Generative AI chatbots have human-like conversational abilities and can understand and respond to natural language, even somewhat comprehending context. These bots now offer unparalleled customer support automation and valuable internal tools. There are now several widely available models companies can use to leverage AI’s benefits.
Over twelve months after the launch of OpenAI’s ChatGPT, a number of generative AI large language models (LLMs) are now on the market and available to underpin business-focused chatbot applications. Following our first look at types of chatbots, we delve deeper into the generative AI chatbot options available to businesses that use OpenAI, Google, Amazon, and Cohere technology.
OpenAI Custom GPTs and Assistants
Custom GPTs
In November 2023, OpenAI rolled out the ability to create custom versions of ChatGPT to the public. These GPTs can be made on OpenAI’s site in a browser-based dashboard, without coding, and by providing “instructions and extra knowledge, and picking what it can do, like searching the web, making images or analyzing data.” per the OpenAI announcement which says:
“Anyone can easily build their own GPT—no coding is required. You can make them for yourself, just for your company’s internal use, or for everyone.”
These GPTs allow customization of ChatGPT but generally reside at their appropriate link at chat.openai.com and can only be accessed by users with a ChatGPT Plus subscription. They can’t be easily embedded as a widget or live chat on a website, and any specific data uploaded or conversations may be used to train OpenAI’s model if users don’t opt out. Chat logs aren’t available to the everyday GPT builder. However, per OpenAI’s announcement, developers can use APIs to connect GPTs to other applications, and enterprise customers can deploy internal-only GPTs.
CustomGPT.ai provides a business-grade platform to create custom chatbots that can be populated only with business content and embedded into a business website. These bots use OpenAI’s advanced large language models (LLMs) but with RAG technology and guardrails. We’ll cover these generative AI chatbots as an option for businesses shortly in this article. However the blog post
ChatGPT GPTs vs CustomGPT: An In-Depth Comparison to Help You Choose provides an immediate side-by-side comparison that delves into the full capabilities of both ChatGPT GPTs and CustomGPT.ai custom chatbots.
OpenAI Assistants
Where GPTs are built within ChatGPT’s dashboard, the OpenAI Assistants API allows users to build assistants with their own applications. Assistants can answer end-user questions by leveraging specific files the developing company provides, but creating Assistants requires developer-level coding.
The Assistants API beta was launched at OpenAI’s developer conference in November 2023 to allow the creation of “agent-like experiences” within apps. Assistants can have specific instructions and use data outside of OpenAI’s model as well as the model’s ability to perform tasks. Assistants can be used to build a GPT-powered chatbot on a website. This OpenAI tool is billed to developers based on feature usage and any data is not used to train the company’s models by default.
Google Vertex AI Search & Conversation
In August 2023, Google unveiled new generative AI tools in the form of Google Vertex AI Search and Conversation. The tools allow users to create search and conversation apps (chatbots) that use Google’s LLM PaLM 2. Developers can use their own data, customize the apps, and launch either AI search engines or chatbots to serve customers or employees.
Google Cloud vice presidents Amin Vahdat and Burak Gokturk explained:
“These products enable even developers with little machine learning expertise to build and deploy intelligent apps in as little as a few hours.”
The VPs add, “Enterprise developers can quickly ingest data, add customization, and, with a few clicks, build a search engine or chatbot that can interact with customers and answer questions grounded in the factuality of their enterprise website along with specified structured and unstructured data sources.”
Vertex Conversation allows developers to pre-program prompts and responses in natural language and decide if they want their own data to be supplemented by Google’s foundation model’s training data. Conversation supports audio and text and can display citations or produce interaction summaries that can be handed over to human agents. The use of Google’s tools, aimed at enterprise users, is charged for every 1,000 characters of input (prompt) or output (response).
The chatbots we’re covering in this article use retrieval-augmented generation (RAG) technology. All have recently been evaluated by developer data solutions company Tonic and benchmarked using Tonic Validate, including CustomGPT.ai, which outperformed each one.
Find how OpenAI’s Rag Assistants stacked up against Google’s Vertex Search and Conversation.
Amazon Titan
Available via Amazon Web Services (AWS), Amazon’s Bedrock and Titan Generative AI Services became widely available in October 2023. They allow customers to build generative AI-based applications through an API using several LLMs. Amazon Bedrock works a little like a library of foundation models available in the cloud. Amazon’s Titan model, named Titan Embeddings, is an LLM trained for the model customization technique RAG, giving companies the ability to use their proprietary data.
Amazon Titan’s foundational model can be connected to your own data sources. It then uses RAG technology to make a chatbot that is knowledgeable about your business, purpose, or specific topic. Actually building a Titan chatbot does require AWS and Bedrock access, for which there are costs and coding experience to build the bot’s user interface (UI).
Cohere
Cohere is a Canadian enterprise-focused AI company that was founded in 2019 by ex-Google and University of Toronto co-founders. Its investors include Nvidia, Oracle, and Salesforce, and its infrastructure is powered by Google Cloud.
Cohere generative AI is already used in Oracle products and its chat capabilities in Salesforce. In September 2023, it joined the competition to provide the business world with easy-to-build AI chatbots by launching an API to allow enterprise developers to build chatbots using the Cohere LLM, called Command. Cohere launched its Coral chatbot in July 2023, but the API adds the facility for companies to build chatbots into their own internal or external applications.
Cohere also uses RAG technology to allow developers to limit chatbots to proprietary company data or equally allow them to use information from the web. Cohere explained the advantages of RAG at the time:
“RAG systems improve the relevance and accuracy of generative AI responses by incorporating information from data sources that were not part of pre-trained models.”
Cohere’s Generate, Summarize, and Chat APIs are all aimed at developers to build into their own products. The costs vary based on the model used and the number of tokens, and there’s custom pricing for enterprise users.
CustomGPT.ai
CustomGPT.ai is a simple-to-use privacy first business-grade platform for users to create custom GPT chatbots that use OpenAI’s advanced LLM technology plus RAG in minutes with zero-coding that can generate responses from proprietary content provided for better accuracy.
These bots can be easily embedded into a website for customer engagement or deployed internally for employee productivity. The CustomGPT.ai blog shares chatbot use cases, benefits, case studies, and how to integrate CustomGPT.ai quickly with popular website builders and applications.
Start your CustomGPT.ai journey by learning how to Create chatbots using RAG technology through CustomGPT.
Tonic.ai has recently published its RAG Evaluation Leaderboard with CustomGPT.ai as the clear leader for accuracy ahead of OpenAI Assistants, Google’s Vertex Search and Conversation, Amazon Titan, and Cohere.
Frequently Asked Questions
What matters most when choosing a generative AI chatbot that reduces hallucinations?
What matters most is grounding quality, not model brand. Choose a chatbot that answers only from approved business content, shows citations, and refuses to guess when the source is missing.
If you are building a business chatbot on your own manuals, internal documents, or books, test whether it answers from only those approved sources instead of filling gaps with general model knowledge. Ask each vendor, such as Microsoft Copilot, Glean, or CustomGPT.ai, to answer 10 real questions from your documents and require a citation for every answer; if it cannot cite the source, treat the answer as unverified. Also verify file ingestion, access controls, and passage-level retrieval, because hallucinations often start with bad OCR, oversized chunks, or stale permissions before the model writes anything. MIT reports zero hallucinations in a published deployment across 90+ languages, which shows the winning setup is tight source control plus reliable retrieval, not just a famous LLM.
Should businesses separate public-support and internal AI assistants?
Yes. Most businesses should separate a public support bot from an internal AI assistant because they serve different users, data, and risk levels.
Separate them when the customer bot should answer questions only from approved website or help-center content, while the internal assistant needs access to private documents, policies, manuals, books, SOPs, or role-based workflows. Many teams start by asking whether a custom assistant trained on their own documents can do more than ChatGPT. In practice, the public bot is usually best limited to curated public knowledge, and the internal assistant should sit behind SSO with tighter permissions, audit logs, and retention rules. For example, a website bot can answer product and refund-policy questions, while an employee assistant can search onboarding docs or case files. At GEMA, a public-facing AI assistant handles 248,000+ inquiries with an 88% success rate. Tools like Intercom Fin, Microsoft Copilot Studio, or CustomGPT.ai are often deployed separately for exactly this reason.
What is the practical difference between OpenAI Custom GPTs and the Assistants API for business use?
Custom GPTs are best for quick internal assistants inside ChatGPT. The Assistants API is better when you need the assistant inside your own website, app, or customer portal, with your own login, UI, workflows, and integrations.
Choose a Custom GPT if your team mainly wants fast testing on manuals, policies, or SOPs and can accept ChatGPT-based access, limited branding, and lighter control over the user experience. Choose the API if you need live chat on your site, CRM or help-desk integration, actions triggered by user behavior, or document retrieval across larger knowledge bases. BQE Software reports 86% AI resolution in its support deployment, the kind of result usually tied to an embedded assistant, not a ChatGPT link. Also note that OpenAI is pushing newer app builds toward its Responses API and Agents tools, so roadmap matters. If you need a custom assistant trained on business documents for real use, platforms like Intercom Fin or CustomGPT.ai may be easier because they add document management, website deployment, and admin controls without full custom development.
How should enterprise teams compare Google, Amazon, OpenAI, and Cohere for chatbot projects?
Enterprise teams should compare Google, Amazon, OpenAI, and Cohere by the business use case for a generative AI chatbot, not by model brand alone. For a custom assistant trained on internal documents, score each option on cloud fit, retrieval quality, security, and deployment speed.
Google is often strongest for Google Cloud shops and search-heavy experiences; Amazon fits AWS-centered environments and contact-center workflows; OpenAI is commonly chosen for broad model capability and developer tooling; Cohere is often shortlisted for enterprise language control and private deployment options. Test whether the platform can ingest manuals, SOPs, and diagnostic codes with citations, enforce document-level access controls and retention, and reduce migration friction for teams starting from ChatGPT or Azure OpenAI pilots. At MIT, an AI assistant was required to support 90+ languages with zero hallucinations, which shows why grounding, permissions, and multilingual accuracy matter before price.
When does it make sense to evaluate Cohere alongside OpenAI, Google, and Amazon?
Evaluate Cohere when you are selecting a production AI assistant, especially one that must answer from company documents, meet security reviews, or run in a controlled deployment. It belongs beside OpenAI, Google, and Amazon once grounding, admin controls, and data handling affect the decision.
Add Cohere to the shortlist when you are issuing an RFP, moving from pilot to rollout, or comparing retrieval quality across vendors such as OpenAI and Google. It is less urgent if you are still experimenting with generic chat use cases. In practice, many teams add Cohere when procurement starts, because private deployment, data residency, and permission controls begin to matter more than raw demo quality. At GEMA, the AI assistant handles 248,000+ inquiries with an 88% success rate, which is the kind of grounded, high-volume use case that makes vendor differences visible. CustomGPT.ai is another option if your evaluation centers on document-based AI search and support.
What is a common mistake businesses make when choosing a generative AI chatbot?
The most common mistake is choosing a generative AI chatbot by model popularity instead of deployment needs. Many teams start with ChatGPT-style tools, then discover they cannot easily embed the bot on their site, connect several document sources, or track conversations for support follow-up.
Before picking a platform, answer five questions first: Do you need a website widget, ingestion from manuals or internal PDFs, role-based access, analytics, and saved conversation history? Also check whether the bot can hand off to a human and retain transcripts, because support chats may need to be reviewed later as business records. For instance, a business may assume a popular OpenAI option will fit customer support, then learn that Custom GPTs stay inside ChatGPT and the Assistants API needs custom development. That is why buyers should compare deployment and maintenance requirements against tools like Intercom Fin or Chatbase before comparing model brands. BQE Software reports 86% AI resolution, which shows workflow fit matters more than model fame.