Stop AI hallucinations. Ground your LLM in YOUR data. Embeddings (OpenAI, BGE, Cohere, E5) + Vector DBs (ChromaDB, Qdrant, Milvus, Pinecone) + LLMs (GPT-4, Claude, Llama). 95-99% factual accuracy. 90% cost savings.
Start with YOUR knowledge challenges, not technology
LLMs make up facts, provide outdated information, can't access your company data
✓ RAG Solution:
RAG grounds AI responses in YOUR actual documents. 99% factual accuracy. Real-time data access. Zero hallucinations.
Staff spending hours searching through docs, wikis, PDFs. Manual knowledge retrieval is slow.
✓ RAG Solution:
Semantic search finds exact answers in milliseconds across millions of documents. Natural language queries.
Generic chatbot answers. Can't answer questions about YOUR products, policies, or data.
✓ RAG Solution:
RAG chatbots know YOUR business. Instant answers from product docs, support tickets, contracts, any data.
Sending entire documents to GPT-4/Claude costs $50-$500 per query. Unsustainable at scale.
✓ RAG Solution:
RAG sends only relevant snippets (10x smaller). 90% cost reduction. Self-hosted embeddings = $0 API fees.
We choose the optimal embeddings, vector DB, and LLM based on your data and requirements
See how we match your knowledge base to the right RAG stack
Customer support chatbot with product knowledge
Generic chatbot can't answer product questions. Customers frustrated. High support costs.
RAG-Powered Support Chatbot
BGE-large embeddings (self-hosted) + Qdrant vector DB + Llama 4 70B (or GPT-4 API)
Hybrid (embeddings self-hosted, LLM cloud or on-premise)
Product docs, FAQs, support tickets, manuals
95%+ answer accuracy, citations to source docs
6-8 weeks
Legal/contract search & analysis (enterprise)
Lawyers spend 10-20 hours/week searching contracts. Compliance risks. Missed clauses.
RAG Legal Document Search
OpenAI embeddings (high accuracy) + Pinecone (fast search) + Claude 3.5 (legal reasoning)
Cloud (premium quality for high-value legal work)
Contracts, case law, regulations, legal memos
98% retrieval accuracy, clause extraction, risk analysis
10-12 weeks
Internal knowledge base search (company wiki)
Employees waste 3-5 hours/week searching Confluence, Notion, docs. Knowledge silos.
RAG Enterprise Knowledge Search
E5-large-v2 (self-hosted) + ChromaDB (simple) + Llama 4 13B (fast)
Fully self-hosted (data privacy, $0 API fees)
Confluence, Notion, Google Docs, Slack, emails
Instant semantic search, natural language Q&A
4-6 weeks
Medical diagnosis assistant (healthcare)
Doctors need quick access to medical literature, patient history. HIPAA compliance critical.
HIPAA-Compliant RAG Medical Assistant
BioBERT embeddings (medical) + Milvus (on-premise) + Llama 4 70B fine-tuned (medical)
Fully on-premise (HIPAA, data never leaves network)
Medical journals, patient records, clinical guidelines
Medical-grade accuracy, citation tracking
12-16 weeks (includes HIPAA compliance)
E-commerce product recommendations
Generic product search misses intent. Low conversion. Customers can't find products.
RAG Semantic Product Search
Cohere Embed (multilingual) + Qdrant (filters) + GPT-4 (personalization)
Hybrid (embeddings self-hosted, GPT-4 API for recommendations)
Product catalog, reviews, specs, user behavior
40% increase in conversion, better product discovery
8-10 weeks
Financial research & market analysis
Analysts spend days reading reports. Can't keep up with market news. Missed insights.
RAG Financial Intelligence Platform
OpenAI embeddings + Pinecone + Claude 3.5 (long-context for reports)
Cloud (need premium quality, long context)
Financial reports, earnings calls, market news, SEC filings
Real-time insights, trend analysis, automated summaries
10-14 weeks
Expert RAG implementation, not just integration
We analyze YOUR knowledge base, then recommend the optimal embedding model, vector DB, and LLM based on data volume, accuracy needs, and budget.
Use best tools for each layer: OpenAI/BGE for embeddings, Qdrant/Pinecone for storage, GPT-4/Llama for generation. Switch without rebuilding.
Self-hosted embeddings (90% savings), efficient chunking (10x less tokens), caching (70% hit rate). Hybrid deployment.
On-premise RAG for HIPAA, GDPR, SOC 2. Data never leaves your network. Or use cloud with compliance (Claude, GPT-4).
Ingest from PDFs, Word, Confluence, Notion, databases, APIs, Slack. Automated chunking, metadata extraction, incremental updates.
Combine semantic (meaning) + keyword (exact match) search. Reranking with Cohere. Filters, metadata. Sub-second retrieval.
Our systematic approach to RAG technology selection
| Criteria | Low Need | Medium Need | High Need |
|---|---|---|---|
| Data Volume | <10K docs: ChromaDB (simple) | 10K-1M docs: Qdrant (production) | >1M docs: Milvus, Pinecone (distributed) |
| Embedding Quality | all-MiniLM-L6 (fast, cheap) | BGE-large, E5-large (balanced) | OpenAI 3-large, Cohere (premium) |
| Privacy Requirements | Cloud OK: OpenAI embeddings, Pinecone | Hybrid: Self-hosted embeddings, cloud DB | Fully on-premise: BGE + Milvus (HIPAA) |
| LLM for Generation | Llama 4 13B (self-hosted, fast) | GPT-4 Turbo (cloud, quality) | Claude 3.5 Opus (long context, accuracy) |
| Search Type | Semantic only: Vector search | Hybrid: Vector + keyword (Qdrant) | Advanced: Hybrid + reranking (Cohere) |
Every industry has unique knowledge challenges - we know which RAG stack works best
Challenge:
Chatbots can't answer product questions, high support costs, inconsistent answers
RAG Solution:
RAG chatbot with product docs, FAQs, tickets → instant accurate answers with citations
AI Stack:
BGE embeddings (self-hosted), Qdrant, Llama 4 70B
Results:
70% reduction in support tickets, 95% answer accuracy
Challenge:
Contract search takes hours, compliance risks, missed clauses, expensive legal hours
RAG Solution:
RAG contract search → instant clause extraction, risk analysis, compliance checks
AI Stack:
OpenAI embeddings, Pinecone, Claude 3.5 (legal reasoning)
Results:
90% faster contract review, 100% compliance coverage
Challenge:
Doctors need quick access to medical literature, patient history, HIPAA compliance
RAG Solution:
HIPAA-compliant RAG → medical Q&A, patient history search, clinical decision support
AI Stack:
BioBERT (medical embeddings), Milvus (on-premise), Llama 4 fine-tuned
Results:
Medical-grade accuracy, HIPAA compliant, faster diagnosis
Challenge:
Analysts spend days reading reports, can't keep up with market news, missed insights
RAG Solution:
RAG financial intelligence → automated research, real-time market analysis, summaries
AI Stack:
OpenAI embeddings, Pinecone, Claude 3.5 (long-context)
Results:
80% faster research, real-time insights, trend detection
Challenge:
Generic product search, low conversion, customers can't find products
RAG Solution:
RAG semantic product search → natural language queries, intent understanding, recommendations
AI Stack:
Cohere Embed (multilingual), Qdrant (filters), GPT-4
Results:
40% conversion increase, better product discovery
Challenge:
Employees waste 3-5 hours/week searching Confluence, Notion, docs, knowledge silos
RAG Solution:
RAG enterprise search → unified search across all sources, instant Q&A
AI Stack:
E5-large-v2 (self-hosted), ChromaDB, Llama 4 13B
Results:
80% time saved, knowledge democratization, $0 API fees
From RAG consulting to full enterprise platform
Architecture Recommendation
🚀 Consulting only - no development
Single Data Source
🚀 Cloud (Pinecone) OR Self-hosted (ChromaDB)
Multi-Source + Advanced Features
🚀 Hybrid (embeddings self-hosted, LLM cloud or on-premise)
Custom Multi-Modal Platform
🚀 Multi-cloud + on-premise hybrid, custom GPU cluster
Everything you need for production-ready RAG deployment
Everything you need to know about RAG implementation
It depends on 4 factors: (1) Quality: OpenAI text-embedding-3-large (best quality, 3072 dims, $0.00013/1K tokens) OR Cohere Embed v3 (multilingual, 100+ languages, $0.0001/1K). For self-hosted: BGE-large-en-v1.5 (SOTA quality, $0 API fees) OR E5-large-v2 (Microsoft, excellent retrieval). (2) Cost: High volume → Self-hosted (BGE, E5, all-MiniLM, $0 API fees). Low volume → Cloud APIs (OpenAI, Cohere). (3) Languages: Multilingual → Cohere Embed v3 (100+ languages). English only → BGE or OpenAI. (4) Privacy: HIPAA/GDPR → Self-hosted only (BGE, E5). We often recommend HYBRID: Self-hosted BGE for bulk embedding (millions of docs, $0 cost) + OpenAI for query embedding (better quality, $0.001/query). Best of both worlds!
Depends on scale and needs: (1) ChromaDB: <10K docs, POC/MVP, embedded (Python), simple setup. Perfect for testing RAG. Free, self-hosted. (2) Qdrant: 10K-1M docs, production, hybrid search (semantic + keyword), filters, metadata. Self-hosted or cloud. Enterprise-ready. (3) Milvus: >1M docs, billions of vectors, distributed cluster, horizontal scaling. For massive scale (Google-size). Self-hosted on Kubernetes. (4) Pinecone: Managed cloud, no ops, fastest setup, pay-as-you-go ($0.096/hour). Great if you don't want to manage infrastructure. (5) pgvector (Postgres): Use existing Postgres, simple, reliable, <100K docs. Good for teams already on Postgres. We recommend: Start with ChromaDB (POC) → Qdrant (production) → Milvus (massive scale). Or Pinecone if you want managed cloud.
MASSIVE savings! Sending full docs to LLM: Example: 100-page PDF = 50K tokens. GPT-4 input: $0.01/1K tokens = $0.50 per query. 1000 queries/day = $500/day = $15K/month = $180K/year. RAG approach: (1) Embeddings (one-time): 50K tokens × $0.00013 (OpenAI) = $0.0065 per doc. Or $0 if self-hosted BGE. (2) Vector search: Free (self-hosted) or $0.096/hour (Pinecone) = $70/month. (3) LLM with RAG (only relevant chunks): 2K tokens per query (10x smaller!) × $0.01 = $0.02 per query. 1000 queries/day = $20/day = $600/month = $7.2K/year. Savings: $180K - $7.2K = $172.8K saved per year (96% reduction!). Even with cloud vector DB: $7.2K + $0.84K = $8K/year vs $180K = 95% savings. ROI is insane!
Chunking = breaking documents into smaller pieces for embedding. CRITICAL for RAG accuracy! (1) Why chunk? LLMs have context limits. Embeddings work best on 100-500 tokens. Need to retrieve most relevant sections, not entire docs. (2) Chunking strategies: Character-based (simple, 512 chars, overlapping 50). Recursive (smart, respects paragraphs/sentences). Semantic (AI-based, breaks at meaning changes). Document-specific (PDFs: by section, code: by function, tables: by row). (3) Overlap: Add 10-20% overlap between chunks to preserve context. Example: Chunk 1: tokens 0-512, Chunk 2: tokens 450-962 (overlap 450-512). (4) Metadata: Extract title, section, page number, date per chunk for filtering. Bad chunking → poor retrieval → wrong answers. Good chunking → 95%+ accuracy. We test 5-10 chunking strategies and pick the best for YOUR data!
RAG vs Fine-tuning vs Prompts: (1) RAG: 95-99% factual accuracy (grounded in docs), works with latest data (real-time updates), no retraining needed, cost-effective ($8K-$55K one-time + low hosting). Best for: Q&A, search, chatbots with company data. (2) Fine-tuning: 90-95% accuracy (can still hallucinate), requires labeled data (1000s of examples), expensive ($20K-$100K), needs retraining for updates. Best for: Specific tasks (classification, style), proprietary workflows. (3) Prompt engineering: 70-85% accuracy (limited by context window), manual prompt crafting, limited knowledge (only what fits in prompt). Best for: Simple tasks, prototypes, low-volume. RAG advantages: Always accurate (citations to source docs), scales to billions of docs, stays current (sync with data sources), cost-effective at scale. We often COMBINE: RAG for knowledge retrieval + fine-tuned LLM for domain reasoning. Example: Medical RAG (retrieves papers) + fine-tuned medical LLM (diagnosis reasoning) = 99% accuracy!
YES! Multiple approaches: (1) Incremental indexing: New/updated docs → embed → upsert to vector DB (seconds to minutes). Example: New support ticket arrives → embed → add to Qdrant → instantly searchable. (2) Scheduled batch updates: Nightly/hourly sync with data sources (Confluence, databases). Check for changed docs, re-embed, update vector DB. (3) Webhook-based: Data source sends webhook on change → trigger embedding pipeline → update index. Example: Notion page updated → webhook → re-embed → update ChromaDB. (4) Streaming updates: Real-time data streams (Kafka, Kinesis) → continuous embedding → vector DB. For high-frequency updates (stock prices, news). (5) TTL (Time-to-Live): Set expiration on embeddings, auto-refresh stale data. Latency: Incremental: seconds. Batch: minutes to hours (depending on frequency). Streaming: real-time. We implement: Automatic sync jobs + webhook listeners + manual refresh API. Your RAG always has latest data, no stale answers!
YES, with on-premise deployment: (1) Healthcare (HIPAA): Self-hosted BGE embeddings (data never sent to OpenAI). Milvus vector DB on-premise (patient data never leaves network). Llama 4 70B fine-tuned for medical Q&A (on-premise). Includes: encryption (TLS 1.3, AES-256), audit logs (every query logged), access controls (RBAC), PHI detection/masking, BAA (Business Associate Agreement). Example: Patient record search → embed on-premise → Milvus lookup → Llama 4 answers (all on-premise, zero external APIs). (2) Finance (GDPR, PCI-DSS): Hybrid option: Claude 3.5 API for general queries (Anthropic is SOC 2, HIPAA-eligible), BGE + Milvus on-premise for sensitive data (SSN, account numbers). Data residency (EU servers only if required). Example: Contract search → embed on-premise → anonymize data → send to Claude for analysis → store results in EU database. (3) Audit trails: Every retrieval logged (who, what, when, which docs). Immutable logs for compliance. Reports for auditors. Cost: On-premise RAG starts at $55K (includes compliance setup). Cloud with compliance: $22K (using HIPAA-eligible APIs). We handle: BAA agreements, security reviews, compliance documentation.
YES! Multi-modal RAG handles all data types: (1) Images: Use CLIP (OpenAI vision embeddings) or GPT-4 Vision for image→text descriptions → embed text → vector DB. Query: "Find product images with blue packaging" → retrieves relevant images. (2) Tables: Extract tables from PDFs/Excel → convert to text/JSON → embed with metadata (column names, values) → hybrid search. Or use specialized models (Table-BERT). Query: "What are Q3 2024 revenue figures?" → retrieves exact table. (3) PDFs (scanned): OCR (Tesseract, GPT-4 Vision) → extract text + layout → embed with page numbers → retrieve with citations. Preserves formatting, tables, images. (4) Mixed documents: Single PDF with text + images + tables → extract each type → embed separately with same doc_id → unified retrieval. Example: Medical case with patient notes (text) + X-rays (images) + lab results (tables) → all searchable in one RAG system. (5) Multi-modal embeddings: New models (ImageBind, BLIP-2) embed text + images in same vector space → true multi-modal search. We implement: Custom pipelines for each data type + unified vector DB + multi-modal retrieval. Your RAG searches EVERYTHING!
We'll analyze your knowledge base and recommend the optimal embeddings, vector DB, and LLM (OpenAI, BGE, ChromaDB, Qdrant, Pinecone, GPT-4, Claude, Llama) - with detailed accuracy and cost projections.
