Enterprise AI platform: what it is and what your company needs from one

An enterprise AI platform is the software layer that connects AI models to your company’s data, your tools, your security rules and an approval step. This guide sets out what Microsoft, Google, AWS, OpenAI, Anthropic and Cohere say their platforms include. It then covers what a manufacturer or distributor of 20 to 200 people needs from one. That includes where your data is stored and where the model processes it.

Derik Lawlis Founder, ThriveAI

What an enterprise AI platform is

Enterprise AI is AI put to work on a company’s own records, inside the company’s own access rules. C3.ai defines the platform as “an integrated set of technologies that enables organizations to design, develop, deploy, and operate enterprise AI applications at scale.” For a company that buys one, it helps to think in layers around the model:

• Models. A model is the program that reads a request and writes an answer, such as OpenAI’s GPT or Anthropic’s Claude. Running a model on a request is called inference.
• Connections to your data. A connector lets the model read one system, such as a shared mailbox or an ERP (the system that runs your orders, stock and invoicing). MCP (Model Context Protocol) is “an open-source standard for connecting AI applications to external systems.” Its site lists Claude and ChatGPT among the assistants that support it.
• Actions in your tools. An agent is an AI tool that takes steps on its own, such as drafting a purchase order or updating a record in the ERP.
• Identity and security. This layer controls who can sign in and which records the AI may open for each person.
• Approvals and logs. A named person approves an action before it runs, and a log records what the AI read and what it drafted.

A chat window on its own has a model and little else. The other layers let AI work on a quote or a supplier email with the records behind it.

Enterprise AI software, sorted by who uses it

Products sold as enterprise AI software fall into two groups. Workplace apps are used by your staff, in a browser or inside Microsoft 365. Builder platforms are used by developers, who assemble their own AI applications from models and parts.

Workplace apps your staff use

• ChatGPT Enterprise, from OpenAI. According to OpenAI’s product page, admins “can manage who can use agents, connected tools, and actions across the workspace.”
• Claude Enterprise, from Anthropic. On Anthropic’s Enterprise page, “admins approve which connectors are available org-wide and set per-tool permissions.”
• Gemini Enterprise app, from Google. Google calls it “an advanced agentic platform that brings the best of Google AI to every employee, for every workflow.” Staff can build agents without writing code in a tool called Workflow Builder.
• Microsoft 365 Copilot with Copilot Studio. Microsoft says Copilot Studio “is a platform for building and managing agents.” The same page announces GPT-5 and Anthropic models in Copilot Studio.
• Cohere North. Cohere, founded in 2019 in Toronto, says you can run North “in your own VPC, on-prem environment, or through Cohere’s secure Model Vault inference platform.” A VPC (virtual private cloud) is a private network inside a cloud provider, and on-prem means on servers at your own site.

Builder platforms your developers use

• Amazon Bedrock. AWS calls Bedrock “The platform for building generative AI applications and agents at production scale.” AWS also says Bedrock does not use your data to train models. Whether it keeps a copy of a request depends on the retention setting, covered below.
• Gemini Enterprise Agent Platform. This is Google’s developer platform, which Google announced on April 22, 2026 as “the evolution of Vertex AI.” Its product page offers “200+ Google and third-party AI models and tools.”
• Microsoft Foundry. Microsoft’s documentation lists Azure AI Studio and Azure AI Foundry as its previous names. Its product page describes “a unified platform to build, ground, and govern AI apps and agents” with access to over 11,000 models.
• OpenAI Frontier. OpenAI presents it on its own page, apart from ChatGPT Enterprise, as “The platform powering enterprise AI.” Its Enterprise Frontier Program “pairs forward deployed engineers from The OpenAI Deployment Company with your team.” These are engineers who work with your staff to design the system and run its agents in daily use.

A builder platform supplies the models and the parts. Your team or a contractor still sets up each connection and the approval step, and writes the connector when your ERP has no ready-made one. How to build an AI agent walks through that work, and Claude API pricing shows what model use costs when you build on one.

What each enterprise AI platform includes

The table repeats what each vendor’s own pages say, checked on September 27, 2026. Rows are in alphabetical order, and the table does not rank or score anyone. “Not stated” means the pages we checked do not say. Seat prices for the workplace apps are compared in ChatGPT alternatives for business in Canada.

What a smaller manufacturer or distributor needs from one

An AI platform for business at a company of 20 to 200 people has a different job from the one the vendor pages describe. In Statistics Canada’s survey for the second quarter of 2026, 19.2% of businesses reported using AI to produce goods or deliver services. Wholesale trade had one of the lowest rates, at 7.9%. AI for manufacturing covers the jobs a platform would do at each desk in a plant.

It reads the systems your plant runs on

The connector lists on vendor pages mostly name office and online software. Google’s list, for example, is “Google Drive, Microsoft OneDrive, SharePoint, HubSpot, Jira, and more.” A plant keeps its knowledge in ERP exports, a shared order mailbox, a drawings folder and spreadsheets on a file server. Ask how each of those gets connected, and who writes the connector when no ready-made one exists. Legacy ERP automation compares the routes into an older ERP that has no API.

Every answer names its source

An answer about a lead time or a price helps only if someone can check it. Ask the vendor to show the record behind each answer, such as the order line or the drawing revision. When the records cannot answer a question, the system should say so.

A named person approves every action

An approval step means the AI drafts and a person decides. Each vendor documents it differently, and Microsoft and Google mark theirs as preview or experimental:

• Microsoft says multistage approvals “are available exclusively in agent flows,” and “All of these capabilities are in preview.”
• Google’s Agent Development Kit has a confirmation step for “decision making, verification, security, or general oversight,” marked Experimental.
• AWS lets a Bedrock agent ask the user to CONFIRM or DENY an action, as a safeguard against “malicious prompt injections.” Prompt injection is text hidden in a document or an email that changes what the AI does.

Microsoft’s page also says preview features “aren’t meant for production use.” Before anything goes live, name the person who approves each purchase order and each ERP change. Human in the loop explains how an approval step works day to day, and AI governance covers who decides which tools are allowed.

Someone builds it and keeps it running

In the same Statistics Canada survey, 12.0% of manufacturing businesses named a lack of skilled workers as a barrier to AI. A builder platform needs a developer to connect it. A workplace app needs an administrator to set connectors and permissions. OpenAI’s Enterprise Frontier Program pairs its customers with forward deployed engineers, according to its Frontier page. At a company with one IT person, decide who owns the platform before you buy it.

Where your data sits and where the model runs

In the same Statistics Canada survey, 22.3% of businesses with 20 to 99 employees named cybersecurity or privacy concerns as a barrier to AI. Two separate questions sit behind that concern. Storage, or data at rest, is where chats and files are kept. Processing, or inference, is where the model reads a request and writes the answer.

The vendor pages, checked on September 27, 2026, answer them differently:

• OpenAI. ChatGPT data residency lets new Enterprise customers store data at rest in Canada, at no additional cost. In-region processing is offered only for Europe (EEA and Switzerland), the United States and the United Arab Emirates.
• Google. The Gemini Enterprise locations page lists Canada for stored data in the Standard and Plus editions, once Google’s account team grants access. Processing is set model by model. Google lists in-country processing in Canada for Gemini 3.5 Flash and Gemini 2.5 Pro, and neither in-country storage nor processing for Gemini 3.1 Pro. Gemini 3.6, 3.7 and 3.8 Flash are listed only for the global region, so an app set up in Canada must route those requests to Google’s global endpoint.
• Microsoft. For a Power Platform environment in Canada (the workspace where Copilot Studio agents are built), Microsoft’s table shows Azure OpenAI, the Microsoft service that runs OpenAI’s models, hosted in the United States. When data movement is allowed, “your inputs (prompts) and outputs (results) might move outside of your region.”
• AWS. On Amazon Bedrock, Claude Opus 5.5 has no in-region option in either Canadian Region. Its US routing option “Keeps data within US and Canada regions.”
• Anthropic. Its API, the connection your own software uses to call Claude, accepts only “global” or “us” as the inference location, and only “us” for the data it stores.

Retention is a third question. Zero data retention (ZDR) means the provider keeps no copy of a request or its answer. Anthropic says its Claude Teams and Claude Enterprise interfaces “are not ZDR-eligible.” On its API, a feature outside the arrangement is not blocked, so the request goes through under that feature’s own retention. Amazon Bedrock works the other way: with retention set to none, it blocks a model that requires retention and returns an error.

Two privacy rules apply when personal information leaves the province or the country. Quebec’s Commission d’accès à l’information says an organization must carry out a privacy impact assessment before it communicates personal information outside Quebec. The rule has applied since September 2023. The federal Privacy Commissioner says PIPEDA, the federal privacy law for businesses, does not prohibit processing abroad. The sender “is accountable for the information in the hands of the organization to which it has been transferred.”

Sovereign AI covers data sovereignty and whose law can reach your data, and private AI for business compares the three ways to deploy. Local LLM covers running a model on your own hardware. What staff should keep out of a general chat tool is in secure AI at work. This page is not legal advice.

Questions to ask any enterprise AI vendor

Ask each vendor these in writing, and keep the answers with the contract.

1. Where are our records stored, and in which data centre?
2. Where does the model process a request, for each model we will use?
3. What is the retention setting, and which models does it cover?
4. If a request falls outside the zero data retention arrangement, does it fail or go through?
5. Which of our systems does it connect to, and with whose login?
6. Who approves an action before it runs, and where is that approval logged?
7. Who builds the connections, and who maintains them after launch?

Secure AI at work turns the answers into a setup checklist, and AI security covers the risks behind each question.

How ThriveAI helps

ThriveAI is an AI engineering company in Ottawa that works with manufacturers and distributors in Ontario and Quebec. ThriveAI builds private AI systems on your own data. The platform is designed to keep your data on its own server in Canada that only your company uses. You choose the AI model that reads it: one that runs on that server, or a hosted model under a written zero data retention agreement. A hosted model may process requests outside Canada, so the contract names the exact model it covers.

Every connection only reads data, and nothing touches a live system until you approve what it will read. Nothing is sent or saved in your systems until the person responsible approves it, and every answer shows its source. Derik Lawlis leads every project and stays close to the build.

Derik at Thrive was instrumental in taking incredibly messy data we inherited in a business we acquired and, through using AI, organized it in record time in a way that made it reviewable by our team for final review and approval.

Rios-Karim Mercier, Belmont Capital.

Working sessions run on site with your team, in French or English. ThriveAI also runs hands-on AI training on your own documents. For the company and how a project runs, see About ThriveAI.