RAG (Retrieval-Augmented Generation) Solutions are AI systems that answer questions by retrieving real, relevant context from a business's own documents and data before generating a response — reducing hallucination compared to an LLM answering from memory alone — and include document ingestion pipelines, vector/hybrid search tuning, and source-cited answers.
Private AI knowledge systems grounded strictly in your company's PDFs, SOPs, databases, and wikis—delivering accurate, citation-backed answers with zero hallucination.
Real situations clients across these industries come to us with — and how rag (retrieval-augmented generation) solutions fixes them.
The problem: New hires spend weeks searching scattered internal documents to find answers senior staff already know.
How we solve it: We build a RAG system over your internal knowledge base so staff get a grounded, cited answer in seconds instead of searching manually.
The problem: A support team answers the same product questions repeatedly, referencing documentation customers rarely read themselves.
How we solve it: We build a RAG-powered assistant that answers customer questions directly from your real documentation, with sources.
The problem: Staff need quick answers from lengthy internal protocols and policy documents during a shift.
How we solve it: We build a RAG system grounded in your actual protocol documents, so answers are traceable back to the real source.
The problem: Technical manuals for equipment are long and hard to search quickly on the shop floor.
How we solve it: We build a RAG-based lookup tool so staff can ask a direct question and get a cited answer from the real manual.
What you actually get from a rag (retrieval-augmented generation) solutions engagement.
RAG (Retrieval-Augmented Generation) Solutions covers a range of specific needs — here's the full breakdown.
Timeline depends on scope and complexity — confirmed together during your discovery call.
Starting prices with our current launch offer. All amounts in INR, plus applicable taxes. Your quotation confirms the exact deliverables, exclusions and delivery estimate before payment.
Document Q&A pilot
Up to 50 text-based documents, total 500 pages, basic chat UI and source references.
AI offers are starter implementations or pilots, with agreed usage limits. OCR, fine-tuning, complex permissions and production scaling are separate. Model API charges, cloud storage, BI licences and third-party subscriptions are excluded.
Your approved project scope is delivered at the agreed price. Additional scope or change requests are quoted separately before any extra work begins.
The same straightforward process behind every project, regardless of size.
We start by understanding your actual workflow, goals, and constraints — no generic templates, just a conversation about what you need.
You get a written scope, timeline, and price before any work begins. Anything outside that approved scope is quoted separately as a change request.
Where the project has a user-facing interface, we agree on screens and user flows with you before development starts.
We build in phases with regular check-ins, so you see progress and can give feedback along the way, not just at the end.
Each phase is tested against the agreed scope before release, and you review it before it goes live.
We handle deployment and go-live, including data migration where the project needs it.
We stay on for a post-launch support window, with the option of ongoing maintenance afterward.
Real rag (retrieval-augmented generation) solutions projects we've delivered for clients.
Invoice generation, approval routing, and payment reminders were handled manually, consuming significant finance team time every month.
Automated invoice generation, approval routing, and payment reminders, cutting manual finance work by 70% each month.
The admin team previously sent enrollment confirmation and fee-reminder emails one by one to every student.
Automated the enrollment confirmation and fee-reminder emails.
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“Data24Zone rebuilt our sales process from the ground up. Our team now closes deals faster and reporting time that used to take a full day happens in minutes. Best decision we made this year.”
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Timelines depend on scope — a focused project can take a few weeks, while a larger multi-module build can run several months. We give you a realistic timeline after an initial scoping call, and break larger projects into phases so you see working results early rather than waiting for one big launch.
No — we're happy to work within tools you already use (like your existing CRM, ERP, or hosting setup), or recommend a stack from scratch if you're starting fresh. We'll flag it upfront if an existing tool genuinely isn't a good fit, but the default is to fit into what you already have wherever practical.
No — RAG (Retrieval-Augmented Generation) Solutions covers a range of specific needs (Knowledge Base Q&A, Document Ingestion Pipelines, Vector Search Tuning, and more), and most clients start with the one piece that matters most right now and expand later.
Every project includes a post-launch support window to fix anything that surfaces under real usage. After that, you can move to an ongoing maintenance and support arrangement if you want continued monitoring, updates, and priority fixes — it's optional, not forced.
We scope revisions into the project plan upfront rather than treating them as an afterthought — you review work at each phase, not just at the very end, so feedback gets folded in as we go instead of piling up into a single high-stakes final review.
We've built this for a range of industries — including SaaS & Startups, Services & Consulting, Education, Healthcare — but the underlying process is the same regardless of industry: understand your actual workflow first, then build around it.
Tell us about your project and we'll help you scope the right solution — no obligation, just a clear plan forward.
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