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Trusted across 500+ enterprise AI initiatives and recognized by Clutch, Deloitte, and Entrepreneur for excellence in delivery, we build custom RAG systems that transform enterprise knowledge into accurate, contextual, and actionable intelligence:
Our Core Capabilities
Data Scientists & AI Engineers Onboard
Custom Gen AI Models Trained and Deployed
Reduction in Hallucinations Through RAG-Enabled Knowledge Retrieval
Faster Access to Accurate, Domain-Specific Information
Our RAG consultancy services are designed to simplify your decisions - what to build, when to build it, and how to get results that matter. We don’t push tools, we build what fits.
Identify the workflows where RAG can save time or improve quality.
Create a step-by-step plan for integration, testing, and scale.
We create tailored retrieval pipelines that combine your internal data with large language models to produce grounded, fast, and useful answers. Our custom RAG AI services focus on turning your documents into accurate responses, not generic guesses.
Choose the architecture based on your business scale, privacy requirements, and data structure.
Build retrieval flow that mimics how your team searches and asks questions.
We review existing setups to check if your system is truly retrieval-augmented or just mimicking search. With our RAG system development services, we measure what’s working and fix what’s not, before you scale.
Spot weak retrieval links, irrelevant results, or hallucinated outputs.
Test real-world scenarios to ensure the system performs under pressure.
We make sure your RAG engine doesn’t sit in isolation. As a seasoned RAG development company, we help you plug into tools your teams already use so AI becomes part of the workflow, not a separate tool.
Integrate with CRMs, ERPs, or data dashboards securely and cleanly.
Keep your internal data updated so retrieval is always accurate and current.
We build apps powered by retrieval augmented generation services that are simple to use but deeply capable. Whether for customer service, legal search, or internal Q&A, every app is made with your users in mind.
Build clean interfaces where users ask questions and trust the answers.
Ensure low latency, high recall, and accurate citations from day one.
Pre-built models need more than just data. They need tuning that respects your language, compliance needs, and workflows. Our AI RAG system development services focus on refining both the retrieval and generation layers.
Organize, tag, and embed your documents for higher relevance.
Adjust generation rules and retriever ranking to avoid irrelevant or risky outputs.
We can help you build custom RAG systems that deliver accurate, contextual answers and unlock new enterprise efficiencies.
Making Personal Finance Easier to Understand and Use
The client wanted to build a budgeting app that didn’t just show numbers but helped young users to actually understand and manage their spending habits.
We built a retrieval-augmented chatbot that uses stored financial tips, habits, and examples to give helpful, real-time answers. Users could ask anything, from saving strategies to spending limits and get grounded, personal advice instantly.
Helping a European Bank Stay Personal at Scale
A major European bank was struggling to keep customer support fast and consistent across multiple languages and regions. They needed something more reliable than scripts and more flexible than forms.
We created a multilingual RAG assistant that could pull answers from the bank’s own support documents, policy files, and transaction guides, responding in local languages, instantly.
Speeding Up Hiring with Smarter Search
JobGet needed a way to help blue-collar workers find jobs fast, without filling long forms or waiting for filters to work.
We developed a retrieval-powered job matching tool that could instantly surface relevant openings based on what users typed, said, or tapped. It pulled from verified listings and personalized them based on behavior and need.
Giving Patients a Faster Way to Ask for Help
The client needed a better way for patients to reach staff especially when mobility or isolation made it hard to press a button or call out.
We built a voice-enabled RAG system that connected patient questions to standard hospital responses and routed urgent requests directly to nurses. It used real hospital protocols to give real-time, useful answers.
Turning Business Documents into Real-Time Strategic Advice
SMBs needed a way to get expert-level business insights without hiring consultants or reading through long reports.
We built a multi-agent RAG system that reads uploaded documents, extracts key points, and delivers personalized advice—right inside a simple chat interface.
Powering Faster Deliveries Across 12 Countries
Americana Group wanted to unify their last-mile delivery data while reducing the overall delays across brands like KFC and Pizza Hut.
We created a real-time intelligence layer that helped them automate order assignments, enforce geofencing, and give store managers instant insights.
Making Travel Better with a Simple Airline App
Flynas struggled with an app that frustrated users, leading to confusing booking processes, dropping engagement, and weak customer loyalty.
We reimagined the app with a sleek interface and AI-driven features, including a smart chatbot for quick booking and support. The platform now ensures seamless experiences and is future-ready for growth.
“We were looking for an agency that would understand the direness of the hourly worker job search cycle situation when we came across Appinventiv. What we liked about the team is how they did not just understand what we were looking for but also gave us ideas on how we could make the process more efficient and simplified for the jobseekers through their empathy mapping skillset.”
“We came to Appinventiv looking for a change in the conventional system where patients had to depend on call bells for getting assistance. The team, however, advised us to feature additions that would make the solution truly all-patients inclusive. They made our product more innovative and useful than we had envisioned.”
We approached Appinventiv with a clear vision to build a robust and future-ready platform that could seamlessly integrate with the busy lifestyle of our customers while uplifting their overall experience and giving us a competitive edge. The Appinventiv team not only understood our vision but also enhanced the overall solution with their expertise. The end results exceeded our expectations with massive improvements in the user base and digital revenue for different brands.
Tell us the industry you belong to, and we’ll craft a tailored strategy that fits your unique goals.
SOC 2 (System and Organization Controls 2)
CCPA (California Consumer Privacy Act)
FedRAMP (Federal Risk and Authorization Management Program)
COPPA (Children’s Online Privacy Protection Act)
EU AI Act EU Artificial Intelligence Act
OECD AI Principles
FIPs Fair Information Principles
UK DPA Data Protection Act
PIPEDA Personal Information Protection and Electronic Documents Act
APPs Australian Privacy Principles
PDPA Singapore Personal Data Protection Act
LGPD Brazil General Data Protection Law
BIPA Illinois Biometric Information Privacy Act
New York SHIELD Act
GLBA Gramm-Leach-Bliley Act
DORA Digital Operational Resilience Act
CCPA Canada Consumer Privacy Protection Act
SOC 2 (System and Organization Controls 2)
CCPA (California Consumer Privacy Act)
FedRAMP (Federal Risk and Authorization Management Program)
COPPA (Children’s Online Privacy Protection Act)
EU AI Act EU Artificial Intelligence Act
OECD AI Principles
FIPs Fair Information Principles
UK DPA Data Protection Act
PIPEDA Personal Information Protection and Electronic Documents Act
APPs Australian Privacy Principles
PDPA Singapore Personal Data Protection Act
LGPD Brazil General Data Protection Law
BIPA Illinois Biometric Information Privacy Act
New York SHIELD Act
GLBA Gramm-Leach-Bliley Act
DORA Digital Operational Resilience Act
CCPA Canada Consumer Privacy Protection Act
We’ve worked closely with businesses to solve real problems like messy content, scattered knowledge, and slow answers. From customer support to internal documentation, our RAG development services help replace outdated search tools with systems that actually deliver the right information.
Our data teams understand that clean input drives clear output. We specialize in preparing documents, PDFs, and legacy content for indexing, removing noise, adding structure, and improving retrieval outcomes in every custom RAG model development project.
Our systems are built for scale, with vector databases, hybrid retrievers, and real-time pipelines designed to handle large volumes of enterprise knowledge while ensuring low latency and traceability.
Enterprises today don’t need more content but need clarity. With our custom RAG development services, businesses are turning internal documents into fast, trusted responses that drive real outcomes:
Efficient retrieval starts with the right storage. We work with leading vector databases to structure and query embeddings for fast, precise responses.
We use domain-tuned embedding models to convert documents, data, and queries into meaningful vectors that improve retrieval quality.
To ensure the LLM receives the right context, we build systems that dynamically expand and tailor prompts with retrieved data.
RAG systems must scale to meet enterprise demand. We carry our Retrieval Augmented Generation (RAG) app development using robust infrastructure and orchestration tools.
RAG can work with a range of models depending on your privacy, control, and performance needs.
Turn these technologies into a competitive edge!
From copilots to research assistants, we engineer retrieval-augmented generation that scales securely and delivers measurable impact.
Requirement Analysis
We begin by understanding what your team needs from a RAG system. This includes identifying knowledge gaps, content sources, and use cases where custom RAG model development can improve decision-making or reduce effort.
Project Planning
Once the needs are clear, we create a structured roadmap with milestones, data readiness checks, and timelines. This ensures your RAG application development moves fast and stays aligned with business goals.
Architecture & Design
We design a system that connects your existing data with a retrieval-augmented generation pipeline. Our engineers map out vector databases, retrievers, and generation logic tailored to your internal content.
Agile Development
The build happens in sprints with each one focused on delivering a usable feature. From document indexing to retrieval logic, we test each piece for speed, accuracy, and ease of integration.
Security and Access Control
As a trusted RAG development services company, we implement strong controls like role-based access, content masking, encryption, and secure APIs to protect internal documents and user interactions.
System Integration
Our team connects your RAG system with internal tools like CRMs, knowledge bases, and intranets. This makes your RAG-based AI services feel like a natural part of your workflow, not an add-on.
Testing and Optimization
We test everything from how well the system retrieves relevant content to how helpful the generated output is. Feedback loops and fallback logic are added to ensure real-world reliability.
Compliance Validation
RAG systems often touch regulated data. We audit the full setup for GDPR, HIPAA, SOC 2, and other applicable standards, ensuring responsible AI use across every interaction.
Deployment and Handover
Once ready, we deploy your RAG solution with minimal disruption. We also equip your teams with admin tools, usage dashboards, and handbooks to ensure long-term usability.
Ongoing Support
Our work doesn’t stop after go-live. We monitor system performance, handle updates, and tune your retrieval pipelines over time to keep your RAG system accurate, secure, and scalable.
The cost depends on how complex your retrieval needs are, how much content you’re working with, and what integrations are required.
For example, a basic custom RAG system may cost $50,000 to $100,000, while enterprise-grade RAG development services with multi-agent architecture, security layers, and advanced personalization can range between $150,000 to $350,000+.
Need a precise quote? Share your project details with our experts.
Development time for a RAG system typically ranges between 4 to 10 months. This depends on the number of data sources, the retrieval logic complexity, and whether multi-agent architecture is required.
We follow an agile approach to deliver usable versions early, and keep improving from there. For a custom timeline estimate, speak with our RAG development team.
Retrieval augmented generation services work best when your knowledge base changes often, or when it’s too large to fit into a fine-tuned model. Instead of retraining an LLM every time your content updates, RAG lets you keep the model fixed and simply update the data it retrieves from.
This is especially useful when building custom RAG model development for businesses that rely on current documents, customer support materials, or regulated policies that change regularly.
[Also Read: RAG vs Fine Tuning: Which AI Approach is Best for Your Business?]
A wide range of sources can be connected to a RAG system, including PDFs, Excel files, internal wikis, CRM records, technical manuals, chat logs, and product documentation.
With best RAG AI services, we convert these into clean, structured content for retrieval, ensuring relevance and accuracy in every response.
We evaluate RAG performance using several metrics: retrieval precision, output relevance, response latency, and user satisfaction. Each retrieval is checked for how well it aligns with the question, and the final output is scored on usefulness.
As a RAG application development company, we also monitor fallback rates and feedback loops to tune and improve the system over time.
With custom RAG model development, you get more than just speed. It offers control, security, and reliability. Your system pulls directly from trusted internal sources, not public internet data.
It lowers hallucination risk, reduces time spent searching for answers, and ensures responses are grounded in your business reality.
RAG application development services are used across industries from AI assistants that answer policy questions, to legal search tools, research copilots, sales enablement bots, and internal knowledge portals.
If your teams use long documents, scattered content, or need to pull verified information quickly, RAG-based AI services for enterprises can simplify that work.
RAG solves the problem of information overload. Businesses have thousands of pages of valuable data, but no easy way to search, understand, or act on it.
By using retrieval augmented generation services, teams can ask questions and get answers sourced from their own content fast, accurate, and traceable.
RAD stands for Retrieval-Augmented Decision-making and is focused on helping systems make decisions using retrieved data. RAG stands for Retrieval-Augmented Generation and is designed to generate natural language responses based on retrieved information.
RAD supports decision logic while RAG supports content generation. Both use retrieval, but for different goals.
