AI & Machine Learning Development

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.

Cost scales with complexity. An LLM-powered chatbot with RAG is the most contained tier and scopes lower. Custom computer vision systems with detection and inference pipelines scope higher. Full AI product development with custom model training, evaluation, and MLOps sits at the top. The real drivers are model complexity, data volume, and accuracy requirements. Ongoing API costs vary with usage, and we optimize for cost efficiency. We scope a firm figure on a free call before work starts.

Yes. We add AI features to existing applications, including 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.

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.

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.

We scope a dedicated AI/ML team to the seniority and product scope you need. LLM application work and MLOps are different skills, and both are part of the scope rather than billed on top. Each seat is full-time at 160 hours a month, and the team runs under us as your build partner. One call gets you the figure.

Hosted model APIs like GPT-4o and Claude bill per million tokens, and published rates shift often, so we price your workload against current provider rates rather than a stale number. A chatbot handling 1,000 daily conversations sits in a modest monthly API range, and we optimize prompts and implement caching to cut that by 40-60%. Self-hosted open-source models eliminate per-token costs after the initial setup. We scope your expected monthly API spend on a free call.

Yes. We have built RAG-powered support systems that take real load off the teams running them. A RAG pipeline with vector search, document ingestion, and a chat interface usually takes 6 to 8 weeks. What moves the cost is how big your knowledge base is and what it has to connect to. We put a dedicated AI team on it, scope it with you on a call, and can start within days.