DUST AGENCY: BUILD AI ASSISTANTS THAT ACTUALLY WORK
Hack'celeration is a Dust agency that helps you create custom AI assistants connected to your data and tools. We build AI agents that integrate with your stack (Slack, Notion, Google Drive, your APIs) and actually do useful stuff.
We configure Dust workspaces, create specialized assistants with custom instructions and tools, connect your data sources (documents, databases, APIs), and automate workflows with AI agents. We handle everything from workspace setup to advanced integrations with your existing systems.
We work with tech startups building AI-first products, scale-ups automating internal ops, and SMEs wanting to deploy AI without hiring a whole ML team. We also support agencies needing AI capabilities for their clients.
Our approach: we prototype fast, iterate based on real usage, and give you assistants you'll actually use every day. No AI theater, just practical tools that save time and scale your team.
Let's build your growth engine.
Why partner
with a Dust agency?
Working with a Dust agency can transform scattered AI experiments into production-ready assistants that your team actually uses every day. Dust is powerful but requires understanding how to architect AI agents, connect data sources properly, write effective instructions, and integrate everything into your workflows. Most teams start experimenting, create a few assistants, then hit a wall when trying to scale or connect to their real data. Production-ready AI assistants → We build assistants with custom instructions, tools, and data connections that handle real use cases. We configure retrieval over your documents, connect APIs, and set up workflows that actually work. Complete data integration → We connect Dust to all your knowledge bases (Notion, Google Drive, Confluence, your databases) and configure semantic search with proper chunking and retrieval strategies. Your assistants access the right information at the right time. Workflow automation → We create AI agents that trigger actions, call APIs, update your tools (Slack, HubSpot, Airtable), and automate repetitive tasks. Not just chatbots, but agents that do stuff. Multi-assistant architecture → We design systems with specialized assistants for different teams and use cases, with proper prompt engineering, tool selection, and context management. Each assistant knows its job. Security and governance → We configure workspace permissions, data access controls, and usage monitoring. We make sure your data stays secure and you control who can do what. Whether you're starting from scratch with Dust or have already created a few assistants, we help you build a complete AI system that scales with your team and actually delivers ROI.
Our methodology
for Dust Agency.
STEP 1: AI USE CASE AUDIT
We start by understanding what you want to automate and where AI can actually help. We audit your current processes, identify repetitive tasks that AI can handle, and map your data sources (documents, databases, APIs). We also check what you’ve already tried with AI and what worked or didn’t. We list concrete use cases ranked by impact and feasibility. We identify which data sources to connect and what integrations you need. We define 2-3 priority assistants to build first, with clear success metrics. At the end of this step, you have a roadmap of AI assistants to build and a clear plan for your Dust workspace.
STEP 2: WORKSPACE AND DATA SETUP
We configure your Dust workspace and connect all your data sources. We set up your workspace structure, configure team permissions, and connect your knowledge bases (Notion, Google Drive, Confluence, Slack channels). We also configure API connections to your internal tools and databases. We test retrieval quality with different chunking strategies and embedding configurations. We make sure your assistants will access the right information with proper context. We set up data sync schedules and access controls. We configure managed data sources for sensitive information. At the end of this step, your Dust workspace is ready with all data sources connected and properly indexed for semantic search.
STEP 3: ASSISTANT DEVELOPMENT
We create your custom AI assistants with specialized instructions and tools. We write detailed system prompts with clear instructions, examples, and guardrails. We configure which data sources each assistant can access and what tools it can use (API calls, web search, code execution). We develop custom tools and API integrations for specific actions (updating your CRM, creating tickets, sending notifications). We test assistant responses with real queries and edge cases. We iterate on prompts and tool configurations based on actual outputs. We add few-shot examples and refine instructions until responses are consistently good. At the end of this step, you have functional assistants that understand their role and can access your data and tools.
STEP 4: WORKFLOW INTEGRATION
We integrate your assistants into your daily workflows and tools. We connect Dust to Slack so your team can interact with assistants directly in channels. We set up API workflows that trigger assistants automatically (new ticket → assistant analyzes and routes it). We create custom integrations with your tools (HubSpot, Airtable, your internal systems) using Dust’s API and webhook capabilities. We configure conversation histories and context management. We test end-to-end workflows with real scenarios. We make sure assistants handle errors gracefully and escalate to humans when needed. At the end of this step, your assistants are embedded in your workflows and your team can use them naturally throughout their day.
STEP 5: TRAINING AND OPTIMIZATION
We train your team and optimize assistants based on real usage. We run training sessions showing how to use each assistant, when to use which one, and how to get the best results. We share best practices for prompting and interacting with AI agents. We monitor usage analytics, track common queries, and identify patterns. We analyze where assistants perform well and where they struggle. We iterate on prompts, add new examples, refine data source configurations, and improve tool integrations based on feedback. We add new assistants for emerging use cases. At the end of this step, your team is autonomous with Dust and your assistants keep improving based on real-world usage data.
