LLMs That Work on Your Data,
Not Just Generic Prompts
Off-the-shelf ChatGPT wrappers are not Generative AI strategy. At Aavyalabs, we build production-grade GenAI systems grounded in your proprietary data — RAG pipelines, fine-tuned models, and agentic workflows that automate real business processes and deliver accurate, auditable outputs.
We also build and operate Maitil.AI — our GenAI platform for MSMEs. Talk to us if a white-label or hosted deployment fits your use case.
Our Generative AI Capabilities
From RAG knowledge systems to autonomous AI agents — we cover the full GenAI engineering stack.
Custom LLM Development & Fine-Tuning
Domain-specific language models fine-tuned on your proprietary data — delivering tone, accuracy, and terminology that off-the-shelf APIs cannot match.
RAG Systems & Knowledge Bases
Retrieval-Augmented Generation pipelines that ground AI responses in your documents, databases, and internal knowledge — eliminating hallucination in high-stakes deployments.
AI-Powered Chatbots & Assistants
Intelligent conversational agents for customer support, internal helpdesks, and guided workflows — with context memory, handoff logic, and multi-channel deployment.
Content Generation & Summarisation
AI pipelines that generate, rewrite, summarise, and classify text at scale — from product descriptions and marketing copy to report synthesis and document extraction.
Agentic AI Workflows
Multi-step AI agents that plan, reason, and act autonomously — browsing, searching, calling APIs, and completing complex multi-tool tasks without human intervention at each step.
Multimodal AI (Text + Image + Voice)
Solutions that combine text, image, and audio understanding — from document OCR and visual QA to voice-to-action pipelines and image-described content generation.
Service Quick Facts
Key delivery metrics and technical specifications at a glance.
| Metric / Attribute | Specification Details |
|---|---|
| Average Delivery Time | 4–8 weeks for RAG/chatbots; 8–16 weeks for multi-agent workflows |
| Typical Team Setup | 1 GenAI Solutions Architect, 1 Prompt/LLM Engineer, 1 Full-Stack Dev |
| Primary Technologies | OpenAI GPT-4, Anthropic Claude, Llama 3, pgvector, LangChain, Qdrant |
| Service Model | Phased Proof-of-Concept (POC) followed by production SOW |
| Post-Launch Support | Accuracy Drift Monitoring, Cost Guardrails Auditing, and Safety Filtering Updates |
Industries We Serve
We tailor GenAI solutions to the data, compliance, and workflow requirements of each industry.
Manufacturing
- AI-generated maintenance reports
- NLP-based work order processing
- Document intelligence for compliance
Finance & Banking
- Automated financial report summarisation
- AI underwriting narrative generation
- Compliance document analysis
Retail & E-Commerce
- AI product description generation at scale
- Personalised marketing copy
- Customer sentiment analysis & response
Healthcare
- Clinical note summarisation
- Patient FAQ assistants
- Medical document extraction & coding
Professional Services
- Proposal and contract generation
- Internal knowledge assistants
- Meeting summarisation & action extraction
HR & Recruitment
- AI-powered CV screening and ranking
- Job description generation
- Candidate communication automation
Our Delivery Process
A structured methodology from use-case definition to production deployment — with clear milestones and measurable quality gates at every stage.
Use Case Definition
We identify the highest-ROI GenAI opportunity in your business — defining the task, data sources, success metrics, and risk constraints before any model selection.
Data & Architecture Design
We design the data pipeline, retrieval architecture, and model strategy — choosing between RAG, fine-tuning, or agentic patterns based on your requirements.
Model Selection & Prompt Engineering
We evaluate candidate models, design and test prompt strategies, and validate baseline accuracy before committing to an architecture.
Pipeline Development
We build the full GenAI pipeline — ingestion, chunking, embedding, retrieval, generation, and output validation — with hallucination guardrails and safety filters.
Integration & Deployment
We deploy into your environment (cloud API, on-premise, or VPC) and integrate with your existing systems via webhooks, APIs, or embedded UI components.
Monitoring & Optimisation
Ongoing evaluation of output quality, latency, cost, and drift — with continuous prompt and retrieval tuning to maintain performance as your data evolves.
Why Aavyalabs for Generative AI?
Model-Agnostic Engineering
We're not tied to a single LLM provider. We choose GPT-4, Claude, Gemini, Llama, or Mistral based on what best fits your cost, latency, and data-privacy requirements.
Production-Ready, Not POC-Ready
We build for production from day one — with proper error handling, fallback logic, cost controls, output validation, and observability. No demo-ware.
Hallucination Mitigation Built In
Every RAG system we build includes source attribution, confidence scoring, and guardrail layers — so your AI doesn't confidently invent answers.
Data Privacy First
For sensitive use cases, we deploy open-source models on your own infrastructure — zero data leaves your environment.
Frequently Asked Questions
Common questions about building production Generative AI systems for business.
What is Retrieval-Augmented Generation (RAG)?
RAG is a technique that grounds an LLM's responses in your specific business data — documents, databases, or knowledge bases — rather than relying solely on the model's training data. This produces accurate, domain-specific answers without hallucination, and allows the AI to stay current as your data changes.
What is the difference between fine-tuning and RAG?
Fine-tuning bakes your data and tone into the model's weights — ideal for style, format, and domain vocabulary. RAG retrieves relevant context at inference time — ideal for factual accuracy, large knowledge bases, and frequently-changing data. Most production GenAI systems use both in combination.
Which LLM models does Aavyalabs work with?
We work with GPT-4o and GPT-4 Turbo (OpenAI), Claude 3 Opus and Sonnet (Anthropic), Gemini 1.5 Pro (Google), and open-source models including Llama 3, Mistral, and Falcon — choosing the right model based on cost, latency, data privacy, and task requirements.
How long does a Generative AI project take to deliver?
A focused GenAI integration — such as an internal knowledge assistant or a customer-facing chatbot — can be designed, built, and deployed in 4–8 weeks. More complex agentic workflows or multi-model production systems typically take 8–16 weeks depending on data complexity and integration depth.
How do you handle data privacy and security in GenAI projects?
We evaluate privacy requirements at the architecture stage — choosing on-premise, VPC-deployed, or API-based models based on your data classification. For sensitive data, we use private deployments of open-source models (Llama 3, Mistral) that never send data to third-party APIs.
Ready to Build Your First Production GenAI System?
Let's identify the right use case — the one with the highest ROI and lowest risk — and build a roadmap to get it live. Talk to our GenAI engineers today, no obligation.
Book a Free GenAI Consultation