AnAIworkspacebuiltonyourorganization'sknowledgeandautomatedworkflows.
Documents, dashboards, processes, decisions, and AI agents in one place. Every Building Block has an owner. Building Blocks connect into workflows your team runs and your organization compounds.
An agent that does the work, not a chatbot that answers questions.
Atlas is built around an agentic AI loop. Not a chat that retrieves and summarizes — an agent that reads, writes, modifies, and executes work alongside your team.
Humans are always in the loop. Every action that touches a record, a customer, or a payment passes through an explicit approval step. The agent does the work. Your team approves what ships. Both are first-class — neither is bolted on.
The agent does the work.
Reads files, writes outputs, modifies Building Blocks, runs scheduled jobs.
Your team approves.
Approval gates configured per workflow. Conservative defaults; loosen as trust builds.
Every action is logged.
Read, write, and approval events captured with user identity and timestamp.
Work creates structure. Structure creates workflows.
Everything your team builds in Atlas becomes a structured Building Block — a document, a dashboard, a piece of code, a decision, an AI agent. Each has an owner. Building Blocks can be shared, nested, and connected into the workflows your team already runs — and the ones it hasn't built yet because nothing else made them easy.
Connected.
A Building Block's output feeds the next as input.
Pattern: data → analysis → report → dashboard
Nested.
A parent Building Block contains children. Multiple owners contribute pieces; the parent composes them.
Pattern: a case file containing evidence, arguments, and a draft brief
The AI knows what you're talking about.
Most AI assistants start every conversation from zero. Atlas doesn't. Atlas indexes your organization's documents, dashboards, processes, decisions, and prior work — and respects who's allowed to see what. When someone asks a question, the answer is grounded in your team's actual knowledge: this quarter's analysis, last month's decision, the policy your CFO approved.
New hires inherit institutional memory. Context built in one team's work reaches the rest of the organization through explicit publishing. Knowledge stops belonging to whoever happened to be in the meeting.
Q1 Revenue Analysis
Finance team completes quarterly analysis with regional breakdowns and trend forecasts.
Board Approves Expansion
Based on Q1 analysis, board approves APAC expansion with $2M budget allocation.
APAC Hiring Policy
HR publishes hiring guidelines for new Singapore office, approved by CFO.
"What's our APAC hiring budget?"
Six teams. Six workflows. One platform.
Building Blocks compose into workflows the way your teams already work — with handoffs, approvals, and dependencies. Each example below is a real workflow shape.
Finance — Accounts Payable
AI extracts structured data from incoming invoices. An employee reviews and approves. Approved invoices flow into the AP ledger. The Scheduled Payment Building Block triggers payment on the due date. Each step has its own owner. Human-in-the-loop approval is a first-class step, not bolted on.
Legal — Case File
The Case is the parent. Multiple people contribute children inside it. Paralegal owns evidence. Associate owns argument. Attorney composes them into the brief. Each child has its own owner; the parent unifies them as the work product.
Marketing — Campaign Performance
Campaign telemetry feeds analyses. Analyses become weekly insight reports. Reports compose into the CMO's dashboard. The same campaign data feeds attribution and forecasting Building Blocks in parallel.
Multi-Location Operations
Each location feeds its own data Building Block. Regional managers compose performance analyses. Executive leadership pulls regional reports into one view.
People Operations / HR
Survey responses, performance data, and tenure feed engagement analyses. Analyses become quarterly people insights reports. Leadership dashboard composes across teams and functions.
Analytics / Consulting
Each engagement is a parent containing the work for that client. Analyses reference the firm's methodology Building Blocks, which compound across engagements. Methodology becomes a separate connected layer that any engagement can pull from — compounding IP across the practice.
Once the AI understands your data, you can automate the work that uses it.
Most automation tools force you to define every step before you get any value. Atlas works the other way. Your team builds Building Blocks as part of doing the work. Once the structure exists, automation is configuration, not engineering.
The agent runs the connected workflow. When data arrives, dependent Building Blocks refresh. Approvals route to the right owner. Scheduled jobs fire on cadence. Dashboards update without anyone refreshing them.
We host. Or you host.
Atlas runs in our cloud or yours. Same product, same support.
Most teams don't want to think about where their AI workspace runs — they want it to work. So we host Atlas for you on the Azurite cloud, with everything configured and monitored. For teams that have to know where every byte lives — regulated industries, government, defense, sovereign cloud requirements — Atlas runs entirely on your cloud, under your control.
Azurite-hosted (default)
- Up and running in minutes — no infrastructure work
- Continuous platform updates and monitoring
- For most teams that want AI without ops overhead
Self-hosted on your cloud
- Deploy entirely on your own AWS, Azure, or GCP
- Data never leaves your environment
- For teams that have to control where every byte lives
Same Atlas. Same support. Two ways to deploy.
We're inviting 5 founding design partners to shape Azurite v1.
Five seats. Six months. Direct line to the team building it.
We're recruiting five teams to use Azurite as part of their daily work for six months — and shape what we build with us.