Use case
Email drafting
Drafting replies in the inbox from the thread, the CRM record and the company style. Small per message, large in aggregate — and the place where a wrong statement leaves the building fastest.
- Source
- Editorial ontology entry — no fetched document behind this page
- Evidence
- none on this page — it links to the pages that hold it
- Category
- Communications
- Typical risk
- medium
- Entry
- editorial · reviewed 25 Aug 2026
01What this is
Email assistance ranges from a reply suggestion inside the mail client to a shared-inbox workflow that drafts responses for an agent to approve. The useful version reads the thread and the relevant record, not just the last message.
A good deployment always leaves a human send step for external mail, keeps drafts inside the existing mail tenancy, respects signature and disclaimer rules, and never fabricates a commitment about price, date or liability. Tone follows a stated style guide rather than the model's defaults.
Pitfalls: auto-send configurations that escape review; drafts that quote internal context to an external recipient; and a mail plugin whose vendor now processes the whole mailbox. Mailboxes are among the most sensitive corpora a company holds — scope the assistant's access before deploying it.
- Typical data
- email, crm records, customer data, internal documents
- Solution classes it admits
- Enterprise SaaS, Private cloud, Self-hosted
02Deployment options
- ASSESSMENT
On a neutral reading of this use case, Self-hosted is a strong alternative, Private cloud is a strong alternative and Enterprise SaaS is conditional.
- ASSESSMENT
Read as a generic reading of this page, not a recommendation: no organisation, size, jurisdiction, budget or technical capability has been supplied, so wherever an option depends on one of those, it says "unknown". Ask your own question to get a verdict that accounts for them.
Private cloud
STRONG ALTERNATIVEManaged model API or private model deployment in your cloud account
- ASSESSMENT
Two patterns fit inside one account in a region you name: call a managed foundation-model API such as Amazon Bedrock, Microsoft Foundry / Azure OpenAI, or Vertex AI; or deploy an open-weight model on GPU compute you control. Your application, retrieval layer, storage, identity and logs remain in your cloud boundary in both patterns.
- ASSESSMENT
The managed-API pattern can use closed-source frontier models without buying or operating GPUs. It is usually the fastest way to build a custom workflow, but prompts and retrieved context are processed by the managed service, so model availability, retention, abuse monitoring and regional routing must be checked for the exact feature and endpoint.
- ASSESSMENT
The private-model pattern gives more control over weights, serving and network paths, and can use managed endpoints or your own containers. It also makes your team responsible for capacity, patches, model upgrades, evaluation and failover.
- ASSESSMENT
The cloud provider becomes a data processor in either pattern: you need a DPA, a documented region, and an answer on cross-region routing and where support staff can access the environment from.
- RECOMMENDATION
Start with the managed-API pattern when the workflow is custom but model operations are not the source of competitive advantage; move to private model serving only if evaluation, volume, portability or the data boundary justifies the extra operations. Your stated technical capability is "unknown".
Self-hosted
STRONG ALTERNATIVEOpen-weight models on infrastructure you operate
- ASSESSMENT
Documents, queries and embeddings stay on machines you own, using an open-weight model whose licence you review. For a brief that involves confidential documents and personal data, that removes a model-API vendor from the data path rather than governing that transfer by contract.
- ASSESSMENT
It costs you the operational work instead: a GPU server, Docker, Linux, backups and a patching routine. Your stated technical capability is "unknown", which is the attribute this option most depends on.
- ASSESSMENT
No processor agreement, subprocessor list or cross-border transfer assessment is needed for the model itself, because no third party processes the content.
- RECOMMENDATION
Recommended where local processing is preferred (you did not say so) and the content is sensitive (confidential documents and personal data).
Enterprise SaaS
CONSIDER IFFinished closed-source cloud product with enterprise controls
- ASSESSMENT
This is a complete vendor application, not a model API: examples include an enterprise assistant, coding copilot or document product with the workflow, interface, connectors and administration already built. It can use closed-source cloud models while requiring no model hosting or application engineering from your team.
- RECOMMENDATION
Choose it when the product already performs the actual workflow and its controls meet your requirements. Do not choose it only because its underlying model is strong: a finished SaaS product is less flexible than building against a managed API when your process, integrations or review steps are organisation-specific.
- ASSESSMENT
Vendor commitments are treated as unverified until we have fetched the page that makes them. Until then this option carries questions to ask, not assurances: a signed data processing agreement covering the data you will actually put in; a documented data residency commitment naming the region, in the contract rather than a blog post; a written no-training commitment for your content, including uploads and connected sources; stated retention periods and a deletion path you can exercise; an administrative audit log you can export, and SSO with group-based access control.
- RECOMMENDATION
No jurisdiction was named, so this is the check rather than the conclusion: compare the vendor's stated processing locations and subprocessor list against the cross-border transfer rules wherever you operate before uploading anything.
03Deployment stacks
- Enterprise SaaSEnterprise SaaS assistant with governance controlsA business or enterprise plan from an established vendor — ChatGPT Enterprise, Microsoft 365 Copilot, Glean or an equivalent — connected to your identity provider, scoped by existing permissions, covered by a DPA, and rolled out behind a written policy.
- Self-hostedPrivate ChatGPT on your own serverOllama serving an open-weight model on one GPU, Open WebUI in front of it for accounts, chats and admin controls, both in Docker on a single machine. Deliberately the smallest thing that works: no vector database, no connectors, no cluster.
04Tools by hosting option
Self-hosted1
Private cloud2
- Self-hostedLibreChatSelf-hosted multi-model chat application with authentication, per-conversation model switching, plugins, file upload and an admin configuration file. Familiar interface for staff moving off consumer tools.
- SaaSMistral Le ChatAssistant and API platform from a European vendor, offered as a hosted service and, for enterprise customers, as a deployment inside the customer’s own infrastructure.
Vendor cloud7
- SaaSChatGPT EnterpriseOpenAI’s administered ChatGPT tier with SSO, workspace controls, retention settings, connectors to company systems and business terms that differ from the consumer product.
- SaaSClaudeAnthropic’s assistant, available as a team and enterprise product with SSO, audit logs, project workspaces and commercial terms, and as an API for building applications.
- SaaSGemini for Google WorkspaceGemini features inside Gmail, Docs, Sheets and Meet, grounded in Workspace content under the existing Workspace agreement and admin controls.
- SaaSIntercom FinAI support agent that answers customer questions from a company’s help content, takes defined actions and hands conversations to human agents inside Intercom’s inbox.
- SaaSJasperMarketing content platform with brand voice controls, campaign workflows and channel-specific templates, aimed at teams producing copy at volume.
- SaaSMicrosoft 365 CopilotAssistant embedded in Word, Excel, Outlook and Teams, grounded in the tenant’s own content through Microsoft Graph and governed by existing Microsoft 365 permissions.
- SaaSMistral Le ChatAssistant and API platform from a European vendor, offered as a hosted service and, for enterprise customers, as a deployment inside the customer’s own infrastructure.
On-premise (enterprise plan)2
- Self-hostedLibreChatSelf-hosted multi-model chat application with authentication, per-conversation model switching, plugins, file upload and an admin configuration file. Familiar interface for staff moving off consumer tools.
- SaaSMistral Le ChatAssistant and API platform from a European vendor, offered as a hosted service and, for enterprise customers, as a deployment inside the customer’s own infrastructure.
05Compliance hot spots
This use case usually raises personal data, confidentiality, prompt leakage, model training, retention, human oversight, cross-border transfers, data processing agreement, logging, security, transparency, vendor jurisdiction.
- Prompt leakage
- No published jurisdiction page names this topic yet
- Model training
- European UnionSouth Korea
- Retention
- Hong Kong
- Human oversight
- No published jurisdiction page names this topic yet
- Cross-border transfers
- European UnionUnited KingdomChina (mainland)Hong KongTaiwan
- Data processing agreement
- Japan
- Logging
- European Union
- Security
- China (mainland)
- Transparency
- United KingdomJapanSouth Korea
- Vendor jurisdiction
- China (mainland)
06Example questions
Each of these opens the question box with the text already in it. The answer is researched for your organisation, not for this page.
07Related use cases
- CommunicationsVoice agentA system that answers or places phone calls and holds a spoken conversation — booking, triage, reminders, first-line support. The hardest stack in this ontology: telephony, speech recognition, a model, speech synthesis and interruption handling, all under a latency budget.
- CommunicationsTranslationTranslating documents, messages and interfaces between languages while keeping terminology and formatting intact. In East Asian and European operations this is often the highest-volume AI workload in the company.
- CommunicationsMeeting transcriptionTurning internal meetings into a transcript, a summary and a list of actions. Usually a bot that joins the call, or an app that records the room. Value comes from the follow-up, not the transcript.
- CommunicationsCRM call summariesWriting the call back into the CRM: a summary, the next step, the fields a rep would otherwise type. The point is not the transcript but that the record exists at all and is consistent across the team.
- CommunicationsCall transcriptionTranscribing telephone calls — support lines, sales calls, advice sessions — usually from a contact-centre or telephony platform rather than a meeting app. Narrow-band audio and regulated recording rules make it a distinct problem.
- CommunicationsAI customer-support agentAn assistant that answers customer questions from your help content and account systems, resolves what it can, and hands the rest to a person with context attached. Deflection rate matters less than what happens on the cases it cannot close.
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