From POC to production AI
AI & ML Services
We build computer vision, NLP, RAG/GenAI, and MLOps pipelines on AWS, with proper monitoring, versioning, and cost controls.
Get StartedWhat We Deliver
How We Approach AI & ML
Most AI POCs never make it to production. The model works in a notebook, the demo impresses investors, and then the project stalls because no one knows how to serve it reliably at scale. As a dedicated hire AI development company for startups, we build AI applications designed for production from day one, with proper APIs, monitoring, versioning, cost controls, and the ability to swap models as the landscape evolves.
We work across the full AI development stack: data pipelines, model selection, fine-tuning, RAG architectures, AI agent design, and MLOps. We build custom LLM applications using OpenAI, Anthropic, Mistral, and open-source models like Llama and Qwen. We know when to use each and how to keep costs from escalating.
For startups, we recommend a crawl-walk-run approach: start with off-the-shelf models and RAG for fast time-to-value, then move to fine-tuning and custom models once you understand your actual data. We help you make that journey without rebuilding everything from scratch at each stage.
Who is this for
Startups adding AI features to an existing product. Companies with valuable data that want to extract insight and automation from it. Enterprises building internal AI tools for document processing, customer service, or risk management. Teams that built an AI POC and need help shipping it to production.
Technologies
How We Work
AI Feasibility & Use Case Assessment
We assess whether AI is the right solution for your problem, which approach fits (RAG, fine-tuning, agent, classification), and what data you need. Many startups save months by getting this right at the start.
Data Assessment & Pipeline Design
We audit your existing data, identify gaps, and design the ingestion, cleaning, and feature engineering pipelines. Good models require good data. This step cannot be skipped.
Model Selection & POC
We build a working proof of concept in 2–3 weeks. You see real performance on your data before we commit to a full build. This is where we validate the approach.
Production Architecture
We design the serving infrastructure, API contracts, caching strategy, and fallback logic. AI systems in production need careful architecture: cold starts, rate limits, token costs, and latency all need to be solved.
MLOps & Monitoring
We set up model versioning, A/B testing infrastructure, performance dashboards, and drift detection. Your AI system should get better over time, not degrade silently.
Frequently Asked Questions
What is RAG and when should we use it?
RAG (Retrieval-Augmented Generation) connects an LLM to your own data (documents, databases, support tickets) so it can answer questions about your specific business. Use RAG when you need AI that knows your company's information without expensive fine-tuning. It is the fastest path to a useful AI product for most startups.
Fine-tuning vs RAG: which is right for us?
RAG is right when you want the AI to know your data. Fine-tuning is right when you want the AI to behave in a specific way: a particular tone, output format, or reasoning style. For most startups, RAG is faster and cheaper to start with, and fine-tuning becomes relevant once you have enough usage data to know what to optimise.
How much does it cost to build an AI application?
A production-ready RAG application development services engagement (covering chat interface, document ingestion pipeline, and basic monitoring) typically costs INR 8–15 lakh to build. Ongoing inference costs depend on usage and model choice. We help you architect for cost efficiency from the start.
Do you build AI agents?
Yes. We build single-agent and multi-agent systems using frameworks like LangGraph and custom orchestration. We design agents with proper human-in-the-loop controls and fallback logic.
Which AI models do you work with?
We are model-agnostic. We work with OpenAI (GPT-4o, o1), Anthropic (Claude Sonnet/Opus), Google (Gemini), Mistral, and open-source models (Llama 3, Qwen, Phi) on AWS Bedrock or self-hosted. We recommend the right model for your cost, latency, and capability requirements.
Can you help if we already have a POC?
Yes. We review the existing code, redesign the architecture for reliability and scale, add monitoring and cost controls, and ship it properly.
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