The engine behind every Augmented Labs agent

One operating layer for channels, context, actions, integrations, testing, analytics, and governance. Managed end to end from launch through scale.

One intelligence layer across every channel

Customers move between voice, chat, email, and messaging without starting over. Customer history, account data, enterprise knowledge, policies, and real-time context stay with the conversation.

JM
Jordan M.Case #48213 · Order #10241
Voice
Where’s my order?
The agent verifies identity by phone, locates order #10241, and lets Jordan know it shipped this morning.
I’m back about the order from this morning.
The platform immediately recognizes Jordan’s identity and the case from the call. No re-verification, no repeating the story.
Attached: proof of delivery address.
The agent continues the same case from an inbound email, matching it automatically and updating the record, without asking Jordan to start over.
Persistent context
Jordan M.Case #48213 · Order #10241

Verified customer · account recognized

Voice → Chat → Email

Order #10241 · shipped this morning

Delivery policy · address verification

Order status synced · latest action recorded

The channel can change. The context doesn’t.

From conversation to completed outcome

Agents understand the request, connect to the right systems, take action, and confirm the result in real time.

Customer request

“I need to move my appointment to Friday and update the card on file.”

Confirmed
Appointment moved to Friday · 3:30 PM
Payment updated · customer notified

Awaiting confirmation…

Verify & retrieve
Identity verified
Booking retrieved
Identity & booking
Schedule
Friday 3:30 PM found
Appointment moved
Scheduling system
Payment
Payment method updated
Billing system
Confirm
Confirmation sent
Customer

Tested before deployment, refined after launch

We validate every agent against realistic scenarios, policies, guardrails, and expected outcomes before deployment, then use real interactions and evaluation data to continuously refine performance after launch.

Before deployment
Scenario 042 RUNNING
Customer attempts an edge-case policy request outside standard refund windows.
Accuracy
Policy adherence
Tone
Action execution
Resolution
Reason: policy threshold not met.
Version comparison · requires approval to deploy
After launch
01
Production conversations
02
Insights
03
Recommendations
04
Testing
05
Approval
06
Deployment
Back into production conversations

Every improvement is surfaced, reviewed, tested and approved before it deploys. Nothing learns or ships on its own.

Complete visibility through Console

Console gives your team continuous visibility into performance, conversations, testing, governance, deployment status, outcomes, and changes across your environment, while we handle day-to-day operations.

console.augmentedlabs.ai
Console
Agents
Voices
Conversations
Insights
Telephony
Phone Numbers
Batch Calls
Knowledge Base
Tools
Team
Triage Agent / Main
Deployment status
Live · version 14 · approved Aug 3
Performance
84.2% resolution · 3.1% escalation
Knowledge sources
3 connected · last synced 2 hours ago
Testing
42 scenarios · 41 passed · 1 under review
Guardrails
12 active policies · 0 pending overrides
Change history
Last change reviewed and approved by the Ops team
Explore Console

Human oversight and governance

Policies, permissions, guardrails, approvals, and human escalation work together so every agent operates within defined boundaries and transfers full context when human judgment is required.

Governed decision
Complex billing dispute
Policy and confidence check
Resolve
Take action
Ask for approval
Escalate
Identity & access

Authentication, permissions

Agent guardrails

Policies, approvals, escalation rules

Data governance

Retention, auditability, governed access

Human oversight

Escalation, review, accountability

Context transferred Ready for human

Conversation history
Customer identity
Customer profile
Relevant context
Actions already taken
Reason for escalation
Certified & compliant infrastructure: SOC 2 Type II ISO 27001

Infrastructure designed to evolve

The Platform orchestrates speech, reasoning, real-time interaction, telephony, data, models, and deployment infrastructure behind every agent.

Platform orchestration
Interaction layer
Speech
Real-time interaction
Telephony
Intelligence & data layer
Reasoning
Models
Data
Deployment layer
Deployment infrastructure

Underlying infrastructure can evolve as technology improves. The Platform remains the orchestration and governance layer behind every agent.

Operated from launch to scale

We design, deploy, operate, monitor, test, and continuously refine your conversational agents around your systems, policies, and customer experience.

01DiscoverIdentify workflows and objectives
02DesignAgents, systems, policies and experience
03DeployIntegrate, validate and launch
04OperateMonitor, test and continuously refine
05ScaleExpand teams, channels, regions and volume

Give us every conversation. We’ll take it from there

From your highest-volume interactions to your most complex cases, see what happens when conversational agents are built to actually get things done.

See it in action
🇺🇸 English

Experience a live conversational agent.