AI That Works in Production, Not Just in Demos
We build and deploy machine learning models and AI systems that deliver measurable business value, with the MLOps infrastructure to keep them reliable, accurate, and auditable over time.
AI & ML
- Predictive Analytics
- LLM & ChatBot Applications
- Computer Vision
- NLP & Text Analytics
Predictive & Machine Learning Models
We build supervised and unsupervised ML models solving real business problems, from churn prediction and demand forecasting to anomaly detection and segmentation.
Get Started- Classification models: churn, fraud, lead scoring
- Regression models: demand forecasting, pricing optimization
- Clustering and segmentation for customer analytics
- Anomaly detection for operations and security
- Ensemble methods and gradient boosting (XGBoost, LightGBM)
LLM & Generative AI Applications
Leverage the latest large language models to build intelligent applications: document processing, knowledge bases, conversational AI, and content generation with proper guardrails.
Get Started- RAG (retrieval-augmented generation) architectures
- Document intelligence and extraction pipelines
- Enterprise knowledge base chatbots on internal data
- LLM fine-tuning for domain-specific tasks
- Responsible AI guardrails and output validation
MLOps & Model Lifecycle Management
Models that aren't monitored degrade silently. We build the MLOps infrastructure to track model performance, detect drift, and manage the full model lifecycle.
Get Started- MLflow and Weights & Biases for experiment tracking
- Model registry with versioning and approval workflows
- Automated retraining pipelines on data drift
- A/B testing frameworks for model rollout
- Model performance dashboards and drift alerting
Computer Vision & NLP
Specialized AI for unstructured data, including images, documents, and text, turning raw inputs into structured, actionable information.
Get Started- Object detection and image classification (YOLO, ResNet)
- Document OCR and intelligent data extraction
- Named entity recognition and text classification
- Sentiment analysis and topic modeling
- Speech-to-text and audio processing pipelines
What We Deliver
A comprehensive set of AI & ML capabilities, designed to work together or independently.
Predictive Analytics
Churn, demand, fraud, and lead scoring models connected to your data warehouse.
LLM & ChatBot Applications
RAG-based enterprise chatbots and document intelligence on your internal data.
Computer Vision
Object detection, image classification, and visual inspection automation.
NLP & Text Analytics
Entity extraction, classification, and sentiment analysis for unstructured text.
MLOps Platform
MLflow model registry, drift monitoring, and automated retraining pipelines.
Recommendation Engines
Collaborative and content-based filtering for product and content recommendations.
From problem framing and data prep through model training, deployment, and ongoing monitoring.
Every model deployed with experiment tracking, registry, monitoring, and retraining pipelines.
Fairness, explainability, and bias testing embedded in every ML project.
Why Choose InnovTen
We don't just deliver projects. We build partnerships that drive long-term outcomes.
Business-Problem-First
We start with the business problem and work backward to the model, not the other way around.
Responsible AI
Fairness audits, explainability, and bias testing built into every ML project.
Production MLOps
Models don't degrade silently. Drift detection and retraining pipelines keep them accurate.
Stakeholder Explainability
SHAP values and model explanations that non-technical stakeholders can understand and trust.
Data Privacy
Differential privacy, data anonymization, and on-premises model options for sensitive data.
Model Documentation
Model cards, training data documentation, and performance benchmarks for every deployed model.
Our Delivery Process
How we approach every AI & ML engagement, from first call to ongoing operations.
Problem Framing
Translate the business problem into an ML problem statement with clear success metrics.
Data Preparation
Feature engineering, data cleaning, and train/validation/test split with quality validation.
Model Development
Iterative model training, experiment tracking, and hyperparameter optimization.
Evaluation & Validation
Performance evaluation, fairness testing, explainability analysis, and business validation.
Deploy & Monitor
Production deployment with API serving, drift monitoring, and retraining pipeline.
Problem Framing
Translate the business problem into an ML problem statement with clear success metrics.
Data Preparation
Feature engineering, data cleaning, and train/validation/test split with quality validation.
Model Development
Iterative model training, experiment tracking, and hyperparameter optimization.
Evaluation & Validation
Performance evaluation, fairness testing, explainability analysis, and business validation.
Deploy & Monitor
Production deployment with API serving, drift monitoring, and retraining pipeline.
AI & ML in Action
Real-world applications across industries we've delivered for.
Customer Churn Prediction
XGBoost churn model identifying at-risk customers 30 days in advance, enabling retention campaigns that reduced churn 25%.
Document Intelligence Pipeline
LLM-powered document extraction processing 10,000 claims documents/day, reducing manual review time by 80%.
Real-Time Fraud Detection
Gradient boosting fraud model scoring transactions in <10ms with 97% precision, deployed on SageMaker with drift monitoring.
Demand Forecasting
Multi-variate time series model forecasting SKU-level demand 8 weeks out, reducing overstock by 22% and stockouts by 18%.
Frequently Asked Questions
Common questions about our AI & ML services.
It depends heavily on the problem type. Simple classification models can work with a few thousand labeled examples. Deep learning typically needs tens of thousands or more. We assess your data volume and quality early in the engagement and are transparent about whether it's sufficient for the desired outcome.
RAG (retrieval-augmented generation) retrieves relevant documents at inference time and passes them to an LLM as context: it's faster to build, cheaper, and keeps knowledge up-to-date. Fine-tuning bakes knowledge into the model weights, which is better for specific tone, format, or task specialization. Most enterprise use cases are better served by RAG.
We implement data drift and concept drift monitoring using statistical tests on incoming data distributions. When drift exceeds defined thresholds, automated retraining pipelines trigger, the new model is evaluated against holdout data, and if it passes validation gates, it's promoted to production.
Both. For LLMs, we evaluate the trade-offs between commercial APIs (OpenAI, Anthropic, Google) and open-source models (Llama, Mistral) based on your data privacy requirements, latency targets, and cost tolerance. On-premises open-source deployment is common for sensitive data use cases.
Ready to Get Started with AI & ML?
Tell us about your project. We'll respond within 24 hours with a clear next step.