Model Fine-Tuning: Train AI on Your Data
Train specialized AI models on your company manuals, past bids, and proprietary engineering documents. Run everything securely inside your private cloud account.
Why Generic Public AI Fails Industrial and Technical Operations
Public AI tools hallucinate part numbers, technical tolerances, and state-specific regulatory requirements.
Uploading sensitive blueprints, pricing models, and client contracts to consumer AI portals risks proprietary data leaks.
Generic chatbots cannot parse custom engineering jargon, internal abbreviations, or legacy ERP item descriptions.
Relying solely on keyword search returns dozens of irrelevant PDF pages instead of precise operational answers.
What We Build For Your Enterprise
Curated training datasets built from your verified manuals, past quotations, standard operating procedures, and technical logs.
Domain-specific fine-tuning of leading open-weight models adapted specifically for your industry terminology.
Dedicated cloud deployment inside your private VPC on AWS, Microsoft Azure, or Google Cloud.
Role-based access controls and audit logging ensuring staff only query data appropriate to their clearance.
What Fine-Tuning Cannot Fix
- ×Model accuracy depends entirely on document truthfulness. If your manuals contain conflicting guidelines, the model cannot guess which is correct.
- ×Fine-tuning is not suitable for static data that can be solved with a simple SQL database lookup.
- ×Requires ongoing evaluation as your company releases new product versions or updates internal procedures.
Model reliability depends on source document accuracy, data curation, and team usage. We audit your documentation quality in our 2-week pilot before training production models.
Frequently Asked Questions
Where does the fine-tuned model run, and who owns the weights?
The model runs entirely inside your own private cloud account on AWS, Microsoft Azure, or Google Cloud. You own 100 percent of the resulting weights, code, and training pipelines. We use Google Colab only during initial data preparation and experimental tuning.
Is our proprietary company data ever sent to external third parties?
No. Your proprietary data never leaves your controlled environment. We implement strict cloud data perimeters so your sensitive IP remains completely private.
How many internal documents are needed to fine-tune a model?
Quality matters much more than raw volume. A verified dataset of 200 to 500 clean question-and-answer pairs or structured procedure guides is often enough to achieve high domain precision.
What is the expected timeline and cost?
A 2-week pilot establishing benchmark accuracy on a single document set costs between Rs 1.5 Lakh and Rs 3 Lakh. Full training and private cloud production rollout ranges from Rs 5 Lakh to Rs 12 Lakh.
Ready to build a private AI model for your company?
Book a free 20-minute call. We evaluate your document archive and outline a secure pilot plan.