Managed AI Operations
You don't buy an agent. You hand us an outcome.
We take responsibility for a defined workflow, redesign it around AI, run it in your systems with your people approving exceptions, and report against the business metric we agreed.
The category
Managed AI Operations
Managed AI Operations means handing HonestAI a defined business workflow and its outcome. We redesign the process, deploy the AI and integrations, supervise exceptions, measure performance, and continuously improve the operation.
You don't buy an agent. You hand us an outcome.
- We run the workflow.
- We measure the outcome.
- AI handles repetitive execution.
- Humans handle judgment.
- Production, not experiments.
- Operations, not demos.
Traditional automation
- Tool delivered
- Customer operates it
- Value depends on adoption
- Project ends at deployment
HonestAI Managed AI Operations
- Workflow owned
- HonestAI operates it
- Value measured against an outcome
- Optimization continues
Operating model
Operate is where the relationship begins.
Most AI projects end at deployment. Ours start there: once a workflow is live, we run it and improve it against the outcome we agreed.
- 01Project
Discover
Identify high-friction work and baseline the economics.
- 02Project
Redesign
Split the workflow between AI execution, policy controls, and human judgment.
- 03Project
Deploy
Connect secure production agents to existing enterprise systems.
- 04Recurring
Operate
Run the workflow, handle exceptions, monitor SLAs and govern execution.
- 05Recurring
Improve
Optimize the workflow continuously against agreed business metrics.
Workflow economics
Every workflow has a cost. We run it for less.
We baseline the workflow before we touch it, then report the same measures every month.
Accounts payable — invoice processing
Before
- People involved
- 9 FTE
- Hours / month
- 1,440
- Cycle time
- 6.5 days
- Error / rework
- 8%
- Annual operating cost
- $1.08M
With HonestAI · estimate
- AI execution share
- 84%
- Human review share
- 16%
- Cycle time
- 1.2 days
- Capacity returned
- 1,150 hrs / month
- Exception / error rate
- Baseline agreed in discovery
- Cost per completed invoice
- $2.10 vs $9.20
- Annual economic opportunity
- $690K
Illustrative values for a typical mid-size AP function. Not a customer result.
Model My WorkflowHuman accountability
AI executes. Humans decide. Every action is accountable.
- 01
Agent executes
- 02
Policy & permission checks
- 03
Confidence / exception rules
- 04
Human specialist review
- 05
Outcome recorded
- 06
Audit trail retained
- 07
Fed back into improvement
Human judgment
People decide anything that needs judgment, authority or accountability.
Approval controls
Thresholds and maker-checker rules you set — enforced on every item.
Traceability
Inputs, decision summary, action and approver logged for every task.
Escalation handling
Clear routing decides who sees which exception, and how fast.
SLA ownership
We own the workflow's service levels and report against them monthly.
Secure integration
Least-privilege access through your identity provider and systems.
Managed operations portfolio
Every workflow has an owner and a metric.
| Workflow | Status | Outcome metric | Monthly volume |
|---|---|---|---|
| Invoice processing | Operating | Cost per invoice | 9,800 |
| Bank reconciliation | Optimizing | Days to reconcile | 1,150 |
| Lead qualification | Operating | Qualified accounts / week | 2,400 |
| Case triage | Operating | First-response SLA | 6,300 |
Engagement model
Start with one workflow. Prove the economics. Then expand.
Pricing is scoped to the workflow and the outcome. No fixed public price list.
- Step 1
AI Opportunity Map
Find the business workflows where AI can create measurable economic value.
- Workflow and system inventory
- Baseline economics per workflow
- Ranked opportunity map
- Step 2
Production Workflow
Redesign and put one high-value workflow into production.
- Redesigned workflow and controls
- Production agents in your systems
- Agreed outcome metric and baseline
- Step 3Flagship · recurring
Managed AI Operations
Hand HonestAI the workflow. We operate it, govern exceptions, and improve performance.
- Daily operation and SLA ownership
- Exception handling with your approvers
- Monthly outcome and economics review
- Continuous improvement
- Step 4
AI Operations Portfolio
Expand the model across a function or multiple departments.
- Multiple workflows under one operating model
- Shared governance and reporting
- Portfolio-level economics
Implementation Services
Need it built first? We implement too.
Agent-Ready Enterprise Integration
Connect your business systems to compatible AI assistants through governed connectors so customers, employees and partners can take authorized action.
Enterprise AI Workflow Implementation
Redesign a multi-step process so agents carry it end to end across your existing tools, with human checkpoints where judgment is needed.
Forward Deployed AI Engineering
Engineering execution for teams that need hands-on build capacity for a specific AI initiative.
Managed AI Operations
We operate the workflow after go-live — monitoring, exceptions, improvement and monthly reporting against the agreed outcome.
Direct answers
Questions buyers ask.
What work should AI take off your team?
Start with one workflow. Prove the economics. Then expand.