Production-grade AI features — RAG, agents, and LLM integration built into your product.
Fremen Consulting integrates AI and large language models into web and mobile products — RAG pipelines, agent workflows, and OpenAI and Anthropic APIs with the guardrails production systems require.
Problems we solve for businesses like yours
Raw API calls without RAG grounding, evaluation, or guardrails produce hallucinations and unreliable UX that erodes user trust after launch.
Teams add LLM features without identifying workflows where AI measurably improves outcomes — wasting budget on demo-grade chatbots nobody uses.
Connecting LLMs to proprietary data, auth systems, and existing product architecture requires vector databases, embedding pipelines, and observability expertise.
Solutions tailored to your industry and growth goals
Retrieval-augmented generation with Pinecone, pgvector, or Weaviate grounding LLM responses in your documents, product data, and knowledge base.
Multi-step agent workflows with LangChain or custom orchestration for customer support, data extraction, and internal operations with human-in-the-loop controls.
Prompt management, evaluation frameworks, cost monitoring, and fallback strategies on AWS for reliable AI at scale.
Measurable outcomes from projects in this space
RAG-powered support assistant grounded in product docs resolved roughly 60% of tier-1 tickets without human escalation.
LLM document analysis feature integrated into existing SaaS product from prototype to production with evaluation benchmarks and cost controls.
Clear answers to common questions in this industry
We offer RAG pipeline development, LLM API integration (OpenAI, Anthropic), AI agent workflows, vector database setup, prompt engineering, evaluation framework design, and production deployment on AWS.
Yes. We integrate AI features into existing web and mobile applications with proper auth, rate limiting, cost controls, and UX that fits your product rather than a generic chatbot widget.
We use RAG grounding, citation requirements, confidence scoring, evaluation datasets, and human-in-the-loop review for high-stakes outputs. Fallback responses handle cases where the system cannot answer reliably.
We work with Pinecone, Weaviate, pgvector, and other vector stores depending on scale, latency requirements, and existing infrastructure.
Focused AI feature integrations typically take six to ten weeks. Full AI-native product modules take twelve to twenty weeks depending on data pipeline complexity and compliance requirements.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.