Eight team workspaces compared on what a client asks: training terms, where data is processed, who has access and what a team can evidence
Oct 13, 2026 · 15 min read
A team answers a client's AI questionnaire best when it can describe one controlled setup and back each answer with a document or a screenshot. An approved business workspace, a dated list of the AI tools and model providers in use, and a saved evidence pack do that better than personal subscriptions. A single-vendor business plan is easier to describe when everyone's work fits one provider, and below about five people one or two seats often cost less. The answer is no when the client's contract bans AI processing, because the right control there is exclusion and not a better workspace. The eight workspaces compared on the same criteria below are Playgram, WorkLLM, nexos.ai, Langdock, TeamAI, Aymo, Magai and TypingMind.
Questionnaires go wrong when answers are assembled from privacy pages, sales emails and old spreadsheets. SIG and CAIQ, two standard vendor-assessment frameworks, already cover access control, subprocessors and cloud controls, and AI questionnaires add model, training and inference questions6. Public templates such as the ACLU's generative AI vendor questionnaire41 and NIST's AI procurement workbook40 show the same shape.
This guide lists what clients ask, prices the single-vendor stack for five people and compares eight workspaces on training terms, hosting, access and usage controls. It also shows a workflow for drafting and checking answers and a pilot that tests the evidence behind them. A public security page rarely proves a team's own settings, so every answer still needs screenshots or exports from the workspace the team actually uses.
Questionnaires arrive during sales, onboarding and renewal, and security and legal staff approve the wording. Leadership signs the final declaration, so each answer needs evidence behind it.
Consultants and agencies process client documents, engineers use AI with source code, and support and operations teams handle client records. Each group may reach for a different provider.
Work passes between personal accounts, company accounts, API tools and browser extensions. Nobody can say which account touched which client's material.
AI use is limited to public information, no client material enters a model and one approved business account covers every workflow. If a client contract bans AI processing, exclusion is the control.
Four layers explain why a client questionnaire turns into an investigation when AI use is spread across accounts.
Separate subscriptions charge for access, so a team can pay for ChatGPT Business, Claude Team, Google Workspace with Gemini and Grok Business for a person who uses each one now and then. ChatGPT Business does not automatically refund an unused seat during a committed billing period7, and Claude Team needs at least five members2. The larger cost is repeated review, since every extra AI vendor brings another contract, subprocessor list, retention policy, residency answer, incident process and renewal.
Answers are often put together by hand from privacy pages, sales emails and old spreadsheets. One employee answers for ChatGPT while another used Claude. A no-training answer may describe a business plan when the work happened in a personal account, or a connector may have sent data somewhere the security page does not mention. A model change may never reach the answer bank, and a client who asks for evidence receives marketing language, so each questionnaire becomes an investigation.
Earlier answers, security decisions and approved wording sit in one person's files or in old questionnaires, and documents have to be attached again in each separate tool. A chat history keeps a transcript but does not give another employee a reviewed answer to reuse. The team has to tell apart the vendor's published claim, its own configuration, the approved answer, the evidence behind it, the owner and review date, and any exception for one client.
Personal subscriptions give the organization weak evidence. It may not know who has an account, which plan each person uses or which models are enabled. It may also not know whether prompts are logged, which files were uploaded, which connectors are authorized or whether a former employee still has access. Central workspaces improve this, but admin analytics, hard budgets, role controls, retention settings and audit logs cannot be assumed from the phrase team workspace.
Five groups covering what a setup needs so each questionnaire answer is repeatable, scoped and backed by evidence.
The team can approve named model providers and avoid personal accounts nobody has listed. It needs an inventory showing which companies receive prompts, and data-flow notes naming the workspace, the model host, the region and the connected services.
Check each product for web research, Word and PDF work, spreadsheet work on questionnaire workbooks, image and video generation, code-file analysis for secure development answers and no-trace chats. Credit a tool only when the vendor's own documentation confirms it.
Approved policies, architecture notes and earlier answers stay attached to the project, so a second person starts from them. Client projects stay separate from internal and other-client work, and each answer keeps an owner and a review date.
Admins see usage by person and model and limit who reaches sensitive client projects, connectors and models. Retention and deletion controls say how long prompts, files and logs stay. Screenshots, audit records and contracts can be attached to an answer.
Occasional users should not need four full seats, and pricing may be per seat, per workspace, per credit or through the team's own API keys. Compare seat cost, included model use and overages separately. New staff get the approved setup and leavers lose access, while team-owned evidence stays.
The multi-model workspaces a team is most likely to weigh up when a client asks how its data is handled in AI tools.
This table compares multi-model team workspaces with each other on what a client questionnaire asks. Pricing is the lowest-priced paid plan that covers five users, at the monthly rate. Each cell cites the page that documents that cell. Figures checked October 13 2026, and cells marked 'Manual test required' could not be confirmed from public documentation. Magai and TypingMind were not verified in this pass.
The same products again, on the facts a questionnaire asks for: tools, integrations, visibility, limits, training terms and processing location.
'Not publicly documented' means the official sources checked did not state it, and 'Manual test required' means the behaviour cannot be confirmed without trying it. Neither means the feature is absent, so read them as questions to put to the vendor. Checked October 13 2026.
The published per-seat price of each major single-vendor team plan, billed monthly, which a team pays for each person who needs all four.
Example only: ChatGPT Business, Claude Team, Gemini Enterprise Business and Grok Business at July 2026 list prices come to about $101 a person a month, or roughly $505 for five people. A cheaper mix is easy to assemble and the plans do not buy the same amount of use, so run the estimator below with your own numbers. Figures checked July 2026.
These drivers change the real cost of answering clients more than any single plan's list price does.
Six setups, led by the one this guide is about, with what each lets a team say and prove to a client.
One administrative boundary can cover several model providers, so the team describes one workspace and lists the providers behind it.
Best for: Teams where several departments need different models under one admin boundary.
Strengths
Trade-offs
Each person opens their own accounts, so settings, history and terms stay personal.
Best for: A very small team working only with non-sensitive material.
Strengths
Trade-offs
The team standardizes on one business plan from OpenAI, Anthropic, Google or xAI, which gives one primary provider and one admin environment to describe.
Best for: Teams whose work fits one model family and its native tools.
Strengths
Trade-offs
The team buys business plans from several vendors and keeps the contract terms and controls each one supplies.
Best for: Teams that need the native features and contract terms of several vendors.
Strengths
Trade-offs
Engineers build their own interface, logging and routing on top of provider APIs.
Best for: Teams with engineers and client-specific limits on models or retention.
Strengths
Trade-offs
The same workspace plus saved context, so approved answers, decisions and client constraints stay available for the next questionnaire.
Best for: Teams with recurring questionnaires and long client relationships.
Strengths
Trade-offs
The team opens a restricted client project, and a questionnaire moves through mapping, drafting, checking and saving inside it.
A reviewer checks that each statement describes the exact product, plan, region and integration in use before drafting starts. The checking model also compares the draft with the admin settings and earlier answers, and anything that disagrees goes back to the reviewer. The questionnaire and its evidence index are saved as team-owned outputs, so a teammate can later open the project and see sources, open questions and approved wording without a new briefing.
Products in this category mean different things by the word memory, and for a questionnaire the useful kind is reviewed knowledge that keeps its source.
A context window is how much text a model reads in one request, and it empties when the chat ends. Memory is context stored outside the chat and pulled back into later ones. A chat history is only a transcript and does not apply itself to a new chat.
Some keep chat history and shared threads only. Some rely on a knowledge base built by hand from files and instructions. Some learn automatically but keep it private to one person. Some save it at a level the whole team can reach, and Aymo adds a Private Chat that is removed when the tab closes.
A client-specific exception should stay scoped to that client. The boundary that matters most here is between client projects, so one client's files and decisions cannot be retrieved in another client's work.
An answer should keep its source and its approval date, and a user should be able to correct or delete an outdated fact. Most public pages do not say whether deleting a source also removes memory derived from it, so that needs a pilot.
Five steps start from an audit of what is in use today and end with the questions to put to each vendor before anything is signed.
Record every AI product and browser extension, the plan owner, current and former users, the models enabled and whether each account is personal or business. Add API keys, connected apps and their permission scopes, the data categories entered, client restrictions, retention, region and training settings, and each contract, data-processing agreement and renewal date.
Use six real tasks. Complete a spreadsheet questionnaire, answer whether data is used for training, list providers and subprocessors, explain hosting and processing locations, say who can reach prompts and files, and update an old answer after a provider policy change. Then build an evidence pack of 14 items. It holds the approved AI-tool register, plan and product names, the model-provider list, a data-flow diagram and the agreement and commercial terms. It also holds current subprocessor lists, training and retention terms, hosting and processing regions, an admin-role export, enabled integrations and their scopes, and retention-setting screenshots. The last three are a usage or audit-log sample, a deletion-test record and the owner with the next review date.
Use synthetic or approved data first. Test whether the model cites the correct source, whether one client project can retrieve another client's files, what an administrator can see, whether restricted models are really blocked and whether spend limits stop requests. Then test whether temporary chats disappear as described and what remains after a user is removed. Delete a source document and see what happens, switch models and check the context holds, and confirm an integration respects the permissions of the system it reads.
Track time to a useful first answer, manual edits, repeated context or uploads, and unsupported claims caught in review. Also track subscription overlap, active use by approved people and model fit by questionnaire stage. Finish with new-user onboarding time, time to assemble the evidence pack and the number of answers with no owner or review date.
Ask which legal entity is the vendor and which model providers receive prompts. Ask whether training is excluded in writing for every enabled provider, where prompts are stored, where processing happens, whether global models can be disabled and which vendor staff can read customer content. Ask what roles exist, whether usage shows by person, model and period, whether limits apply before spend and how long chats, files, logs and backups are kept. Ask whether one client project can be isolated, whether deleting a source also removes derived memory and whether temporary sessions exist. Ask which integrations and permission scopes apply and whether MCP is supported and can be limited per server. Finally ask for a current agreement and dated subprocessor list, the SOC, ISO or penetration-test documents available, support for a client deletion or incident inquiry and notice of model, subprocessor or region changes.
A team answers client AI questionnaires reliably when it controls the environment before the questionnaire arrives: an approved business workspace, a precise list of providers and integrations, reviewed answers and an evidence folder. A multi-model workspace can reduce account sprawl and make model use easier to govern, especially when several departments need different models under one admin boundary. It does not remove the need to review downstream model providers, integrations and regional settings.
Pricing and plan allowances change, and plans with similar names can carry very different controls. A no-training promise does not answer retention, human access, residency or subprocessors, and hosting the application in the EU does not prove that every selected model runs there. Shared history and uploaded files are not automatic shared memory, and a marketing page cannot prove the team's own configuration. The only reliable test uses the team's real questionnaires, contracts and client data boundaries.
What the team is choosing between is answers rebuilt for each client and answers drawn from one documented setup. The setup around the models decides that as much as the model list, since settings, roles and saved evidence either exist before the question arrives or get reconstructed afterwards. Test one real questionnaire from start to finish, and time how long the evidence takes to assemble.
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