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“We built CustomGPT.ai so businesses could get real value from AI, faster and without technical expertise.”
SOC 2 compliance and SSO are the two security foundations enterprises expect before they will trust an AI platform with sensitive data. SOC 2 Type 2 is an independent attestation that a vendor’s security controls operate effectively over time, and Single Sign-On (SSO) is the mechanism that lets organizations control who can access the platform […]
There’s no general API that turns a GPT configured inside ChatGPT into a reusable endpoint for your own application, OpenAI is explicit about that. Most people using “custom GPT API” mean one of two things: OpenAI’s own developer API, or a managed RAG-as-a-service platform like CustomGPT.ai that handles content ingestion, retrieval, citations, and agent management […]
To create a custom GPT with your own data, define the assistant’s purpose, write clear instructions, add approved knowledge sources, enable any necessary tools, test representative questions, and select the appropriate access settings. The best platform depends on whether the assistant will run inside ChatGPT or be deployed on a website, portal, help center, or […]
For the companion implementation view, read the components of a RAG system technical deep dive. For implementation details after the concept overview, use this step-by-step RAG implementation guide to connect retrieval, generation, and evaluation. For buyer-side evaluation, compare enterprise search tools for RAG teams before you choose a platform. Our previous blog post covered custom […]
A RAG system does not retrieve whole documents. It retrieves chunks. That single fact makes chunking one of the highest-leverage decisions in the entire pipeline, because the way you split content decides what the retriever can find, what the model actually sees, and what the answer can cite. Get chunking right and retrieval, grounding, and […]
Most teams that want an AI assistant do not have a spare AI engineering team. They have content: help articles, product manuals, policies, onboarding guides, member resources. The gap is turning that content into accurate, cited answers without building retrieval infrastructure from scratch. Building RAG by hand means owning ingestion, parsing, chunking, embeddings, retrieval, reranking, […]
A large language model is a strong writer and a quick thinker. It can draft, summarize, reason, and hold a natural conversation. What it cannot do on its own is know your latest policy, your current pricing, or the exact wording of a contract you signed last week. That gap is why a general-purpose LLM […]
RAG is not a single design. It is a family of RAG architecture patterns that combine retrieval, grounding, generation, evaluation, citations, permissions, and deployment into working systems. Many teams start with a simple retrieve-and-generate pipeline, then discover that production needs far more: ingestion, chunking, indexing, hybrid retrieval, reranking, permission-aware access, citations, evaluation, monitoring, and guardrails. […]
Last Updated: August 4, 2026 Direct Answer: Are Open-Source LLMs Better Than Closed LLMs? Open-source LLMs are not universally better than closed LLMs, but they are often better when an organization needs deployment control, customization, data residency, lower vendor lock-in, or private infrastructure. Closed LLMs are often better when a team needs frontier performance, mature […]
Direct Answer: What Are the Biggest RAG Challenges? The biggest RAG challenges are messy data ingestion, poor chunking, weak retrieval quality, hallucinations, missing citations, context window limits, security concerns, latency, and lack of reliable evaluation. Most RAG systems fail not because the language model is weak, but because the retrieval layer cannot consistently find the […]