Production OpenAI integrations — GPT 5.5, Codex, Image 2, Realtime 2, and cost controls.
Fremen Consulting integrates OpenAI APIs into products and workflows — GPT 5.5 chat and reasoning, GPT Codex for code generation, GPT Image 2 for visual content, GPT Realtime 2 for voice and live agents, plus embeddings and function calling with production guardrails, cost monitoring, and evaluation frameworks.
Problems we solve for businesses like yours
Direct OpenAI API calls without rate limiting, error handling, or cost caps create runaway bills and unreliable user experiences when models timeout or return unexpected formats.
Raw GPT responses without grounding, citation, or confidence scoring erode user trust — especially in customer-facing or compliance-sensitive applications.
Teams ship prompt changes without measuring quality regression, discovering problems only through user complaints rather than automated evals.
Solutions tailored to your industry and growth goals
OpenAI SDK integration for GPT 5.5 chat and GPT Codex code generation — streaming, tool use, retry logic, token budgeting, caching, and model routing for high-availability LLM features.
GPT Image 2 for visual generation and editing, GPT Realtime 2 for voice agents and live conversational UX — with latency tuning, session management, and production observability.
Embedding pipelines with vector search, context injection, citation formatting, prompt versioning, and automated eval datasets for grounded, verifiable answers.
Measurable outcomes from projects in this space
OpenAI RAG integration resolved roughly 60% of tier-1 support tickets with cited answers, reducing handle time and improving CSAT scores.
Clear answers to common questions in this industry
We integrate GPT 5.5, GPT Codex, GPT Image 2, GPT Realtime 2, embeddings, and function calling into web and mobile products with production-grade error handling, cost controls, RAG grounding, and evaluation frameworks.
Yes. We implement prompt optimization, response caching, and model routing — using GPT Codex for code tasks, GPT 5.5 for complex reasoning, and lighter models for simple requests — plus token budgeting and batch API usage.
Yes. We build retrieval-augmented generation pipelines using OpenAI embedding models with Pinecone, pgvector, or Weaviate for grounded answers with source citations.
We create evaluation datasets with expected outputs, run automated evals on prompt changes, track regression metrics, and integrate LangSmith or custom observability for production monitoring.
A focused GPT feature integration takes four to eight weeks. Full RAG systems with evaluation and production hardening typically take eight to fourteen weeks.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.