Dust

Name: Dust
Brand: Dust
Rating: 4.9 (23 reviews)

AI Native

Transform how work gets done. Just use Dust.

Est 2023|Valuation $98.8M|Raised $61.6M|42 Ref Customers

LinkedIn 42K|Web traffic 455K|FTEs 143|HQ Paris, Île-de-France, FR

MarketsAI Assistant Agent Builder Platforms

Published Oct 15, 2026

01Common complaints

- If you need a mature, enterprise-search-first solution with deep, proven relevance tuning and large-scale references - Consider Glean or other enterprise search incumbents if your primary problem is search relevance and compliance at very large scale. - If your primary use case is IT service automation/ticket deflection with established ITSM integration playbooks - Consider Moveworks (or ServiceNow-native options) when ITSM automation is the center of gravity. - If your internal data is too messy or ungoverned to safely connect to an AI agent platform - Independent commentary emphasizes that assistants inherit the underlying content quality problems; the result can be lower trust and poor adoption. ([technologysolutions.com](https://technologysolutions.com/dust-tt-review/?utm_source=openai)) - If you cannot tolerate occasional performance slowdowns - At least one review flags intermittent “lenteurs” (slowdowns). ([capterra.com](https://www.capterra.com/p/10019510/Dust/?utm_source=openai)) - If you require ISO 27001 certification (or other specific standards) as a hard gate - Dust claims SOC 2 Type II and GDPR, but ISO 27001 is not clearly evidenced in the sources captured; validate before proceeding. ([dust.tt](https://dust.tt/home/security?utm_source=openai)) - Known issues and product limitations (from reviews) - Natural language scheduling not reliable; desire for a simpler scheduling mechanism. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Occasional performance slowdowns. ([capterra.com](https://www.capterra.com/p/10019510/Dust/?utm_source=openai)) - Roadmap / ongoing updates - Dust maintains a changelog in its docs, suggesting frequent iteration; buyers should review recent entries during due diligence. ([docs.dust.tt](https://docs.dust.tt/changelog/changelog-update-25?utm_source=openai)) - Open-source repository vs hosted product - Dust maintains a public GitHub repo (dust-tt/dust). However, having open code does not necessarily mean the SaaS is designed for easy self-hosting with full parity; buyers should clarify support, deployment models, and what is actually open vs proprietary. ([github.com](https://github.com/dust-tt/dust?utm_source=openai)) - Security/compliance gaps in public materials - ISO certifications, data residency specifics, subprocessor disclosures, and pen test summaries were not clearly identified in the sources captured; enterprises should request these artifacts.

02Why they buy

- Validated (or partially validated) by user feedback - Multiple agents + shareable configurations and strong integrations are explicitly praised in the Gartner Peer Insights “likes” snippet. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Ease of use / manageable learning curve is praised by the (single) Gartner reviewer. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Vendor-claimed differentiation (needs buyer validation) - “Multiplayer AI” concept: a shared workspace for people and agents collaborating, rather than isolated chat. ([dust.tt](https://dust.tt/?utm_source=openai)) - “Enterprise-grade security” including SOC 2 Type II and GDPR compliance claims. ([dust.tt](https://dust.tt/home/security?utm_source=openai)) - Areas where competitors clearly have advantages (balanced view) - Larger incumbents may offer more proven deployments, broader peer review footprint, and longer operational track record. - Mature enterprise automation/search suites can bundle adjacent capabilities (ITSM, ECM, analytics) that Dust may not match out of the box.

03Field reports

- Review volume caveat - Public review counts appear low on major platforms in accessible pages (e.g., 1 review on Gartner Peer Insights and 1 on Capterra). This limits statistical confidence and can overrepresent early adopters or incentivized reviewers. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Gartner Peer Insights (quote examples) - Positive (Apr 6, 2026; Operations Manager; Banking): “It is very simple to use, especially for non-technical teams, the learning curve is not that steep.” ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Mixed/negative signal (product “dislike” snippet): “The schedule with natural language sometimes does not work, it would be nice to have a simple schedule.” ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Capterra (quote examples) - Positive (May 14, 2025): “Très facile d'utilisation, très pertinent et très rapide dans sa globalité” (very easy to use, relevant, and fast overall). ([capterra.com](https://www.capterra.com/p/10019510/Dust/?utm_source=openai)) - Negative (same review, Cons): “Quelque fois il y a des lenteurs qui empêchent le logiciel d'etre efficace” (sometimes there are slowdowns that prevent the software from being effective). ([capterra.com](https://www.capterra.com/p/10019510/Dust/?utm_source=openai)) - Note: Capterra flags this as an “Incentivized review,” which can introduce bias despite moderation. ([capterra.com](https://www.capterra.com/p/10019510/Dust/?utm_source=openai)) - Reddit (community discussion signals) - /r/MarketingAutomation thread asks for user experiences; the mere presence suggests curiosity but not necessarily adoption proof. ([reddit.com](https://www.reddit.com/r/MarketingAutomation/comments/1osz7hz/has_anyone_here_used_dusttt_for_building_custom/?utm_source=openai)) - /r/Rag thread includes a positive sentiment: “the agent orchestration is pretty solid” (from a prospective/evaluating stance). ([reddit.com](https://www.reddit.com/r/Rag/comments/1osikdq/using_dusttt_for_advanced_rag_agent_pipelines/?utm_source=openai)) - Limitation: the accessible snippets don’t provide enough detailed, verified negative experiences to balance; this is a common issue for newer products with limited public footprint. - What customers say they like (themes across sources) - Ability to create many agents and share configurations. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Integrations described as “very complete” (Gartner snippet). ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Common criticisms and complaints (themes across sources) - Reliability/UX issues with scheduling via natural language. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Performance “slowdowns” at times (Capterra). ([capterra.com](https://www.capterra.com/p/10019510/Dust/?utm_source=openai)) - Data quality dependency: assistants can be undermined by messy, contradictory internal documentation (independent review). ([technologysolutions.com](https://technologysolutions.com/dust-tt-review/?utm_source=openai)) - Why customers chose this product over alternatives (inferred from positioning and comments) - Dust is positioned as a platform to build custom agents connected to internal data, not only a single general assistant. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Why some customers left for competitors (churn reasons) - No credible, specific churn narratives surfaced in accessible public reviews; any strong claims of “zero churn” should be treated cautiously without third-party confirmation. ([rywalker.com](https://rywalker.com/research/dust?utm_source=openai)) - Vendor/press examples - TechCrunch describes internal use cases like HR assistants answering corporate policy questions connected to internal knowledge bases (e.g., Notion) as examples of intended value. This is illustrative, not a quantified case study. ([techcrunch.com](https://techcrunch.com/2024/06/27/dust-grabs-another-16-million-for-its-enterprise-ai-assistants-connected-to-internal-data/?utm_source=openai)) - PYMNTS notes a customer example (Qonto) claiming Dust enabled teams to test and deploy AI while keeping customer data safe; it cites a “release,” which likely originates from vendor PR—useful but not independently audited. ([pymnts.com](https://www.pymnts.com/news/investment-tracker/2024/dust-raises-16-million-to-help-companies-deploy-ai-assistants/?utm_source=openai)) - Technical case-study style writeups (semi-independent) - ZenML’s LLMOps database describes architectural approaches like creating a “synthetic filesystem” abstraction to navigate disparate enterprise sources. This is useful for technical due diligence, but it’s not a customer ROI proof. ([zenml.io](https://www.zenml.io/llmops-database/building-synthetic-filesystems-for-ai-agent-navigation-across-enterprise-data-sources?utm_source=openai)) - Critical evaluation of metrics - Some third-party research-style pages cite high adoption rates in deployments (e.g., “88% daily active user rates”), but without transparent methodology these should be treated as anecdotal/marketing-adjacent until validated. ([zenml.io](https://www.zenml.io/llmops-database/building-a-horizontal-enterprise-agent-platform-with-infrastructure-first-approach?utm_source=openai))

04Best fit

- Company sizes: specific employee counts and revenue ranges - Pro: companies under 100 employees (per Dust docs). ([docs.dust.tt](https://docs.dust.tt/docs/subscriptions?utm_source=openai)) - Enterprise: 100+ employees (sales-led). ([docs.dust.tt](https://docs.dust.tt/docs/subscriptions?utm_source=openai)) - Practical sweet spot (inferred) - Mid-market to enterprise teams that have enough internal documentation and repeatable workflows to justify governance and setup effort. - Industries and verticals where adoption is strongest (with customer examples) - Banking appears in the single Gartner review (Operations Manager, Banking). ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Dust publishes industry-specific pages (e.g., industrial manufacturing), suggesting targeted GTM, but these are vendor pages rather than proof of adoption. ([dust.tt](https://dust.tt/home/industry/industrial-manufacturing?utm_source=openai)) - Roles and personas: decision makers AND end users - Decision makers - CIO/CTO, Head of IT, Head of Data/AI, Security/Risk, Ops leaders - End users - HR, Operations, Support, Sales enablement, Engineering/product teams - Pain points and challenges the product addresses - Reducing time spent searching across systems (Slack/Notion/Drive/GitHub) - Standardizing how internal knowledge is used (consistent policy answers, reusable agent behaviors) - Enabling semi-technical users to build and share task-specific agents ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Qualification criteria: what makes a GREAT fit - The company has well-maintained knowledge sources, clear permissions, and an owner for governance. - There are repeatable workflows where an agent can provide consistent value (policy Q&A, drafting, internal support). - Deal disqualifiers: what makes a POOR fit - Extremely messy, contradictory documentation with no plan to clean it up (risk of “garbage in, garbage out”). ([technologysolutions.com](https://technologysolutions.com/dust-tt-review/?utm_source=openai)) - Organizations requiring public proof of large-scale adoption and dozens/hundreds of peer reviews before purchase.

05The pitch

- What the product does (core value proposition in objective terms) - Dust is a B2B “agent platform” for creating, deploying, and managing custom AI agents connected to internal company data (e.g., Google Drive, Notion, Slack, GitHub) and callable via a web workspace, Slack, browser extension, and API. ([dust.tt](https://dust.tt/?utm_source=openai)) - It positions itself as a shared (“multiplayer”) workspace where teams build and reuse agents (not just individual copilots). ([dust.tt](https://dust.tt/?utm_source=openai)) - Target market and market positioning - Dust targets teams that want to operationalize internal AI assistants/agents with governance, integrations, and shareable configurations—often described as “enterprise AI assistants connected to internal data.” ([techcrunch.com](https://techcrunch.com/2024/06/27/dust-grabs-another-16-million-for-its-enterprise-ai-assistants-connected-to-internal-data/?utm_source=openai)) - The docs indicate a dividing line where “Pro” is aimed at companies under 100 employees, while larger companies are routed to Enterprise sales. ([docs.dust.tt](https://docs.dust.tt/docs/subscriptions?utm_source=openai)) - Company background (founding year, HQ, key leadership) - Founded: 2023. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - HQ: Paris, France. ([gartner.com](https://www.gartner.com/reviews/product/dust-363291154?utm_source=openai)) - Leadership: Third-party profiles and press commonly associate the company with Stanislas Polu and/or CEO/co-founder Gabriel Hubert; however, leadership details are not consistently presented across sources and should be verified directly with Dust during procurement. ([techcrunch.com](https://techcrunch.com/2024/06/27/dust-grabs-another-16-million-for-its-enterprise-ai-assistants-connected-to-internal-data/?utm_source=openai)) - Funding and growth: investment rounds, acquisitions, estimated customer count/revenue if public - TechCrunch reported a $16M Series A led by Sequoia Capital (June 27, 2024). ([techcrunch.com](https://techcrunch.com/2024/06/27/dust-grabs-another-16-million-for-its-enterprise-ai-assistants-connected-to-internal-data/?utm_source=openai)) - A GlobeNewswire press release (May 18, 2026) claims Dust raised $40M and has raised “over $60M” total. As a press release, it should be treated as vendor/PR-distributed information rather than independent validation. ([globenewswire.com](https://www.globenewswire.com/news-release/2026/05/18/3296726/0/en/dust-raises-40m-to-make-ai-multiplayer-inside-the-enterprise.html?utm_source=openai)) - Some third-party analyst-style writeups claim strong retention and “zero churn,” but these figures are not clearly independently audited in the publicly accessible sources; treat them as directional only. ([rywalker.com](https://rywalker.com/research/dust?utm_source=openai)) - Pricing model with specific tiers and costs where available - Pro plan - Dust documentation states Pro is per-seat, billed monthly upfront, and includes “regular usage” via web/Slack/browser extension; API/programmatic usage is billed separately. ([docs.dust.tt](https://docs.dust.tt/docs/subscriptions?utm_source=openai)) - Dust documentation also states Pro is for companies with fewer than 100 employees, and 100+ should contact sales. ([docs.dust.tt](https://docs.dust.tt/docs/subscriptions?utm_source=openai)) - Exact per-seat price is not clearly published in the Dust docs page excerpted; some third-party directories estimate ~US$29/user/month, which should be validated with Dust because it is not a primary source. ([automationatlas.io](https://automationatlas.io/tools/dust/?utm_source=openai)) - Enterprise plan - Appears to be custom-quoted (consistent with many enterprise agent/search vendors). Dust docs route 100+ employees to sales. ([docs.dust.tt](https://docs.dust.tt/docs/subscriptions?utm_source=openai)) - API / programmatic usage - Dust publishes an “API Pricing” page and docs describe credits, rate limits when credits are low, and an Enterprise-only pay-as-you-go option with a spending cap and warning emails. ([dust.tt](https://dust.tt/home/api-pricing?utm_source=openai)) - Total cost of ownership considerations (implementation, training, hidden costs) - Integration & data hygiene overhead - User-facing outcomes depend heavily on the quality and organization of internal knowledge bases (Slack/Notion/Drive). Independent commentary notes that “assistant inherits the mess” if docs are outdated or contradictory—suggesting real TCO includes knowledge cleanup and governance. ([technologysolutions.com](https://technologysolutions.com/dust-tt-review/?utm_source=openai)) - Ongoing usage costs (token-based) - API/programmatic usage can create variable spend; Dust includes caps/warnings for pay-as-you-go to avoid runaway charges, implicitly acknowledging the risk of “runaway workflows” or misconfigured automations. ([docs.dust.tt](https://docs.dust.tt/docs/programmatic-usage?utm_source=openai)) - Admin and enablement - Dust launched an “Academy” to address onboarding gaps—suggesting training/enablement is non-trivial for non-trivial deployments. ([dust.tt](https://dust.tt/blog/dust-academy?utm_source=openai)) - Procurement and security reviews - Enterprise deployments typically require security documentation review (SOC 2 report sharing, DPA, etc.). Dust highlights SOC 2 Type II and GDPR compliance, but buyers should still expect a vendor risk process and time/effort. ([dust.tt](https://dust.tt/home/security?utm_source=openai)) - Key competitors in the space (list 4-6 direct alternatives) - Glean (enterprise search + assistant) - Moveworks (enterprise automation/IT workflows + AI) - Microsoft Copilot / M365 Copilot (for Microsoft-centric orgs) - Google Gemini for Workspace (for Google-centric orgs) - ServiceNow AI capabilities (for ServiceNow-heavy orgs) - Atlassian Rovo (for Atlassian-centric orgs)

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