Who offers AI solutions for HR, sales, and support teams?
Davis Christenhuis
Dust offers AI solutions for HR, sales, and support teams through a single platform that connects to your existing tools and data sources. The platform lets teams build custom AI agents through a no-code interface, deploy them across departments, and maintain enterprise-grade security while respecting existing access permissions.
📌 TL;DR
Short on time? Here's what this article covers:
- Who offers reliable AI for business teams: Dust provides a platform where HR, sales, and support teams build custom AI agents on company data.
- What makes AI reliable for enterprises: Security certifications like SOC2 and GDPR compliance, granular permission controls, maintained integrations with existing tools, and guarantees that your data never trains AI models.
- How teams build and deploy agents with Dust: Write plain language instructions, select which data sources the agent can access, choose your AI model, and publish.
- Dust agent builder: A no-code interface where team members create agents by describing what they should do and selecting which tools and data sources they connect to.
What makes an AI solution reliable for business teams?
Reliable AI for business teams means the platform delivers consistent results, protects sensitive data, and works within existing workflows without requiring constant technical intervention.
When evaluating reliability, focus on these core criteria:
- Data security and compliance: Look for platforms with third-party security audits, encryption standards, and compliance certifications relevant to your industry. If you handle health data or financial information, verify the platform supports those specific regulatory requirements.
- Adoption by non-technical teams: AI solutions should be usable by the people who understand the workflows best, not just engineering teams. Evaluate whether domain experts in HR, sales, or support can build and modify solutions independently.
- Integration with tools you already use: Check whether the platform connects to your actual tech stack through maintained integrations rather than requiring custom API development. Updates to connected tools should not break your AI workflows.
- Granular access controls: Platforms should let you restrict which documents, databases, and systems AI can read from based on department, role, or sensitivity level without complex configuration.
- Speed to deployment and measurable ROI: Consider how long it takes to go from purchase to production use. Platforms that require months of setup and training can deliver less value than those that ship working solutions in weeks.
Dust: Operating system for AI agents
Dust is a platform where teams build and deploy AI agents that work on top of their company data, connecting to 50+ integrations including Slack, Notion, Google Drive, Salesforce, and Zendesk. Agents pull context from the systems your team already uses instead of requiring manual data entry or switching between apps.
Sales
Sales teams juggle prospect research, CRM updates, call preparation, and follow-up across different tools. Reps need context from previous conversations in Gong, account details in Salesforce, and product information from internal documentation.
Sales teams use Dust to build agents that:
- Research prospects automatically: Gather company information, recent news, funding rounds, and key decision-makers from web searches and internal databases before outreach.
- Draft personalized outreach: Write initial emails or LinkedIn messages that reference specific prospect pain points and align with your sales playbook.
- Update CRM records: Extract key takeaways from call transcripts and write structured summaries directly into Salesforce opportunity fields.
- Prepare for sales calls: Compile relevant account history, previous interactions, and product positioning based on prospect industry and use case.
Example: Brevo's Revenue Operations team automated prospect research and email personalization, achieving an 80–90% reduction in per-prospect email personalization time without adding engineering tickets. Read the full story →
Support
Support teams field repetitive questions, route tickets based on complexity, and maintain knowledge bases that quickly become outdated. Agents need instant access to product documentation, past ticket resolutions, and escalation procedures.
Support teams use Dust to build agents that:
- Answer common questions: Respond to product questions, account issues, and troubleshooting requests by searching internal documentation and past ticket resolutions.
- Route tickets intelligently: Identify ticket complexity, required expertise, and priority level to classify and tag tickets, triggering routing.
- Draft response templates: Generate initial responses to frequent support requests that agents can review and personalize before sending.
- Update knowledge bases: Help identify documentation gaps, draft article updates based on recurring questions, and maintain consistency across help center content.
Example: Electra's customer care team handles complex support tickets 80% faster by using agents that surface relevant documentation and real-time backend data instantly. Read the full story →
HR
HR teams handle recurring questions about policies, benefits, and onboarding processes that pull from multiple sources. Employees ask about vacation policies stored in Notion, reference information in Google Drive handbooks, and need answers that account for regional differences.
HR teams use Dust to build agents that:
- Answer policy questions: Pull from employee handbooks, benefits documentation, and internal wikis to provide accurate responses about leave policies, expense reimbursement, or health insurance.
- Guide onboarding workflows: Walk new hires through setup checklists, account provisioning, and first-week logistics by accessing onboarding documentation and company resources.
- Draft performance review content: Help managers structure feedback by referencing internal performance frameworks and past review examples.
- Surface relevant documentation: Find the right HR forms, regional policy variations, or compliance resources based on employee location and role.
Example: Lucas, a People Analyst at Alan, introduced Dust to his HR team to streamline employee support and policy questions. Read the full story →
Dust agent builder
The Dust agent builder lets team members create functional agents through a no-code interface. Building an agent follows a structured workflow. You write plain language instructions that define what the agent should do and the boundaries of its role.
Next, you select which data sources the agent can access. Dust provides integrations with authentication flows, so connecting to Slack and Notion works like adding any other workplace app. You choose which AI model powers the agent from options including Claude, GPT, Gemini, and Mistral.
The screenshot shows the Dust agent builder on the left, where a sales agent called @ProspectResearcher has been configured with plain language instructions to research companies, identify key decision-makers, and assess whether a prospect fits the ideal customer profile. It is connected to Notion, web search, and Salesforce to cross-check whether a prospect is already in the pipeline.
On the right, the same agent has analyzed Audi, pulling a full financial performance table, EV delivery numbers, and recent company milestones directly from web sources.
💡 Want to build agents for your team? Try Dust free for 14 days →
Frequently asked questions (FAQs)
Can one AI platform reliably serve HR, sales, and support teams?
Yes, when the platform connects to the tools each department already uses and respects existing permission structures. The challenge most companies face is that point solutions designed for one team create data silos and duplicated vendor management. A reliable cross-team platform needs to handle different data sources (employee handbooks for HR, CRM records for sales, knowledge bases for support) while ensuring agents only access information relevant to their function.
How do sales teams use AI without compromising customer data security?
Sales teams need AI that accesses CRM data, call transcripts, and prospect information without creating compliance risks. The platform should provide visibility into how AI agents interact with your data, including logging capabilities that help compliance teams understand usage patterns. It should also ensure that third-party AI model providers do not retain your data after processing requests or use it for model training. Verify that the platform has zero data retention agreements with its model providers for standard API calls, and understand how the platform itself stores and indexes your connected data to power knowledge retrieval. Sales leaders should be able to restrict which agents can access specific accounts, deal stages, or contact records. This keeps AI productive for research and personalization while maintaining the data governance standards enterprises require.
What's the difference between AI assistants and AI agents for business teams?
AI assistants respond to requests and retrieve information when asked. AI agents plan workflows, decide which tools to use, and execute multi-step tasks. When you ask an assistant about a customer account, it searches your CRM and displays the results. When you ask an agent the same question, it can search the CRM, check recent support tickets, pull relevant contract details, identify wether the renewal is approaching, and draft a summary email without additional prompting. Assistants require you to direct each step of the process. Agents reason through the steps needed to complete a goal based on the context and tools available. For business teams, this means agents reduce the manual work that happens after getting an answer, turning information retrieval into task completion.