- RAG Development & AI Knowledge Systems.
India's Enterprise RAG Development Team —
Built for US, UK, Canada, Australia & UAE Businesses
We build production-grade Retrieval-Augmented Generation systems that connect your LLMs to your proprietary knowledge — accurate, auditable, and enterprise-ready.
🌍 Clients in 12+ Countries | ✅ 120+ RAG Deployments | ⚡ 8–12 Week Delivery | 🔒 NDA-First. Always.
No commitment required · NDA available · Response within 24 hours · USD / GBP / AED contracts accepted
We Love To Make Apps That Make A Difference
Agents Platform - Enterprise Knowledge Assistant
A configurable enterprise RAG platform supporting custom ingestion pipelines, semantic chunking, hybrid search, reranking, and citation-backed responses. Business documents become a searchable knowledge base that delivers accurate, traceable answers while remaining easy to maintain as content evolves.
Quizentia - AI Quiz Generation
Our RAG pipeline uses document-aware ingestion with semantic chapter-based chunking for textbooks and specialized parsing for question banks to preserve learning context. Combined with hybrid retrieval, the platform generates quizzes that remain grounded in the original content instead of relying solely on the LLM's knowledge.
Candidate Hub - AI Recruitment Assistant
Resumes, job descriptions, and interview notes are indexed into a unified knowledge base using semantic embeddings and metadata extraction. The platform retrieves the most relevant candidate evidence to generate contextual summaries and comparisons, helping recruiters evaluate applicants with supporting information rather than keywords alone.
Production Code Chatbot
Unlike traditional document RAG, this solution performs code-aware chunking by indexing functions, classes, and modules while preserving repository structure and metadata. Developers receive architecture-aware answers backed by retrieved source files, enabling faster onboarding and code exploration across large repositories.
Nova - AI Email Automation
Incoming emails are first analyzed for intent before hybrid semantic retrieval searches company documentation, policies, and support resources. The retrieved context is used to generate grounded response drafts that remain reviewable, ensuring faster replies without compromising accuracy or company standards.
Built for Global Clients.
Operated from India.
We've removed every friction point that makes working with an offshore AI team feel risky.
🕐 Timezone Coverage
We work across EST, PST, GMT, AEST, and GST. Overlap hours guaranteed. No "we'll reply tomorrow" delays.
📄 NDA Before Anything Else
Every engagement starts with a signed NDA. Your IP, data, and business logic stay yours — always.
💵 Contracts in Your Currency
We invoice in USD, GBP, CAD, AUD, and AED. No forex surprises. Clean, simple agreements.
⚖️ US / UK Compatible Contracts
Our service agreements follow international standards — milestone-based, deliverable-driven, with clear exit clauses.
🔒 Your Data Never Leaves Your Infrastructure
We deploy on your AWS, Azure, or GCP account. We never store, train on, or retain your proprietary data.
📞 Dedicated Point of Contact
One senior engineer owns your project end-to-end. No ticket queues. No rotating support agents.
We've worked with CTOs, Heads of AI, and Founders across the US, UK, Canada, Australia, and UAE who needed a reliable Indian engineering team — not a vendor. That's the difference.
🇺🇸 US Clients — SOC 2, HIPAA compliance. AWS GovCloud available.
🇬🇧 UK Clients — GDPR-compliant pipelines. GMT overlap hours.
🇨🇦 Canada — PIPEDA-aware data handling. CAD invoicing.
🇦🇺 Australia — AEST overlap. Australian Privacy Act compliance.
🇦🇪 UAE Clients — AED invoicing. Data residency in UAE cloud available.
and others..
Transparent Engagement Models
No retainer traps. No surprise invoices. Pick the model
that fits your stage — from first PoC to full production.
RAG Discovery & Audit
Data audit, architecture blueprint, RAG readiness report, 1 strategy call
Timeline: 3 - 5 Days
RAG Proof of Concept
Working demo on your data, accuracy benchmarking, stakeholder-ready output
Timeline: 2 - 3 Weeks
Production RAG System
Full pipeline build, vector DB, LLM integration, API + UI, 30-day support
Timeline: 8 - 12 Weeks
Enterprise RAG System
All above + SSO/RBAC, compliance logging, multi-tenant, SLA-backed infra
Timeline: 10 - 14 Weeks
RAG Optimization & Fix
Working demo on your data, accuracy benchmarking, stakeholder-ready output
Timeline: 1 - 2 Weeks
Monthly Retainer
Ongoing dev, monitoring, updates, feature additions, priority support
Timeline: Monthly
Engagement Model
Fixed-Price Projects
Best for: Defined scope, clear deliverables You know what you need. We quote it, milestone it, and deliver it. No scope creep. No open-ended billing.
Time & Material
Best for: Evolving requirements, R&D phases Flexible hours model — ideal for startups iterating fast or enterprises exploring RAG capabilities.
Dedicated Team
Best for: Long-term builds, scaling AI teams 1–3 senior RAG engineers embedded in your workflow. Slack, standups, your tools — like an in-house team.
RAG Retainer
Best for: Post-launch maintenance & growth Monthly block of hours for updates, tuning, monitoring, and new feature development.
What affects your RAG project cost?
- Data Volume & Sources
More document types (PDFs, databases, APIs, emails)
= more ingestion complexity = higher cost.
- Compliance Requirements
HIPAA, SOC 2, GDPR pipelines require additional
architecture layers and audit tooling.
- LLM & Embedding Choice
GPT-4 / Claude vs open-source Llama affects
both build complexity and your ongoing API costs.
- Deployment Environment
Cloud (AWS/GCP/Azure) is faster. On-premise or
private VPC adds infrastructure setup time.
- Retrieval Complexity
Naive RAG vs Agentic RAG vs Multi-hop reasoning —
each tier adds engineering depth and timeline.
The Problem We Solve
LLMs Don't Know Your Business
Out-of-the-box language models hallucinate, go stale, and can't access your private data. RAG fixes all of this — when it's built right.
Hallucination at Scale:
Generic LLMs confidently produce wrong answers drawn from their training data — unacceptable in legal, medical, financial, or customer-facing contexts.
Stale Knowledge Cutoffs
Model training ends months or years in the past. Your policies, products, and procedures change constantly — static models can't keep up.
No Access to Private Data
Your most valuable knowledge lives in internal docs, databases, CRMs, and wikis. LLMs have no way to reach it without a purpose-built retrieval layer.
Unprovable Answers
Enterprise teams need citations, audit trails, and source attribution. Black-box AI responses fail compliance and governance requirements.
💡 What does a RAG system actually save?
Typical enterprise clients report:
— 60–80% reduction in time spent searching internal docs
— 40% decrease in support ticket volume (AI answers first)
— 3–5x faster onboarding for new employees
— Full payback on RAG investment within 4–6 months
End-to-End RAG Development
From proof-of-concept to production deployment, we cover every layer of your RAG architecture.
RAG Architecture Design
We design the right retrieval strategy for your data — whether that's naive RAG, hybrid search, agentic RAG, or advanced multi-hop reasoning pipelines.
-
Requirements discovery & data audit
-
Chunking & indexing strategy
-
Embedding model selection
-
Retrieval strategy design
-
Latency & accuracy trade-off analysis
-
Scalability blueprint
Full-Stack RAG Development
Complete build-out of your production RAG system — from data pipelines and vector databases to the LLM integration layer and UI.
-
Multi-source document ingestion pipelines
-
Vector database setup & optimization
-
Custom embedding & reranking models
-
Hybrid search (semantic + keyword)
-
LLM integration (GPT-4, Claude, Llama 3+)
-
API & UI delivery
Enterprise RAG Systems
Mission-critical RAG deployments with enterprise-grade security, access control, compliance logging, and multi-tenant support.
-
SSO / RBAC & permission-aware retrieval
-
On-premise or private cloud deployment
-
SOC 2 / HIPAA compliant pipelines
-
Audit trail & explainability layer
-
Multi-tenant isolation
-
SLA-backed infrastructure
RAG Optimization & Tuning
Already have a RAG system that's underperforming? We diagnose retrieval failures, re-rank bottlenecks, and rebuild for accuracy.
-
Retrieval quality audit & benchmarking
-
Chunk size & overlap optimization
-
Embedding model replacement
-
Reranker integration (Cohere, Jina)
-
Latency profiling & caching
-
RAGAS evaluation framework setup
Agentic RAG Systems
Beyond static retrieval — we build autonomous RAG agents that plan multi-step queries, use tools, and reason over complex information.
-
LangGraph / LlamaIndex agent pipelines
-
Tool-augmented retrieval agents
-
Multi-hop & iterative retrieval
-
Query decomposition & routing
-
Corrective RAG (CRAG) implementation
-
Human-in-the-loop workflows
RAG Training & Enablement
Upskill your internal engineering teams with hands-on RAG training, architecture workshops, and technical consulting retainers.
-
Custom RAG workshop (2–5 days)
-
Team code review & mentoring
-
Architecture consulting retainer
-
RAG evaluation & testing training
-
LLMOps best practices
-
Ongoing technical advisory
Retrieval-Augmented Generation, Explained
RAG is the architecture that grounds LLM responses in your real, current, verified knowledge — dynamically retrieved at query time.
RAG Pipeline Flow
01. Document Ingestion
PDFs, databases, APIs, wikis, emails — your knowledge sources are parsed, chunked, and cleaned.
02. Embedding & Indexing
Chunks are encoded into dense semantic vectors and stored in a high-performance vector database.
03. Semantic Retrieval
User query is embedded and matched against the index — relevant context is fetched in milliseconds.
04. Augmented Generation
Retrieved context is injected into the LLM prompt — producing grounded, citable, accurate answers.
05. Response + Citations
Users receive the answer plus direct links to source documents — fully auditable and traceable.
Real-Time Knowledge
No retraining required. Update your documents and the system reflects it instantly — your AI stays current always.
Dramatic Accuracy Gains
Our RAG implementations routinely achieve 94–99% retrieval precision vs. 55–70% for vanilla LLM responses on domain-specific queries.
Compliance-Ready by Design
Every answer comes with traceable source attribution, enabling audit trails required by HIPAA, SOC 2, GDPR, and enterprise governance frameworks.
Cost-Efficient vs. Fine-Tuning
RAG adapts your AI to proprietary data without the enormous cost and time of model fine-tuning or re-training.
Data Privacy & Security
Your data never leaves your infrastructure. We architect on-premise, VPC, and cloud-isolated deployments with zero data leakage.
Best-in-Class Tools, Expertly Integrated
We're framework-agnostic and model-agnostic — we select the right tools for your architecture, not our convenience.
📦 Pinecone - Managed Vector DB
🐘 pgvector - Postgres Extension
🔵 Weaviate - Open-source VDB
🟡 Qdrant - High-Performance VDB
🔶 Chroma - Local Dev VDB
❄️ Milvus - Cloud-native VDB
🟣 Redis VSS - In-Memory VDB
⚡ Elasticsearch - Hybrid Search
A Proven Delivery Process
Every engagement follows our battle-tested 6-phase methodology — built from 120+ deployments across industries.
How We Work
1. Discovery & Data Audit
We map every knowledge source in your organization — documents, databases, APIs, and internal systems. We assess data quality, volume, update frequency, and access controls. The output is a comprehensive RAG readiness report with recommended architecture.
Week 1, Data Mapping, Requirements Workshop, Architecture Blueprint
2. Proof of Concept Build
Before full investment, we build a working PoC on a representative subset of your data. You can evaluate retrieval quality, answer accuracy, and latency firsthand — with real questions from your domain — before committing to production development.
Week 2–3, Working Demo, Accuracy Benchmarking, Stakeholder Review
3. Pipeline Development
We engineer your ingestion pipelines — multi-source connectors, custom parsers for PDFs/HTML/tables, chunking strategies, embedding batch processing, and incremental update workflows. Robust pipelines are the foundation of reliable RAG.
Week 3–6, Data Ingestion, Chunking Strategy, Embedding Pipeline
4. Retrieval & Generation Layer
We implement the full retrieval stack — vector search, hybrid BM25/semantic retrieval, reranking models, context compression, and prompt engineering. LLM integration is production-hardened with fallbacks, rate limiting, and streaming.
Week 5–8, Vector Search, Reranking, LLM Integration
5. Evaluation & Hardening
We run systematic evaluation using RAGAS, custom golden datasets, and adversarial testing. Every dimension is measured: faithfulness, answer relevancy, context precision, recall, and latency. We iterate until targets are met.
Week 7–9, RAGAS Evaluation, A/B Testing, Security Audit
6. Production Launch & Handover
We deploy to your target environment (AWS, GCP, Azure, on-prem), configure monitoring dashboards, set up alerting, and transfer full ownership to your team with documentation, runbooks, and a 30-day support window.
Week 9–12, Deployment, Monitoring, Documentation, 30-Day Support
Ready to Build AI That Actually Knows Your Business?
Book a free 45-minute discovery call. We'll review your data, discuss your use case, and outline exactly what a RAG system could deliver — no sales pitch, just engineering conversation.
✅ No commitment required ✅ NDA available ✅ Response within 24 hours ✅ Free RAG readiness assessment