterprise data on the condition that they implement strong security measures. Examples of such security measures include RBAC (role-based access controls), encryption, and secure connections. This will ensure that AI does not access sensitive or confidential information.
No, a RAG solution does not erase or replace a company’s existing knowledge base. It simply augments the existing knowledge repositories by integrating them with LLMs to find the most accurate and relevant contextual information.
Companies can prevent hallucinations in a production RAG system by following the tips below.
Yes, a RAG application can understand queries in different languages and provide relevant answers. However, the AI model should support those languages; otherwise, it won’t work.
Standard RAG uses the information in the existing knowledge base to answer a query. On the other hand, agentic RAG breaks up a complex query into multiple parts, chooses the right data sources and APIs, performs multiple checks, and verifies the answer before responding.
The time to go from idea to production RAG depends on project complexity, data sources, integrations, testing needs, and many other factors. From design and integration to deployment, it can take 6 to 16 weeks to develop production-ready RAG solutions. However, it is advisable to consult a reputed RAG development company for an exact answer.
They delivered on time and even offered ideas I hadn’t considered. Their proactive input and execution exceeded my expectations.
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