AI & Machine Learning Development
Intelligent products powered by modern AI
We build AI-powered products that solve real business problems — not proof-of-concepts that sit on a shelf. Our team has delivered 30+ AI projects in production, from RAG-powered customer support systems reducing ticket volume by 60% to computer vision quality inspection saving $2M/year in manufacturing defects. We work with OpenAI, Anthropic Claude, open-source models, and custom-trained systems. Need dedicated talent? Hire AI/ML engineers from our pre-vetted team, or explore our AI integration services for managed AI projects.
What sets our ai & ml development apart
LLM Application Development
We build production LLM applications with OpenAI GPT-4, Claude, and open-source models. Prompt engineering, function calling, structured outputs, and guardrails for safe deployment.
RAG Pipeline Architecture
Retrieval-Augmented Generation with vector databases (Pinecone, Weaviate, pgvector). Document ingestion, chunking strategies, hybrid search, and response quality evaluation.
Computer Vision Systems
Object detection, image classification, OCR, and video analysis for manufacturing quality control, document processing, and medical imaging. YOLO, PyTorch, and TensorFlow implementations.
NLP & Text Analytics
Named entity recognition, sentiment analysis, text classification, and document summarization. Custom fine-tuned models for domain-specific language understanding.
MLOps & Model Deployment
Model versioning with MLflow, A/B testing for model performance, automated retraining pipelines, and monitoring for data drift. Models that stay accurate in production.
What you can build with ai & ml development
Tools and frameworks we use
Common questions about ai & ml development
Should I use OpenAI, Claude, or an open-source model?
OpenAI GPT-4 and Claude are best for complex reasoning, long-form generation, and function calling. Open-source models (Llama, Mistral) are better when you need data privacy, lower latency, or cost control at high volume. We benchmark options on your specific use case and data before recommending.
How much does AI application development cost?
An LLM-powered chatbot with RAG costs $20,000-$50,000. Custom computer vision systems range from $30,000-$80,000. Full AI product development with custom model training, evaluation, and MLOps ranges from $50,000-$200,000. Ongoing API costs vary based on volume — we optimize for cost efficiency.
Can AI be integrated into our existing product?
Yes. We add AI features to existing applications — search with vector embeddings, content generation, automated categorization, smart suggestions, and document analysis. Most integrations take 3-6 weeks and connect through API endpoints that your existing code calls.
How do you handle AI accuracy and hallucinations?
We implement multiple guardrails: RAG with source attribution, structured output validation, confidence scoring, human-in-the-loop review for critical decisions, and automated evaluation suites. We test against ground truth datasets and set minimum accuracy thresholds before production deployment.
Do you handle data privacy for AI applications?
Yes. We configure data retention policies, use Azure OpenAI or self-hosted models when data cannot leave your infrastructure, implement PII detection and redaction, and ensure compliance with GDPR and HIPAA. Every AI project gets a data flow diagram showing where data moves.
How much does it cost to hire a dedicated AI/ML engineer?
Dedicated AI/ML engineers cost $3,000-$4,500/month for junior, $4,500-$6,500/month for mid-level, and $6,500-$10,000/month for senior engineers. LLM application developers and MLOps specialists are at the top end. Full-time, 160 hours/month. US AI engineers cost $15,000-$25,000/month.
How much do AI API costs add to monthly expenses?
GPT-4o costs $2.50-$10 per 1M tokens. Claude costs $3-$15 per 1M tokens. A chatbot handling 1,000 daily conversations costs $300-$800/month in API fees. We optimize prompts and implement caching to reduce costs by 40-60%. Self-hosted open-source models eliminate per-token costs after initial setup ($5,000-$15,000).
Can you build a custom RAG system for my enterprise knowledge base?
Yes. We built RAG-powered support systems that reduced ticket volume by 60%. A RAG pipeline with vector search, document ingestion, and chat interface costs $20,000-$50,000 and takes 6-8 weeks. Get a dedicated AI engineer from $4,500/month to start building your knowledge base AI in 1 week.
Developer Rates
| Level | Monthly Rate |
|---|---|
| Junior | $3,000-$4,500/mo |
| Mid-level | $4,500-$6,500/mo |
| Senior | $6,500-$10,000/mo |
AI Hiring Platform — 70% Faster Screening
70% faster candidate screening with RAG-powered analysis
Read full case study →Hire dedicated AI/ML engineers from $3,000/month to build intelligent products with LLMs, RAG pipelines, and computer vision. Our 10+ AI engineers work with OpenAI, Claude, PyTorch, and custom-trained models. Custom AI projects from $20,000 for chatbots to $200,000 for enterprise ML systems. Staff augmentation starts in 1 week with a paid trial.
Ready to build with ai & ml development?
Tell us about your project and get a detailed proposal within 48 hours. No commitment required.