Production-Ready AI Development: Custom LLMs, Autonomous Agents & Enterprise RAG.
Move beyond fragile prompt wrappers. We engineer high-accuracy, private AI pipelines, autonomous multi-agent systems, and production RAG architectures with sub-200ms latency, zero hallucinations, and bank-grade data security.
Architecture Efficiency: 99.4%
Semantic vector cache hits, prompt token compression, and dense/sparse hybrid search.
The 3 Foundations of Enterprise AI Systems
Building production AI is not about sending basic API calls to ChatGPT. It requires low-latency retrieval, verifiable accuracy, and ironclad enterprise data privacy.
High-Precision RAG & Vector Grounding
We implement hybrid retrieval architectures combining dense vector embeddings with sparse BM25 keyword matching and Cohere cross-encoder rerankers to eradicate hallucinations.
- 99.8% factual accuracy with verifiable document citations
- Semantic chunking preserving tables, PDFs & code syntax
- Role-Based Access Control (RBAC) ensuring data boundaries
Autonomous Agents & Tool Calling
We architect multi-agent systems using LangGraph where AI agents can plan multi-step tasks, query internal SQL databases, call third-party APIs, and resolve complex user intents autonomously.
- Deterministic tool execution with human-in-the-loop checkpoints
- Stateful session memory and self-correcting reflection loops
- Instant integration with CRMs, ERPs, Slack & WhatsApp
Semantic Caching & 80%+ Cost Reduction
Token costs explode when scale hits. We deploy semantic vector caching with Redis, intelligent prompt token pruning, and small-model routing (SLMs) to cut compute bills by up to 84%.
- Sub-180ms responses for recurring user semantic intents
- Multi-tiered model routing (Fast SLM ➔ Reasoning LLM)
- Zero vendor lock-in with unified abstraction layers
6 Specialized AI Architectures We Engineer
From custom enterprise knowledge bases to fully autonomous agents, explore the engineered AI framework designed for your company's operational bottlenecks.
Enterprise RAG & Document Intelligence
Connect your company's private Notion, Google Drive, Jira, and PDF archives into an ultra-fast semantic search engine that delivers verifiable answers with citation links.
- Hybrid Vector + BM25 Search with Reranking
- Document Chunking & Table Parser Accuracy
- Role-Based Multi-Tenant Permissions
Autonomous Multi-Agent Systems
Deploy goal-oriented AI agent teams that reason, delegate sub-tasks, execute SQL queries, update CRM pipelines, and draft responses with zero human intervention.
- LangGraph Orchestrator-Worker Multi-Agents
- Deterministic Function Calling & API Triggers
- Automated Audit Logs & Guardrail Tracing
Fine-Tuned Open-Source LLMs
Domain-specific fine-tuning (LoRA / QLoRA) on Llama 3.1 and Mistral models, deployed on your private AWS/GCP cloud VPC with zero third-party data leakage.
- Custom Dataset Prep & Instruction Tuning
- vLLM High-Throughput Token Serving
- 100% On-Premises & Air-Gapped Deployment
Conversational AI & Real-Time Voice Agents
Human-grade conversational agents for your website, app, or telephony lines with ultra-low latency voice synthesis, emotional intonation, and instant interruption handling.
- Sub-400ms Voice-to-Voice Streaming Pipeline
- Deepgram Transcription & ElevenLabs Voice
- Inbound Support & Outbound Sales Qualification
Predictive ML & Custom Vision Models
Bespoke machine learning models and computer vision pipelines trained on your historical company data for churn prediction, dynamic pricing, and automated document OCR.
- Churn Prediction & Customer LTV Forecasting
- Document OCR & Automated Invoice Extraction
- Real-Time Microservice API Endpoints
Full-Stack AI SaaS & Web Platforms
Turn your AI algorithm into a full commercial SaaS product with modern Next.js/React frontends, FastAPI backends, user authentication, and recurring Stripe billing.
- Interactive Next.js Frontend with Streaming UI
- High-Throughput Asynchronous FastAPI Backend
- Tiered Stripe Subscriptions & Usage Credit Logic
Enterprise Outcomes From Our AI Deployments
Real latency numbers, token cost reductions, and operational automation metrics achieved for forward-thinking enterprises.
Apex Capital Intelligence
Legal analysts spending 14 hours per audit reviewing 120,000 regulatory compliance PDFs. Built custom enterprise RAG with citation links and strict role-based data governance.
Kallos Retail Omni-Support
64,000 monthly customer inquiries overwhelming human agents during festive sales. Engineered autonomous multi-agent resolving orders, refunds, and tracking in real-time.
BioScribe Clinical Platform
Physicians losing 3 hours daily writing patient chart summaries. Fine-tuned private Llama 3 model summarizing clinical audio encounters with zero third-party data exposure.
Generic API Wrapper vs Zavryn Enterprise AI
Compare the engineering reality of a superficial GPT wrapper against an enterprise-grade Zavryn AI system.
Fragile Toy / Basic Prompt Wrapper
- Raw OpenAI API Calls: Sending raw user questions without semantic vector retrieval, resulting in 15%+ hallucination rates.
- Runaway Token Expenses: Zero semantic caching. Repetitive questions re-billed constantly, producing massive month-end API bills.
- 4+ Second Response Latency: Slow, blocking API round-trips causing user frustration, high drop-offs, and browser timeouts.
- Critical Privacy Liabilities: Proprietary company documents fed directly into public LLM endpoints with zero data residency safeguards.
- Rigid Single-Turn Prompts: Unable to take actions in external software, query internal databases, or execute multi-step workflows.
Production ZAVRYN AI Architecture
- Hybrid RAG & Verifiable Citations: Dense vector + BM25 keyword retrieval with cross-encoder rerankers guaranteeing <0.2% hallucinations.
- Semantic Vector Caching: Redis-backed semantic caching slashes redundant LLM token costs by over 80% on frequent user intents.
- Sub-180ms Edge Latency: Asynchronous streaming token generation and optimized model serving providing instant desktop responsiveness.
- Private Cloud VPC Isolation: Zero data leakage. Open-source weights (Llama 3/Mistral) or zero-retention enterprise API endpoints.
- Autonomous Multi-Agent Tooling: Self-correcting LangGraph workflows that query databases, update CRMs, and trigger webhooks.
Our 5-Stage AI Engineering Sprint
A disciplined, production-first roadmap that translates business bottlenecks into autonomous, high-accuracy AI systems with verified ROI.
Data & Feasibility
We audit your unstructured documents, database schemas, and compliance requirements to determine optimal model selection.
RAG & Vector Build
We engineer chunking strategies, set up vector databases (Qdrant/Pinecone), and calibrate hybrid search retrieval pipelines.
Agents & Tool Calling
We construct autonomous LangGraph agent workflows, integrate webhook tools, and implement prompt security guardrails.
Cache & Speed Tuning
We deploy semantic vector caching in Redis, tune token streaming response latency, and run penetration security checks.
VPC Go-Live & Eval
We deploy to your private VPC, set up automated Ragas benchmark evaluation tracking, and train your technical team.
Let's Engineer Your Production AI Architecture
Share your project specifications to receive a comprehensive technical blueprint, model feasibility assessment, and fixed-cost delivery timeline within 24 hours.
Need immediate technical consultation on RAG architectures, multi-agent frameworks, or private cloud VPC deployment? Chat directly with our AI architect.
Chat on WhatsApp Now →Frequently Asked Questions
Everything you need to know about our enterprise AI engineering, RAG accuracy guarantees, and data security protocols.
Stop Experimenting with Toy Prompts. Engineer Production Enterprise AI.
Partner with Zavryn to architect private RAG pipelines, autonomous agentic workflows, and fine-tuned open-source models with verifiable accuracy and sub-200ms speeds.