Ship AI features that work in production — RAG, agents, and LLM integration built into real products.
Fremen Consulting builds AI-powered products and integrates large language models into existing software — RAG pipelines, agent workflows, and production-grade AI features using OpenAI, Anthropic, LangChain, and vector databases.
Problems we solve for businesses like yours
Prototype chatbots built on raw API calls hallucinate, leak context, and break under load. Without proper RAG architecture, evaluation, and guardrails, AI features erode user trust instead of delivering value.
Teams add AI because investors expect it without identifying workflows where LLMs genuinely improve outcomes. Unfocused AI investment burns budget without measurable product impact.
Connecting LLMs to your data, auth, billing, and existing product requires vector databases, embedding pipelines, prompt management, and observability — expertise most teams lack in-house.
Solutions tailored to your industry and growth goals
Retrieval-augmented generation pipelines with Pinecone, Weaviate, or pgvector that ground LLM responses in your documents, product data, and knowledge base for accurate, citeable answers.
Multi-step agent workflows with LangChain or custom orchestration that automate research, data extraction, customer support, and internal operations — with human-in-the-loop controls.
Prompt management, evaluation frameworks, cost monitoring, and fallback strategies so AI features perform reliably at scale on AWS or cloud-native infrastructure.
Measurable outcomes from projects in this space
We built a RAG-powered support assistant grounded in product documentation that resolved roughly 60% of tier-1 tickets without human escalation while maintaining citation accuracy.
Delivered an LLM-powered document analysis feature integrated into an existing SaaS product, from prototype to production with evaluation benchmarks and cost controls.
Clear answers to common questions in this industry
RAG (Retrieval-Augmented Generation) combines LLMs with a search step that retrieves relevant documents before generating a response. Use RAG when your product needs accurate answers grounded in proprietary data — support docs, product catalogs, legal files, or internal knowledge — rather than relying on the model's general training data.
Yes. We integrate OpenAI, Anthropic, and open-source models into existing web and mobile applications with proper auth, rate limiting, cost controls, and UX that fits your product — not a bolted-on chatbot widget.
We reduce hallucinations through RAG grounding, citation requirements, confidence scoring, evaluation datasets, prompt engineering, and human-in-the-loop review for high-stakes outputs. We also implement guardrails and fallback responses when the system cannot answer reliably.
We work with Pinecone, Weaviate, pgvector, and other vector stores depending on your scale, latency requirements, and existing infrastructure. The choice depends on data volume, query patterns, and whether you need managed or self-hosted solutions.
A focused AI feature integration typically takes six to ten weeks including data pipeline setup, RAG architecture, evaluation, and production deployment. Full AI-native products require longer depending on scope and compliance requirements.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.