Managed AI operations.
→Managed AI operations
for real business workflows.
Big Robot builds operating layers and managed agents for companies that need AI connected to trusted data, human approval, and the systems they already run.
CONNECTS YOUR SYSTEMS →
Sage IntacctProcoreMicrosoft 365SharePointMiterQuickBooksSage 300TimberScan TitaniumAPPROVED AI WORK SURFACES →
Claude CoworkOpenAI CodexMicrosoft CopilotCursorApproved agentsPrivate skillsPrivate modelsHow managed AI operations works
Start with the workflow. Build the control layer.
The work starts with one operating problem: which systems hold the facts, who approves action, what can be automated, and what must stay under human review.
Pick the workflow
Start where repeated data entry, exception chasing, or source-of-truth conflicts already cost the team real time.
Map source authority
Identify which system owns each fact, where records disagree, and what evidence must be preserved.
Build the operating layer
Normalize the data, expose review surfaces, and route exceptions before AI is allowed to answer or act.
Operate managed agents
When the workflow can be safely managed through approved access, agents run the procedure with monitoring, updates, and support.
Measure the result
Track cycle time, errors, readiness, and exception visibility instead of counting AI usage.
Workflow first · Human approval · Measured outcomes
Enterprise AI
Enterprise AI starts
with an operating layer.
When systems disagree, AI needs more than another connector. Big Robot builds the trusted layer first: normalized records, source rules, approval paths, and controlled access.
01 · Disconnected systems
Find the real answer.
The same invoice, payment, project, customer, or vendor can appear in several tools. The first job is knowing which fact to trust.
02 · Operating layer
Make the workflow explicit.
Records, rules, approval points, evidence, and exception states become visible enough for people and AI tools to use safely.
03 · Approved AI access
Let AI work from the trusted layer.
Approved agents and AI work surfaces can answer, draft, route, or act only inside the data and permission boundaries you define.
Proof and offers
Two ways to make AI operational.
Enterprise AI needs an operating layer first. Managed Agents can run narrower workflows when the access, procedure, and risk controls are clear enough to manage.
For workflows with legacy SaaS, historical records, source-of-truth conflicts, or sensitive approvals.
Turn disconnected systems into an operating layer AI can trust.
Source-of-truth normalization across existing systems
Workflow automation with human review gates
Evidence and audit trails tied to source records
Approved AI access only after the layer is trustworthy
Trust and security
Controls before
autonomous action.
Private business data changes the AI conversation. Big Robot designs the access, approval, and audit boundary before agents touch real workflows.
Least-privilege access
People and agents get scoped access to approved systems and APIs, not raw credentials or broad private data dumps.
Human approval gates
Money movement, compliance, customer impact, and system mutation require review where judgment or risk matters.
Audit-ready workflow state
Important answers and actions trace back to records, source evidence, user intent, and approval history.
No silent autonomy
Big Robot says no to uncontrolled data access, prompt-only automation, fake proof, and unreviewed actions in sensitive workflows.
Contact
Bring one workflow
worth fixing.
The best first conversation is concrete: the workflow, the systems involved, who approves exceptions, and what result would matter.