AI Infrastructure
Custom LLM Deployment That Actually Knows Your Business
A private AI model trained on your products, policies, and customer data, so every answer sounds like it came from your best employee.
The problem
Generic AI gives generic answers, and your customers can tell
"Off-the-shelf AI doesn't know our products, customers, or workflows." Every founder we talk to has hit this wall: ChatGPT can write a poem but it can't quote your pricing, follow your refund policy, or remember that your top client gets net-60 terms. The result is wrong answers in customer chats, hours wasted re-explaining context to the model, and a tool that never quite earns its keep.
An AI model that learns your business inside out
AIERAX deploys a custom large language model fine-tuned on your catalog, documents, support tickets, and standard operating procedures. We host it privately so your data never trains anyone else's product, and we wire it into the tools your team already uses. You get an assistant that answers like a senior employee on day one, not a generic chatbot guessing from public internet text.
How it works
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Discovery and data audit
We map where your business knowledge lives, including PDFs, spreadsheets, CRM notes, and tribal knowledge sitting in people's heads. Then we score which sources are worth training on first.
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Model selection and fine-tuning
We pick the right base model for your accuracy, speed, and budget needs, then fine-tune or retrieval-train it on your cleaned-up data. You approve the answer quality before anything goes live.
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Private hosting and integration
The model runs on your cloud or our managed infrastructure, never on a public AI provider's shared servers. We connect it to WhatsApp, your website, Slack, or any tool you already use.
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Guardrails and human handoff
We set rules for what the AI can and can't say, and route uncertain answers to a human teammate. No hallucinated refunds, no off-brand replies.
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Ongoing tuning
We track every conversation, retrain the model monthly on new data, and tune prompts as your product line grows. Your AI gets smarter the longer it runs.
A real example
A Chennai-based home services brand was fielding around 180 WhatsApp enquiries a day, with two staff burning four hours each just answering "do you service my pincode" and "what does package X include". AIERAX deployed a custom LLM trained on their service areas, pricing tiers, and FAQs, plugged into WhatsApp Business. Within three weeks the AI was handling 71 percent of first-touch replies automatically, saving roughly 35 staff hours a week and capturing an extra 22 qualified leads per month that previously went cold after 10pm.
Business outcome
- Answers grounded in your real data, not the public web
- Private hosting so customer data stays yours
- Lower per-query cost than public AI APIs at scale
- Plugs into WhatsApp, web chat, CRM, and email
- Human handoff for anything the model is unsure about
- Retrained monthly so it grows with your business
Technologies
- Llama 3
- Mistral
- Claude
- GPT-4 class models
- LangChain
- vLLM
- Pinecone
- AWS Bedrock
Common questions
How is a custom LLM different from just using ChatGPT?
ChatGPT is trained on the public internet and has no idea what your business sells, charges, or promises. A custom LLM is trained on your actual products, policies, and past conversations, so it answers with facts about your company instead of guessing. It also runs privately, so your customer data is never sent to a third party.
Do I need a huge dataset to train a custom LLM?
No. Most SMBs we work with start with a few hundred pages of documents, a year of support tickets, and a product catalog. Modern fine-tuning and retrieval techniques get strong results from small, clean datasets, especially when paired with the right base model.
Where will the model and our data actually live?
You choose. We can deploy on your own AWS, Azure, or GCP account, on a dedicated server in India, or on AIERAX-managed infrastructure. In every case your data stays in your environment and is never used to train any public model.
How long does a custom LLM deployment take?
A focused first deployment typically takes four to eight weeks from kickoff to go-live. That covers the data audit, fine-tuning, integration into one channel like WhatsApp or your website, and a supervised pilot before you switch traffic over.
What does ongoing maintenance look like?
We monitor every conversation, flag low-confidence answers, and retrain the model on new data on a monthly cadence. As you add products or change policies, you send us the update and we refresh the model, so it never drifts out of date.
Want this for your business?
Let's talk about what this looks like for your specific situation. We respond within 24 hours.