Agentic Unconference 2026 - September 19, Chennai
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Our RAG development services connect large language models to your documents, databases and business apps, so every answer cites its source. Senior engineers take enterprise RAG past the demo and into production, the point where most pilots start to hallucinate.
RAG development services cover the design, build and operation of retrieval-augmented generation systems. Engineers connect a large language model to your approved data and index it for search. For each question, the system finds the most relevant passages. The model answers from those alone, with citations and access controls applied.
✓ Your answers live in documents that keep changing. Policies, contracts, product specs and SOPs change every week. RAG picks up a change as soon as the document is re-indexed, with no model retraining.
✓ Someone will ask where an answer came from. In banking, insurance and healthcare, an answer without a source is a liability. RAG returns the passage behind every claim, which gives reviewers and auditors something to check.
✓ Knowledge is scattered across systems and permissions. SharePoint, Confluence, Salesforce and a shared drive each hold part of the answer. RAG can search them together while honoring each source's permissions, and it should sit inside your cybersecurity and compliance controls, not beside them.
✓ You want to swap models without starting over. Your knowledge stays in your index, not in model weights. When a better or cheaper LLM ships, you change the generation layer and keep the retrieval you already tuned.
> Your corpus is small and rarely changes. Below a few hundred pages, long-context prompting with prompt caching is simpler and cheaper. There's no index to maintain and nothing to go stale.
> The answer is a number in a table. "What was Q3 churn by region?" is a query, not a retrieval problem. Text-to-SQL over a governed semantic layer answers it more reliably, and that starts with solid data engineering and analytics.
> You need different behavior, not more knowledge. If the model must write in a fixed format, follow a house style or label tickets your way, fine-tuning fits better. Many teams end up with both, and generative AI consulting up front is how you decide the split.
We build RAG applications around one high-value workflow first, such as policy Q&A, contract review or support deflection. We tune chunking, embeddings and prompts to your documents, then connect the system to your wider enterprise AI solutions roadmap.
Plenty of RAG pilots score well in testing and drift in production. We rebuild the weak layer, usually parsing, chunking or ranking. Then we add hybrid search and a reranker, and test every change against a golden question set.
For teams that want a managed RAG service without running the pipeline themselves. We deploy and operate it inside your cloud account on a managed platform. We also handle ingestion, tuning and monitoring as your content grows.
We connect RAG to the systems where knowledge actually lives and sync each source's access rules into the index. A user only retrieves what they could already open, enforced at query time rather than by a prompt.
We score retrieval and answers on faithfulness, relevance and context recall. Those scores run in CI, so a regression blocks the release. After launch, tracing, cost dashboards and drift alerts run on the same MLOps services foundation.
Some questions need more than one lookup. We build agents that plan the search, query several sources, check their own evidence and call tools through MCP. It becomes the retrieval core of wider agentic AI framework integration.
95%+
Sustained 95%+ data extraction accuracy across growing document volumes
Entrans used OCR and large language models to extract and structure data from unstructured authorization documents, then validated it with a configurable rule engine, accelerating processing by 3X.
70%
70% faster access to critical patient data, with 100% traceability of all retrieval actions
For a multi-location hospital network, Entrans built a FHIR-compliant API gateway with logging and access control, automating real-time retrieval of lab results, imaging and prior history.
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Single point of access for all search queries across hospital systems
Entrans built an AI chatbot that runs semantic, role-aware search across EHR, hospital information, pharmacy and document systems, then summarizes results for doctors, nurses and analysts.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Yes, RAG is still relevant, because long context windows handle small, stable document sets but do not replace retrieval at enterprise scale. Sending a whole knowledge base with every question is slow and expensive. Models also miss facts buried deep inside very long prompts. RAG also applies per-user permissions and attaches a citation to each answer, which a stuffed prompt cannot do.
RAG as a service means a provider runs a managed retrieval pipeline for you, while custom RAG development builds one tuned to your data that your team owns. Managed platforms such as Amazon Bedrock Knowledge Bases or Azure AI Search get a first version live quickly. Custom builds win when documents are complex, accuracy targets are strict, or you need control over chunking, ranking and cost. When you compare RAG as a service providers, ask where your data is stored and how they measure answer accuracy.
Agentic RAG is a RAG system in which an AI agent decides what to retrieve, from which sources and in what order, instead of running one fixed search. It pays off on multi-step questions, such as checking a claim against current policy and the customer's history. The trade-off is higher latency and cost per answer, plus more places for errors to hide. Start with standard RAG, measure where it fails, and add agentic steps only for those question types.
A focused RAG implementation on one document set typically reaches a usable pilot in four to eight weeks. Production takes longer. The timeline depends less on the model than on the data. It turns on how many sources you connect, how messy the documents are and how strict the permissions are. Evaluation takes time too. You need a golden set of real questions with agreed answers before anyone can call the system accurate.
Yes, a RAG system can run inside your own environment, so documents never reach a public model API. The index, embeddings and retrieval can sit in your VPC or data center. The language model can be a private deployment on Azure OpenAI or Amazon Bedrock, which do not train on your data. It can also be an open-weight model like Llama, served on your own GPUs with vLLM. The choice comes down to answer quality, hosting cost and how much data may leave your network.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
