Custom AI Models
From Raw Data to Real Intelligence.
We design, train, and deploy custom AI models tailored to your data, domain, and business goals—delivering capabilities that generic models simply can’t.
At CuriousAI, our mission is simple – turn intelligent technology into practical tools that help teams move faster and think bigger.


What We Offer?
Domain-Tuned LLMs
We fine-tune large language models (LLMs) on your internal data, making them fluent in your language, product, and workflows.
- Fine-tuned on your content and use cases
- Reduced hallucination, increased accuracy
- Supports open-source or proprietary base models



Retrieval-Augmented Generation (RAG) Pipelines
Build AI that knows where to look. We implement RAG pipelines that combine LLMs with real-time data access for grounded, context-aware responses.
- Integrated with your data sources and APIs
- Fast and accurate output with minimal drift
- Designed for high-recall, low-latency tasks
Predictive & Generative ML Models
From demand forecasting to image generation, we create custom models for specific tasks using structured, unstructured, or multimodal data.
- Regression, classification, and sequence models
- Text, vision, or hybrid (multimodal) capabilities
- Model ops, versioning, and performance monitoring



Private & On-Prem Deployments
Your model, your rules. We offer full control over where and how your models run—with secure, private infrastructure.
- On-prem or VPC-based hosting
- Fine-grained access and data policies
- Fully auditable and enterprise-ready
Where Custom AI Models Make a Difference:
Extract and summarize insights from contracts, reports, invoices, and legal docs using domain-specific models that understand structure and nuance.
Contract review and risk flagging
Invoice matching and reconciliation
Legal and compliance document parsing
Predict trends, personalize recommendations, and generate content at scale using models trained on your catalog, customer data, and sales history.
Personalized product feeds
Price optimization & demand forecasting
Review analysis and generation
Train models on medical literature, patient records, or clinical data to unlock new insights and streamline processes in regulated environments.
Symptom checkers and diagnostic aides
Drug discovery research copilots
Lab report summarization
Enable AI-powered content generation, editing, tagging, and summarization workflows with models trained on your brand voice and audience behavior.
Copywriting assistants & ideation tools
Audio-to-text + AI editing pipelines
Auto-tagging and metadata generation
From automating workflows to augmenting decision-making, our custom models power smarter operations across industries:

Why CuriousAI?
At CuriousAI, we don’t just fine-tune—we collaborate deeply to understand your data, challenges, and workflows, ensuring that every model we build is usable, secure, and strategically valuable. With expertise across NLP, computer vision, and tabular AI, we turn your datasets into intelligent systems that work in the real world.



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Our amazing clients are industry experts around the world.
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We're here to answer all your questions.
What are custom AI models?
Custom AI models are machine learning models specifically built and trained for your business using your data, objectives, and workflows. Unlike off-the-shelf AI tools, these models are tailored to solve your unique challenges with higher accuracy and adaptability.
What kinds of problems can you solve with custom AI models?
We build models for prediction, classification, recommendation, forecasting, personalization, anomaly detection, computer vision, NLP, and more. Whether you’re optimizing inventory, scoring leads, detecting fraud, or automating decisions—we can build AI around it.
How do you decide what kind of AI model to build?
We start with a discovery session to understand your business goals and available data. Based on that, we identify the right AI approach—be it supervised learning, unsupervised learning, deep learning, reinforcement learning, or a hybrid method.
What industries do you work with?
We serve a wide range of industries including SaaS, D2C, logistics, healthcare, fintech, education, and enterprise IT. Our modular approach allows us to adapt our models for industry-specific use cases.
Do I need large volumes of data to build a custom AI model?
Not necessarily. While more data can improve model performance, we also build effective models using small or synthetic datasets. For early-stage projects, we can start with baseline models and enhance them as more data becomes available.
Can you work with our internal tech team or data scientists?
Yes. We often collaborate with in-house teams to align our models with your existing architecture and ensure smooth handovers. If needed, we can also act as your dedicated AI team.
Where are the models hosted? Who owns the IP?
We deploy models on cloud platforms like AWS, Azure, GCP—or on your own servers. Unless agreed otherwise, you retain full ownership of the IP, codebase, and trained models.
How do you evaluate and improve model accuracy?
We use proven metrics like precision, recall, F1-score, AUC, and business-specific KPIs. Once deployed, we monitor model drift, collect feedback, and retrain models periodically to keep them accurate and relevant.
How long does it take to build a custom AI model?
It depends on complexity and data readiness. MVPs or proof-of-concept models can be built in 2–4 weeks. Production-grade models with full integration typically take 6–10 weeks.
Is post-deployment monitoring and retraining included?
Yes. We offer ongoing support and monitoring packages that include retraining, tuning, and updates to ensure the model continues to perform well as your business and data evolve.