AI Development
Intelligent solutions powered by machine learning
We build custom AI solutions — from RAG pipelines and LLM chatbots to computer vision and predictive analytics. Whether you need document intelligence, automated workflows, or conversational AI, we turn AI hype into measurable business value.
Custom AI Solutions
Our AI practice focuses on practical, production-ready implementations. We've built RAG systems processing millions of documents, fine-tuned LLMs for domain-specific tasks, and deployed computer vision models with 95%+ accuracy. Every AI project includes model evaluation, prompt engineering, and cost optimization.
RAG pipelines with LangChain, GPT-4o, and vector databases
Fine-tuned LLMs for domain-specific tasks
Computer vision (object detection, OCR, image classification)
Predictive analytics and recommendation engines
Conversational AI (chatbots, voice assistants)
MLOps pipelines for model training and deployment
Key Features
RAG Pipelines
Retrieval-Augmented Generation systems that ground LLM responses in your data. Build intelligent search, Q&A bots, and document analysis tools.
Fine-Tuned LLMs
Customize GPT, Llama, or Mistral models for your domain. Improve accuracy from 60% to 90%+ for specialized tasks.
Computer Vision
Object detection, facial recognition, OCR, and image classification using YOLO, TensorFlow, and OpenCV.
Conversational AI
Build chatbots with memory, context awareness, and tool use. Integrate with WhatsApp, Slack, or web chat.
Predictive Analytics
Forecast sales, churn, demand, or equipment failures using regression, time series, and ensemble models.
MLOps & Deployment
Production-ready ML pipelines with model versioning, A/B testing, monitoring, and automated retraining.
Our Process
AI Readiness Assessment
1 weekEvaluate data quality, define success metrics, identify use cases. Estimate ROI and feasibility.
Data Preparation
2-4 weeksClean, label, and augment training data. Set up data pipelines and vector databases.
Model Development
4-8 weeksExperiment with models, tune hyperparameters, evaluate accuracy. Iterate until performance targets met.
Integration & Testing
2 weeksIntegrate model into your application via API. Test edge cases, latency, and error handling.
Deployment & Monitoring
1 weekDeploy to production with load balancing and auto-scaling. Set up monitoring dashboards and alerts.
Retraining & Optimization
OngoingMonitor model drift, retrain on new data, optimize costs. Continuous improvement based on real-world feedback.
Technology Stack
LLMs & NLP
Vector Databases
ML Frameworks
Computer Vision
Pricing Options
AI MVP / Proof of Concept
Validate AI feasibility with a working prototype.
Production AI System
Full-scale AI solution integrated into your product.
Enterprise AI Platform
Large-scale AI infrastructure with custom models and MLOps.
Real-World Use Cases
RAG system that analyzes contracts for compliance risks. Reduced legal review time by 85%.
Product recommendation engine using collaborative filtering. Increased average order value by 30%.
Medical image classification for radiology. 96% accuracy in detecting anomalies.
Invoice processing with OCR + LLM. Automated 95% of data entry, 97% extraction accuracy.
Frequently Asked Questions
How much data do I need for AI/ML?
For supervised learning, 1,000-10,000 labeled examples is a good start. For fine-tuning LLMs, 500-2,000 high-quality examples can improve accuracy by 20-30%. For RAG systems, you can start with as few as 10 documents. We help assess your data readiness.
How accurate will the AI model be?
Depends on data quality and problem complexity. We typically achieve 85-95% accuracy for classification tasks, 90%+ for OCR, and human-level performance for LLM-based systems with RAG. We define success metrics upfront and iterate until targets are met.
What are the ongoing costs of running AI models?
OpenAI API costs $0.01-0.03 per 1K tokens (GPT-4o). Vector database hosting is $50-500/month. Self-hosted models (Llama) cost $200-2000/month in compute. We optimize costs by caching, model selection, and prompt engineering.
Can you integrate AI into our existing product?
Yes. We've added AI features to 20+ existing products. We build REST APIs that your frontend can call, minimizing changes to your codebase. Most integrations take 4-8 weeks.
Ready to get started?
Let's discuss your project and turn your ideas into reality.