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AI and ML That Ship, Not Just Slide Decks
Automation, forecasting, and support intelligence built with monitoring, data discipline, and a path to production, not a one-off model demo.
Start with a focused use case; we help you pick what pays back fastest.
Practical AI Services for Real Business Outcomes
ML, NLP, vision, analytics, and assistants, each scoped with use cases, metrics, and a rollout plan you can track.
Not sure which to open first? One short call clears it up.
Machine Learning Development
Build custom ML models for prediction and classification with MLOps-ready deployment and scalable performance.
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Natural Language Processing
Deploy NLP pipelines for text analysis, sentiment, translation, and conversational AI with production reliability.
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Computer Vision
Use computer vision for detection, classification, moderation, and visual quality workflows across industries.
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Predictive Analytics
Build predictive analytics models for forecasting demand, risk, and customer behavior with decision-ready insights.
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AI Chatbots & Virtual Assistants
Create context-aware AI chatbots for support and sales across web, mobile, and messaging channels.
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Deep Learning Solutions
Develop deep learning systems with CNN, RNN, and transformer architectures for complex automation use cases.
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Data Science & Analytics
Turn raw data into business insight through analytics, modeling, dashboards, and automated reporting.
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AI Consulting & Strategy
Get strategic AI consulting to prioritize opportunities, build a roadmap, and deliver measurable ROI.
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Reinforcement Learning
Build reinforcement learning agents for autonomous decisions in robotics, game AI, and optimization systems.
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Need help prioritizing a use case?
Full Product Build Beyond the Model
Web, mobile, backend, cloud, and UX so your AI work plugs into a shippable product, not a siloed notebook.
On a call we can flag integration risks, hidden costs, and quick wins before you lock scope and budget.
From Data to Production (With MLOps in Mind)
Data prep, training, validation, deployment, and monitoring, so models stay useful after launch, not just accurate in a notebook.
Discovery & Analysis
1-2 weeks
Assess AI readiness, data availability, and high-impact use cases to define a practical roadmap.
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Data Preparation
2-3 weeks
Prepare training data with cleaning, feature engineering, and robust pipelines for reliable ML outcomes.
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Model Development
4-8 weeks
Build, train, and optimize custom AI/ML models with tuning, versioning, and measurable performance gains.
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Testing & Validation
1-2 weeks
Comprehensive testing with real-world data and validation of accuracy and performance
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Integration & Deployment
1-2 weeks
Integrate and deploy AI models with MLOps, CI/CD, APIs, and production-safe cloud architecture.
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Monitoring & Optimization
Ongoing
Continuously monitoring performance and retraining models for better accuracy
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Engage AI/ML Engineers the Way Your Roadmap Works
Hourly, dedicated, or fixed-scope, vetted engineers across deep learning, NLP, vision, and MLOps. Pick a model; we align the contract to how you want to run delivery.
HOURLY ENGAGEMENT
Pay-as-you-go. Perfect for AI/ML projects and MVPs.
- 3–5 Yrs Experienced AI/ML Engineers
- Daily Progress Updates
- Ideal for Quick Turnarounds
MONTHLY DEDICATED DEV
Hire a full-time AI/ML expert dedicated to your project.
- AI/ML Specialist
- Team Collaboration
- Priority Support
FIXED-COST PROJECTS
Get your complete AI/ML solution delivered within a timeline.
- Detailed Estimation & Scope
- Agile Sprint Milestones
- One-Time Payment
Book Time With Our AI Team
30 minutes to walk through use cases, data, and a realistic first milestone. Same slot on Cal.com: open booking.
Need one clear AI/ML roadmap across teams?
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AI & ML Services FAQs
How we scope ML and AI work, what production looks like, timelines, and how pricing maps to your roadmap.
We provide end-to-end AI and ML services: custom machine learning, natural language processing, computer vision, predictive analytics, conversational AI and chatbots, deep learning, reinforcement learning, data science, and AI consulting, from discovery and data readiness through model development, integration, production deployment, and MLOps.
ML development costs range from $10,000 for simple models to $100,000+ for complex deep learning systems. Our ML development rate is $25/hour. Cost is based on data requirements, model complexity, training time, and deployment infrastructure needs.
We use Python with TensorFlow, PyTorch, scikit-learn, XGBoost, and MLflow. For specific tasks, we use specialized libraries like Pandas for data processing, NumPy for numerical computing, and Hugging Face for pre-trained models. We choose frameworks based on your requirements.
Data requirements are defined by model scope. Simple models can use hundreds of examples, while complex models use thousands or millions. We can work with your existing data, help collect more data, use data augmentation techniques, or leverage transfer learning to reduce requirements.
Simple ML models take 2-4 weeks, medium complexity takes 4-8 weeks, and complex deep learning models take 2-4 months. Timeline includes data preparation, feature engineering, model training, evaluation, optimization, and deployment.
MLOps (ML Operations) involves deploying, monitoring, and maintaining ML models in production. We provide MLOps including model versioning, automated retraining pipelines, performance monitoring, A/B testing, and deployment automation using tools like MLflow and cloud ML services.
Model accuracy is driven by data quality, problem complexity, and algorithm selection. We target high accuracy through proper data preparation, feature engineering, and model selection. We provide accuracy metrics, confusion matrices, and continuously improve accuracy through iteration.
Yes, ML models need maintenance as data patterns change over time. We provide monitoring, retraining pipelines, performance tracking, and model updates. Models need retraining every few months to maintain accuracy as business conditions evolve.