The agent-native system for complex business operations

A productivity boom for operations teams. Atlas turns your scattered systems into one operational picture, and your team's know-how into processes that run themselves — pulling your operators in the moment judgment matters.

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Queue
DRSOLW
P1Payroll parallel run blockedneeds you
Meridian HealthCASE-2041
P1Go-live at riskpaused
Brightside FoodsCASE-2037
P2Data migration stalledrunning
Northwind LabsCASE-2029
P3Kickoff schedulingsent
Fieldstone LogisticsCASE-2016
One Atlas case per row — sources stay systems of record
Meridian Health — payroll parallel run blockedNeeds judgmentCASE-2041
Implementation stage moved to “Payroll parallel run”09:12
MER-142 “Employee import: 43 duplicate records” reopened09:41
“Still waiting on their payroll provider export” — #meridian-impl10:02
Atlas raised this case — parallel run blocked on source data10:05
origin event → graph match → trigger rule → task
A consolidated record from
SalesforceHubSpotJiraSlackMicrosoft TeamsNotionGuruOutlineAsanaAttioZendeskIntercomConfluenceLinearMonday.comAirtableGmailGoogle DriveFrontServiceNowYour internal tools

Every datapoint, assembled from every system

Atlas works where your team does. It continuously ingests from across your stack — Salesforce, HubSpot, Jira, Slack, internal tools — into a single entity graph: cases, people, companies, and every activity between them, reconciled and kept fresh.

PN
Priya Nair
One entity in the graph — three source identities
Reconciled
HubSpotcontact · priya.nair@meridianhealth.com
Jirareporter · pnair · MER-142
Slackmember · @priya · #meridian-impl
Same person everywhere. Your numbers stop disagreeing.
Origin
10:02
Origin event — Slack message in #meridian-impl
10:02
Matched in the entity graph — Meridian Health, implementation case
10:05
Trigger rule fired
stage = "Payroll parallel run" AND blocker > 3d
10:05
Task created in your queue
Rule: Implementation watchdog · open in the trigger library
The full timeline, per case
One execution timeline. No tab-hopping.
One queue, only signal
Atlas watches your entire footprint and raises tasks into a single queue.
Provenance on everything
You can always trace a task back to the facts.

Your team's playbook, made executable

Atlas is designed around your operations team. It learns how your best operators actually work, turns that into procedures both you and agents can read, and executes in context across your existing stack.

Employee data migrationTrained on 86 runsv3
Steps
01
Pull the implementation case and its full execution timeline
get_case
02
Verify employee import counts against the source extract
query_crm_data
03
IFduplicate records > 0
Pause and ask the implementation lead how duplicates should be merged
Asks an operator
04
Draft the parallel-run status update for the customer in Slack
New · from simulation
05
Log the milestone back to HubSpot
engagement.create { type: NOTE, body: milestone_summary }
Handoffs land as cases in the owner's queue and briefing — with the run attached. Edit by hand, or tell Atlas what to change.
Compiled from how you really work
Atlas builds draft procedures from your team's real executions and your own runbooks.
Readable as a doc, precise as code
Every procedure is plain steps your team can review and edit by hand — or just by telling Atlas what to change.
In-context execution
Atlas acts inside the systems you already run — creating tasks, checking state, drafting the next step — with the full case history behind every decision.

Ask anything. Get answers grounded in evidence.

Because Atlas holds the complete, reconciled picture, its answers aren't a guess from one system — they're computed across all of them, using the same techniques as a data scientist.

Which implementations slipped past go-live last quarter, and why?↵
Analyzing CRM data · query_crm_data
6 implementations slipped in Q2. 4 trace to payroll data issues open at go-live; the rest split across customer resourcing and integration scope.
Payroll data issues
4
Customer resourcing
1
Integration scope
1
Real SQL over governed viewscase_reporting_factscase_stage_interval_facts
Complete view
Reporting spans every source, not just the one with the cleanest export. Pipeline, tickets, and conversations in the same analysis.
Reconciled entities
The same customer in HubSpot, Jira, and Intercom is one customer in Atlas. Your numbers stop disagreeing with each other.
The rigor of your best analyst
Atlas answers with the same tools a trusted data scientist would — real queries over governed views, never invented figures.
Reports that watch themselves
Pin a question as a live report and arm it: when a new case matches the pattern, it lands in your queue automatically.

Automation that earns the right to run

No brittle workflow builders. No black-box agent making calls you can't see. Every Atlas procedure is simulated against your own history, and can only go live — with guardrails — when you feel comfortable.

Simulations — Employee data migrationReplayed end-to-end on prior cases. Nothing reaches a customer.3 of 4 passing
Brightside FoodsFail
Meridian HealthPass
Northwind LabsPass
Fieldstone LogisticsPass
✓Pulled the case and execution timeline0.8s
✓Verified import counts against the source extract1.2s
✕Draft the status update — step 4 had no recipient: the case had no implementation lead assigned
Atlas can fix this
Add a step that pauses and asks the project owner to confirm the implementation lead — the way the operator handled it.
Set live
Gated until every simulation passes — judgment calls still route to your queue
Simulate before you trust
Every procedure dry-runs against real prior cases first. You see exactly what it would have done in real scenarios.
Pull operators in when they know best
The moment a procedure reaches a judgment call, it pauses and raises a case in your queue with the full trail.
As dynamic as your operations
Easy updates from new runbooks, or when your team's execution drifts over time.

In your workflow, not another tab

For teams that need to stay where they are, Atlas plugs into the systems your team already runs.

ClaudeAtlas connected
What changed on the Meridian implementation since Friday?
Two things. The payroll parallel run is blocked on the customer's provider export — flagged Friday in #meridian-impl — and ticket MER-142 reopened with 43 duplicate employee records. Atlas raised a case; it's in Sam's queue.
Grounded inAtlas MCP
The same intelligence, as an API
Search your cases, people, and companies
Pull the complete, reconciled record for any of them
Pipe queue tasks into your own workflows
Governed, read-only, and tenant-isolated — built for teams that build.
Connected to your stack
Atlas ingests from and acts across common ops tooling, through to your internal builds.
Build on it
The same governed API that powers Atlas powers your integrations: pipe queue tasks into your existing workflows, embed grounded answers into your own tools.
Plug and play
Atlas works in Claude, ChatGPT, and every other MCP-supporting destination you already use.

Built for teams that can't afford to be wrong

Tenant-isolated by design, enforced at the database layer
Every answer grounded and traceable to source
Agents act only within reviewed procedures — everything else comes to a human

See Atlas running on your kind of casework in a 30-minute walkthrough.

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