ML Models Built for
Your Data. Your Domain.
Domain specific machine learning models trained on your proprietary data for recommendation engines, fraud detection, document classification, computer vision, and more.
Models We Build
Every model is trained on your data for your specific domain not generic weights applied to your problem.
Recommendation Engines
Custom collaborative and content-based recommendation models that surface the right products, content, or actions to each user trained on your actual behaviour data.
Fraud & Risk Detection
Domain specific fraud models trained on your transaction patterns far more accurate than generic solutions because they understand your specific customer behaviour and fraud signatures.
Document Classification & NLP
Models that classify, route, and extract information from your documents support tickets, legal contracts, emails, medical records with accuracy tailored to your specific vocabulary.
Fine Tuned Language Models
LLMs fine tuned on your proprietary data so they write in your brand voice, understand your domain terminology, and generate outputs that match your specific quality standards.
Time-Series & Forecasting Models
Custom forecasting models built for your specific signal patterns energy consumption, financial markets, web traffic, or operational metrics outperforming generic libraries on your data.
Computer Vision Models
Purpose-built vision models for quality inspection, object counting, damage assessment, or medical imaging trained on your specific images rather than generic pretrained weights.
Our ML Development Process
Rigorous data science methodology we establish baselines, experiment systematically, and only deploy models that demonstrably outperform alternatives.
Problem Framing
We define the exact ML problem: what you're predicting, what success looks like, and what data signals are available before a single model is trained.
Data Audit & Engineering
We assess your data quality, volume, and labelling requirements then build the data pipelines and feature engineering needed to give the model the strongest possible signal.
Baseline & Experimentation
We establish a performance baseline with simpler models, then systematically experiment with more complex architectures choosing the best performing approach for your data and requirements.
Training & Evaluation
Models are trained with proper train / validation / test splits, cross validation, and rigorous evaluation against business relevant metrics not just standard ML benchmarks.
Deployment as API or Service
The final model is deployed as a fast, reliable API endpoint or embedded directly into your product with proper versioning, monitoring, and rollback capability.
Monitoring & Retraining Pipeline
We set up automated monitoring for model drift, accuracy degradation, and data quality issues plus a retraining pipeline to keep the model fresh as your business and data evolve.
Problem Framing
We define the exact ML problem: what you're predicting, what success looks like, and what data signals are available before a single model is trained.
Data Audit & Engineering
We assess your data quality, volume, and labelling requirements then build the data pipelines and feature engineering needed to give the model the strongest possible signal.
Baseline & Experimentation
We establish a performance baseline with simpler models, then systematically experiment with more complex architectures choosing the best performing approach for your data and requirements.
Training & Evaluation
Models are trained with proper train / validation / test splits, cross validation, and rigorous evaluation against business relevant metrics not just standard ML benchmarks.
Deployment as API or Service
The final model is deployed as a fast, reliable API endpoint or embedded directly into your product with proper versioning, monitoring, and rollback capability.
Monitoring & Retraining Pipeline
We set up automated monitoring for model drift, accuracy degradation, and data quality issues plus a retraining pipeline to keep the model fresh as your business and data evolve.
Industries & Use Cases
Tech Stack
ML Frameworks
NLP & LLMs
Computer Vision
MLOps & Serving
Infrastructure
Why Choose Digital Aura
- We train on your data not generic pretrained weights alone
- You own the model: weights, training code, and data pipelines
- MLOps from day one: versioning, monitoring, retraining pipelines
- Interpretable models where regulation or trust requires explainability
- Domain expertise across fintech, eCommerce, legal, healthcare, and SaaS
- Honest about what's achievable we won't oversell ML on thin data
Results You Can Expect
Real Clients. Real Growth. Real Results.
Across marketing, development & AI — real numbers from real businesses.
Riant Bikes
bookings processed through the new website, online booking system and fleet management we built.
Read Full Case StudyPrism Calibration Centre
qualified leads generated every month after we rebuilt their site into a search & AI-visibility engine.
Read Full Case StudyIVF Clinic
organic traffic growth in 6 months, turning a strong offline reputation into daily online demand.
Read Full Case StudyGuides & Insights
How to Rank in ChatGPT, Perplexity & AI Overviews
AEO vs GEO vs LLMO Explained: The Complete AI SEO Guide for 2026
6 AI SEO Skills Every Marketer Needs in 2026
SEO Cost in Ahmedabad in 2026: What Decides the Price
What Our Clients Say
"Patient inquiries tripled in 90 days. Doctors who never referred to us before now send us cases every week. Watch how Digital Aura made Gujarat only hand super-…"
Dr. Karan Maheshwari
Hand Surgeon · Krisha Hospital
"3 agencies failed to deliver this Shopify Plus and NetSuite integration. Digital Aura completed it in just 6 weeks seamlessly. Our client went from frustrated t…"
Sachin Salunkhe
Co-Founder · IntegsCloud Technologies
"Every agency gave me a standard pitch. Only Digital Aura actually listened. 6 months later 200 plus leads and my only problem now is I cannot make calls fast en…"
Nikhil Parasher
Founder · Parasher Academy
Frequently Asked Questions
Why build a custom ML model instead of using an existing AI API?
Generic AI APIs are trained on broad, public data and perform well for general tasks. But for domain-specific problems detecting your specific fraud patterns, recommending your specific product catalogue, classifying your specific support tickets a model trained on your data will significantly outperform any generic solution. The accuracy gap is typically 10-40% in our experience, and that gap directly translates to business impact.
How much data do I need to train a custom model?
It depends entirely on the problem. Image classification models can work with a few thousand labelled examples. Text classification can work with as few as 500-1000 examples with modern techniques like few shot learning and transfer learning. Fraud detection typically needs millions of transactions with labelled fraud cases. We always audit your data first and tell you honestly whether custom training is viable and what would improve it if not.
Who owns the trained model?
You own everything: the model weights, the training code, the data pipelines, and all associated IP. We hand over full ownership at project completion and document everything so your team can maintain, retrain, and extend the model independently.
How is a fine tuned LLM different from just prompting GPT-4?
Fine tuning adapts a model's weights to your specific data, vocabulary, writing style, and task so it becomes genuinely specialised for your domain rather than a general model with a long prompt. Fine tuned models are typically faster, cheaper per inference, more consistent in format, and significantly more accurate on domain-specific tasks than their prompted equivalents. They also run independently not dependent on a third party API.
Can you deploy the model inside our own infrastructure?
Yes. We deploy models as containerised API services that can run on your AWS, GCP, or Azure environment or on-premise if required. For regulated industries, fully private deployment is often mandatory and we design for this from the start.
How do you prevent model bias and ensure fairness?
We implement bias evaluation as part of the standard model development process testing performance across demographic segments, protected attributes, and edge cases before deployment. Where fairness is a regulatory requirement (e.g., credit scoring, hiring), we apply fairness aware training and provide documentation for compliance review.
Ready for a Model That's Actually Yours?
Book a free ML scoping session. We'll assess your data, define the right problem framing, and give you an honest picture of what's achievable before any commitment.
Start My ML ProjectReady to Build a Model Trained on Your Data?
Book a free ML Discovery Call. We'll assess your data, define the right model approach, and give you a clear picture of what a custom ML build would involve.
Book My ML Discovery CallNo off-the-shelf models — Custom trained on your data, for your specific outcomes.