Shipsy AgentFleet — Culina Group AI Transformation

AgentFleet Architecture

A department-first
AI organisation.

AgentFleet mirrors your organisational structure. Every department gets its own team of agents — a supervisor that manages the workflow, and task agents that execute. Humans stay in control through explicit approval gates and real-time dashboards.

Organisation

AgentFleet — Culina Group

One unified AI platform spanning all clusters and departments. Governed by a shared policy layer, with full audit trail and HITL controls.

Customer Transport

Driver Management

Carrier Management

Settlements & Finance

Customer Experience

Teams

Department AI Teams

Each department has a dedicated team of agents scoped to its workflows, data access, and KPIs.

💬

CS Team

Customer service workflows, query resolution, escalation routing

🚛

CT Team

Transport planning, exception handling, delivery monitoring

⚙️

Ops Team

Carrier performance, driver compliance, fleet utilisation

💳

Finance Team

Invoice processing, dispute resolution, settlement reconciliation

Supervisor Agents

Orchestration Layer

Supervisor agents manage workflows end-to-end — delegating to task agents, monitoring outputs, and escalating to humans when thresholds are breached.

CX Co-Worker — Customer Experience

CT Co-Worker — Customer Transport

Ops Co-Worker — Operations

Finance Co-Worker — Finance

Task Agents

Execution Layer

Specialised agents that perform discrete tasks — each governed by an explicit policy, with defined permissible actions and approval requirements.

ETA prediction & proactive re-routing

Driver hours compliance monitoring

Carrier invoice validation

POD capture & exception flagging

Query classification & auto-resolution

Freight audit & discrepancy detection

Dynamic load optimisation triggers

SLA breach early warning

Carrier performance scoring

Driving Results Across Departments

Every department.
Measured outcomes.

🗼

Control Tower

65%

faster exception resolution

Zero coordinator overhead

Monitors network, surfaces exceptions

Triggers resolution autonomously

Pre-alerts · SLA monitoring

Delivery coordination

🚛

Driver Operations

20%

productivity uplift

Real-time driver guidance

Coordination b/w ops · customer · driver

Slot planning · driver coordination

Mobile-first comms

🤝

Carrier Operations

10–12%

ops team productivity uplift

Automated slot management

Intelligent schedule management

Carrier communications

3PL performance monitoring

💷

Settlements

50%

workload reduced

Auto-generates invoices & settlements

Dispute resolution agent

Invoice disputes · Driver payouts

Revenue leakage detection

💬

Customer Experience

30%

fewer queries

All customer/consumer coordination

Inbound + outbound handled by AI

Appointment booking · WISMO

B2B + B2C communication management

Use Case Applicability

Where each agent fits
across Culina's clusters.

Cluster	Companies	Control Tower	Driver Management	Carrier Management	Settlements	Customer Experience
FMCG Ambient	Great Bear · MMi Distribution · Culina Logistics · Warrens	Core Staging Congestion · Multi-Leg Visibility	Core Dock Slot Sequencing · Departure Adherence	Core Slot Confirmation · SLA Re-Tender	Core 4-Way Match · Invoice Freeze	Core Proactive ETA Push · Failure Patterns
Cold Chain & Fresh	Fowler Welch · Eddie Stobart	Core Appointment Monitor · Inbound Forecast	Core Dock Slot Sequencing · Temp Excursion Alert	N/A	N/A	Core Pre-Arrival Alert · Exception Claim
Palletised Network	TPN (56 depots, 5,500+ hauliers)	Core Inbound Forecast · Scan Audit	Strong No-Show Detection · KYC Compliance	N/A	N/A	Core ETA Notification · WISMO Resolution
E-Commerce & Returns	iForce	Core Priority Dispatch · Returns Classification	Applicable Pre-Delivery Call · POD Quality Check	Strong Outbound Slot · Reliability Monitor	Core Claims Adjudication · Automated Settlement	Core WISMO Resolution · Post-Miss Recovery
Specialised & European	IPS · Stobart Europe · IRF · CML	Core Port Clearance · Cross-Border Visibility	Core Fatigue Detection · HOS Compliance	Core CO₂ Tracking · Trial Lane Gating	Strong Detention Billing · Ad-Hoc Charges	Strong ETA Communication · NPS Escalation

Our Approach

Go deep on one.
Then scale.

We don't deploy across every department on day one. We pick the highest-value workflow, build it end-to-end with full policy governance and integration, prove the ROI — then scale iteratively across your BUs and departments.

Discovery

Map all existing workflows, data sources, and pain points across target departments

Opportunity Assessment

Quantify ROI potential and prioritise the highest-value use case to start

Solution Design

Define agent architecture, data flows, integration points, and governance policies

Integration & Data

Connect to your BU stacks via MCP connectors — Paragon, SAP, Oracle, Blue Yonder

Agent Build & Test

Build agents, configure HITL policies, red-team for robustness and bias

Controlled Rollout

Deploy to a single cluster first, track KPIs, iterate within sprint cycles

Scale & Expand

Roll out proven agents across all BUs, adding new departments iteratively

Governance Principles

Every agent decision is governed by explicit policies, not opaque model behaviour
Humans are in the loop at every critical decision point via HITL gates
Each agent is evaluated for factual accuracy, action correctness, and red-team robustness before go-live

Diagnostic-led: map baseline, identify friction, spot agentable workflows, quantify value — before any deployment
Sequence by cluster, not group-wide — FMCG, fresh, ambient, and specialist each differ operationally
Three deployment pathways: core Shipsy platform, non-core BU stacks via MCP connectors, and custom agent capability
Pilot → Scale → Autonomy: prove one workflow fully, then replicate across BUs using the same blueprint

AI Transformation in Action

Proven at scale.
In logistics.

UPS

B2B Customer Experience

▶

Watch case study

UPS

Driving differentiated experience for B2B customers

AI-powered customer experience and coordination agents deployed across UPS's B2B logistics operations.

▶

Heineken — Dispute Resolution Agent

Heineken

Solving vendor financial disputes autonomously via dispute resolution agent

human agents' work automated

121 hrs

1st response time — 80th percentile

572 hrs

resolution time — 80th percentile

Legacy Stack

iTrack Suite

iTrack Branch 145 instances

No shared view across branches

iTrack Central Hub

Receives routes from all 145 branches

custom connector

AWS Lambda

Brittle glue — not built to scale

Custom-built

routes feed

RoadNet (Omnitracs)

Planned a week ahead — can't adapt in real-time

Static

field layer

GeoTrack

Live GPS — best real-time signal

bTrack

Legacy HHT — check-ins & missed stops

$5.3B Global Secure Logistics Player

Control Tower-augmented transformation · 52 countries · 600K+ ATMs · 145 branch instances

30–40%+

manpower savings

60%→80%+

fleet utilisation

145→1

branch instances → single platform

5 agents: SLA Monitoring · Dispatch & Allocation · Route Security · Dwell Time Monitor · Driver Assist.

RainBot — live incident: staged/artificial rain detected near camera

Walmart / Flipkart

Control tower identifying fake delivery attempts due to rains

50–53M

unique visitors in 2025

800+

stores across 14 cities

5M+

orders per month in leading markets

RainBot agent detects fake delivery attempts — AI analyses rain conditions at the delivery location and concludes "staged/artificial rain near camera." Fraud flagged autonomously.

Why Not a Horizontal Platform

Six differentiators no generic
AI platform can match.

Culina will be pitched by horizontal AI platforms and consulting firms. Neither can compete on the six dimensions below — not because Shipsy builds faster, but because a decade of logistics deployments creates compounding advantages that can't be replicated from scratch.

🚀

vs. "give us your SOPs"

Hot Start — Not a Cold Start

Horizontal platforms start from zero. They need your SOPs, your documentation, your discovery work. Most logistics companies don't have clean SOPs — Culina's cross-dock operations run on a 2010-era web app and informal processes. Shipsy solves the industry's cold start problem with a hot start: agents arrive pre-loaded with logistics domain knowledge, then ingest 3–6 months of your actual operational data to auto-generate customer-specific SOPs from what actually happens — not what's supposed to happen.

Proof — Heineken

SOPs were analyzed and agents trained on existing operational documentation as part of deployment prep. No new documentation exercise required before go-live.

🕸️

vs. read-only AI layers

Knowledge Graph + Two-Way Sync

Horizontal AI tools sit on top and observe. Shipsy sits in the middle and orchestrates. The platform maintains live two-way connections with every system of record — it reads from WMS, TMS, ERP, carrier portals, and email, and writes back. An agent doesn't just detect a cross-dock labelling issue; it updates the order record, triggers the ASN, and pushes the correction downstream. The knowledge graph connects entities across systems that no single SOR holds: carrier performance by lane, customer exception history, depot processing patterns.

For Culina

Integration layer is Boomi. Shipsy's two-way sync can unify intelligence across CLL, CML, and Fowler Welch before their underlying systems are fully consolidated.

🧠

vs. generic LLM memory

Domain-Specific Memory

Generic memory systems weight everything equally — last Tuesday's weather delay gets the same treatment as a permanent delivery constraint. Shipsy's agent memory is built around logistics-specific categories: customer preferences (SSCC requirements, preferred carriers, delivery window constraints), lane characteristics (which corridors consistently underperform), exception resolution patterns (what worked, what failed), and operational tribal knowledge — the kind that currently lives in people's heads and leaves when they leave.

For Culina's Xdock

Which suppliers consistently send partial information, which retailers reject pallets for minor label deviations, which carrier combinations create timing conflicts at the dock — the agent retains and applies this systematically.

🎚️

vs. all-or-nothing deployment

Centralised Autonomy Control

Six Culina subsidiaries, multiple BUs, different operational maturity levels. You need to roll out AI incrementally — not big-bang across the group. AgentFleet Command Center provides a single control plane with per-agent, per-workflow, per-BU autonomy configuration. CLL's agents can be at selective autonomy while Fowler Welch (onboarded later) is still observe-only.

Observe

Agent watches and generates recommendations — no actions taken

Recommend

Presents recommended actions with evidence for human approval

Act with Supervision

Specific actions allowed above configurable confidence thresholds

Expanded Autonomy

Gradual widening based on tracked performance metrics

🛡️

vs. "we have guardrails"

Three-Tier Agent Safety Architecture

No horizontal player ships all three layers purpose-built for enterprise logistics. Consulting firms will promise to build it — as a 12-month custom development project.

Pre-release Eval

500+ simulated scenarios from historical data. LLM-as-judge scoring across accuracy, business rule compliance, tool usage. Go-live is gated on pass criteria.

Runtime HITL

Supervisor agent reviews every action live. Can pause mid-task before the customer or downstream system is affected. Mobile-first — approve, override, or escalate from any device.

Post-runtime Drift Detection

Continuous eval catches slow degradation — an agent 99% right today but drifting over weeks. Feeds back into Tier 1 automatically.

📊

vs. generic benchmarks

Proprietary Eval Sets from Cross-Customer Deployments

Horizontal players test against generic AI benchmarks. Consulting firms build test cases from scratch on the customer's dime. Shipsy has built proprietary evaluation sets (EWAS) from dozens of logistics enterprise deployments — encoding the specific failure modes that recur across LSPs, freight forwarders, and 3PLs: exception handling patterns, rate and pricing anomalies, compliance and regulatory gaps, integration failure modes.

What this means for Culina

When Shipsy onboards Culina, agents are already stress-tested against failure modes discovered at Aramex, Heineken, MOVIN, Apollo, and dozens of others. A horizontal player starts with zero logistics-specific eval coverage. This moat compounds with every new deployment.

One thing to note: SAP's API policy blocks horizontal AI players from connecting to core logistics platforms. Shipsy's native connectors sidestep this entirely.

HITL in Action — live across desktop & mobile

AgentFleet Command Center

Every agent decision visible, auditable, and approvable in real time. No desktop bottleneck — supervisors approve, override, or escalate from any device.

🤖

My Agent Fleet

AI Operations Dashboard

ON-TIME ORDER

94.2% ↗

AVAILABLE RIDERS

47 / 52

NEED MY INPUTS

AGENT TASKS

AVG DELAY

8.5 min

🔍 Search agent tasks...

My Agent Fleet ▾

All Status ▾

All Hubs ▾

Today ▾

✓ 3 ▶ 1 ⚠ 5 ⊗ 1

All (10) Need my inputs 6

#338 Interrupted

Trip delayed — Multiple orders at risk of SLA breach

Trip: TRP-NOI-78500

⚠ Need my input

#336 ▶ Running

Address enrichment in progress — Geocoding complete

Consignment: CN-78800

#333 Interrupted

Low confidence address — Multiple possible matches

Consignment: CN-78789

⚠ Need my input

#339 Interrupted

Order OFD — No movement for 8 minutes

Consignment: CN-78234

⚠ Need my input

#334 ✓ Success

Pickup delay detected — Auto-resolved after rider movement

Consignment: CN-78100

Agent Task #329 ⊗ Failed ⚠ Need my input

Vehicle Stoppage > 10 min detected

🚗 Vehicle: VEH-TN01AU4614 # EVENT-12194 ↑ Source: GPS Tracker

CREATED

05 Feb 2026, 01:22:05 pm

STARTED

05 Feb 2026, 01:22:06 pm

COMPLETED

05 Feb 2026, 01:22:10 pm

RUNTIME

VEHICLE NUMBER

TN01AU4614

VEHICLE TYPE

Two Wheeler

RIDER NAME

Kamal

CURRENT HUB

NOIDA-S62

🤖 AI Summary Agent Task : 329 Hide ↑

📄 Details Captured

Vehicle TN01AU4614 has been stationary for 12 minutes at Sector 62, Near HP Petrol Pump. Rider Kamal has 94% on-time history.

● Next Steps

Awaiting OFE action to verify rider status.

Last Updated : 12 mins ago

⚠ Intervention Required 1 of 3 ›

⚡ Live AI Task Stream LIVE

Real-time AI actions across all agents

✓ COMPLETED Cluster rebalancing completed 87%

✓ COMPLETED Cluster rebalancing completed 92%

✓ COMPLETED Auto-resolved delivery delay 91%

⚠ ESCALATED Low confidence score 74%

⚠ ESCALATED Low confidence score 73%

✓ COMPLETED Trip reassigned to nearest rider

📊 KPIs swipe →

MY VIEWS

📋

All Tasks

2 need input · 6 running

🔥

Urgent SLA

oldest: 23m ago

2 INPUT

🚛

Vehicle Stoppage

92% auto-resolved

📦

OFE Escalations

1 need input

✅

Completed Today

avg 4.2 min resolution

🏠
Home

📋
Tasks

⚡
Activity

🩺 Agent Health Today ▾

🚛

Stoppage

92%

📦

OFE

89%

🤳

Selfie

78%

👤

Attend.

95%

📍

Address

84%

🏠
Home

📋
Tasks

⚡
Activity

⚡ Agent Activity All Agents ▾

OFE Assist — Reassigned order to nearby rider Arun (2.1km)

TSK-4012 RESOLVED

Vehicle Stoppage — Auto-call failed, escalated to supervisor

TSK-4001 ESCALATED

Address Intel — Geocode corrected 96% confidence

TSK-4008 RESOLVED

Attendance — WhatsApp sent to 3 unconfirmed riders

TSK-4015 MONITORING

Selfie Valid. — Batch validated 12 selfies, 11 passed

TSK-4016 RESOLVED

View all in Activity →

🏠
Home

📋
Tasks

⚡
Activity

KPIS

Hub: NOI-62 Service: Express

Pickup %

94.2 %

↑ 2.1

FAD %

87.6 %

↑ 1.3

AI Resolve Rate

72.8 %

↑ 4.5

Avg TAT

4.2 min

↓ 0.8

Active Tasks

🏠
Home

📋
Tasks

⚡
Activity

Technical Details Overview

Architecture & key
components.

Seven layers — from system of record to full observability. Each layer is purpose-built for logistics, not adapted from a generic AI stack.

◉

Observability

Audit Logs
Monitoring Key Metrics
Feedback & Real-Time Alerts

◈

Security

Guardrails

Evaluation & Drift Detection

◆

Models

Closed-source LLMs (OpenAI · Gemini · Claude)

Open Source Models (Mistral · Llama)

Vision & Speech Models

Rule-based Proprietary Models

⬡

Memory

Short-Term Memory (Conversation Context)

Long-Term Memory (Vector DB)

⬢

Orchestration

LangGraph-based (open source)
Multi-Node Workflow
Maintains State

⚙

Tools

Event-based Triggers

Custom APIs (Shipsy · Geocoding)

MCP Server

Telephony Integration

▣

System of Record

Shipsy WMS / TMS

ERPs (SAP · Oracle · Dynamics)

EDMS

CRMs

Platform Modularity

Modular and customisable
for every SOR use case.

Models

Closed-source API Models (OpenAI, Gemini, Claude)
Open-Source Models (Mistral, Llama)
Fine-Tuned Domain-specific Logistics Models

Deployment Options

Cloud (default)
On-Premise
Hybrid — on-prem vector DB + online model calls

Integrations & Tools

3rd Party: ERPs, CRMs, TMS/WMS
Telephony via SIP Trunking (Aviva / local providers)
MCP connectors per BU stack

Memory Store

Pinecone (default)
Open-source Vector DBs (FAISS)
Graph DB

What Sets Shipsy Apart

Logistics-specific policies.
Built for ops, not retrofitted.

Guardrails + HITL

Confidence score driven human escalation — low-confidence outputs never auto-execute
HITL Chat interface built in; no separate workflows or portals required
Every permissible agent action is explicitly listed — nothing outside the policy set can execute

Eval Policy

Every agent has a linked evaluation policy — accuracy, action correctness, red-team robustness
Production outputs sampled and scored continuously
Drift triggers an alert and gates the agent from high-stakes actions until reviewed

Example — Follow-up Policy

Agent identifies a driver wants a callback in 30 min → follow-up created with time tracking → auto-triggers at the right time, considering time zone, preferred channel (call/WhatsApp), and no-answer escalation logic.

Governance Framework

8 building blocks
driving agent actions.

Role definition + information access

→

System Prompt + Context

SOPs

→

Grounding (RAG), authored in Docs with versioning + approvals

RBAC (Access Control)

→

AI Employee RBAC

Permissible actions

→

Tools (MCPs)

Approval workflows

→

Human in the Loop (HITL)

Manager feedback

→

Real-time supervision by agent

Performance Reporting

→

Agents Performance Analytics

Memory and context graph

→

Persistent entity knowledge across sessions

Evaluation Framework

Continuous improvement loop
from pre-deployment to production.

Scenario-Based Eval Sets

Dedicated test set per agent use case. Covers edge cases, adversarial inputs, and high-frequency logistics scenarios.

Pre-Deployment Testing

LLM-based evaluators run against eval set. Accuracy, hallucination rate, action correctness — all scored before go-live.

Human Feedback Loop

Users rate agent outputs via UI. Ratings feed back into vector DB and training examples for model improvement.

Continuous Prod Evals

Drift detection via canary tests. A/B benchmarking on prompts and model variants. Anomalies trigger alerts — not silent degradation.

Evaluation Metrics: Factual accuracy · Correctness of actions · Red-team robustness (adversarial) · Bias & fairness · Language tone & clarity

System of Action

Trigger framework — maps ops
signals to executable agent work.

Step 01

Work Creation

Observability layer detects operational signal → Incident or task automatically created with full context attached.

Step 02

Work Assignment

Task assigned to AI workforce. Supervisor agent determines priority, routes to the correct task agent.

Step 03

Work Execution

Agent executes with full context — SOP grounding, relevant history, permissible action set. No guesswork.

Step 04

Manager Intervention

HITL available at any node. Supervisor reviews, approves, overrides, or escalates. Full audit trail logged end-to-end.

Deployment Strategy

Diagnostic and business case
driven from day one.

Our 7-step engagement approach

Understand the baseline

Map systems, volumes, business KPIs, and user workload

Identify friction points

Surface manual tasks across planning, execution, finance, and customer service

Spot "agentable" workflows

Prioritise repetitive, high-effort, high-impact processes suitable for AI agents

Quantify value

Estimate labour hours saved, SLA improvements, cost avoidance, and efficiency gains

Design the blueprint

Select relevant Shipsy agents and workflow packs; define policies and guardrails

Build CFO-ready business case

Model savings vs. implementation cost, ROI timeline, payback period

Align with CXOs

Present opportunities, expected outcomes, and phased rollout plan (Pilot → Scale → Autonomy)

Enabled by the FDE model

A Forward-Deployed Engineer embedded at Culina — not a remote integration project. Same-day turnaround on rule changes.

Shipsy engineer embedded at customer site
Configures agents, guardrails, segmentation rules
Handles integrations with Culina's BU stacks
Runs evals, iterates on business logic
Same-day turnaround on rule changes
Bridges the ops team and the AI system

One config. Reusable workflow packs. Scale from one depot across the entire network.

Technology

System of Action
over System of Record.

Shipsy AI doesn't replace your existing systems. It sits as a System of Action above your Systems of Record — connecting to every BU's stack, normalising data, and executing decisions in real time. Two paths, one outcome.

Path A

Shipsy Core + AI

For BUs adopting Shipsy TMS/WMS as the system of record. Native integration — no connectors, no latency, no data mapping overhead.

Shipsy TMS as the operational backbone
Native Shipsy WMS integration
AgentFleet natively connected to all data
Zero connector overhead — direct data access
Fastest path to go-live

Path B

Your Stack + Shipsy AI

For BUs retaining existing systems — Paragon, SAP TM, Oracle TM, Blue Yonder, Manhattan. No migration required.

MCP connectors per BU's existing stack
Knowledge Graph + Context Layer for entity normalisation
Cross-BU data unified without touching source systems
Paragon · SAP TM · Oracle TM · Blue Yonder · Manhattan · Microlise · Dynamics
No migration, no disruption — AI layer only

⬆ System of Action — Shipsy AgentFleet

Agent Orchestrator (LangGraph)

Observability Engine

Memory & RAG

Knowledge Graph

AgentFleet Command Center

HITL Controls

Comms Channels

reads & writes both directions — no disruption to existing systems

Connector Layer

API / MCP Connectors

Event Streams / Kafka

Carrier Track & Trace

IoT & Telematics (Cold Chain)

Document Ingestion

syncs with existing stack — zero migration required

System of Record — Culina's existing stack (unchanged)

ERP (SAP / Oracle / Dynamics)

TMS (Paragon / Blue Yonder / Descartes)

WMS (Manhattan / Blue Yonder / SAP EWM)

Telematics (Microlise / Webfleet)

Carrier & CRM (Oracle CX / Salesforce)

Shipsy AgentFleet Agentic AI for Culina Group.

Five clusters.Five operating models.

Where AI fits acrossCulina's priorities.

Functional Priorities

Technical Imperatives

Outcomes. Velocity.Best-in-class.

Build it yourself

Pre-built. Pre-trained. Pre-done.

A department-firstAI organisation.

AgentFleet — Culina Group

Department AI Teams

Orchestration Layer

Execution Layer

Every department.Measured outcomes.

Where each agent fitsacross Culina's clusters.

Go deep on one.Then scale.

Proven at scale.In logistics.

Six differentiators no genericAI platform can match.

AgentFleet Command Center

Architecture & keycomponents.

Modular and customisablefor every SOR use case.

Logistics-specific policies.Built for ops, not retrofitted.

8 building blocksdriving agent actions.

Continuous improvement loopfrom pre-deployment to production.

Trigger framework — maps opssignals to executable agent work.

Diagnostic and business casedriven from day one.

Our 7-step engagement approach

Understand the baseline

Identify friction points

Spot "agentable" workflows

Quantify value

Design the blueprint

Build CFO-ready business case

Align with CXOs

Enabled by the FDE model

Configure, clone, and deployin minutes.

Clone or create

Configure scope

Attach tools

Set policies

Add custom SOPs

System of Actionover System of Record.

Shipsy Core + AI

Your Stack + Shipsy AI

Built for logistics.Not adapted for it.

How Shipsy AI Connects to Culina's BU Stacks

Culina's BU SORs (indicative list, outside-in)

Connectors

Intelligence Layer

Outputs

Governance

Governance-first. Always.

From conversationto live agent — in weeks.

2–3 Day Diagnostic Workshop

Blueprint + CFO-Ready Business Case

Weeks to First Agent. Months to Value. Not 18 Months.

Ready to start?

Shipsy AgentFleet
Agentic AI
for Culina Group.

Five clusters.
Five operating models.

Where AI fits across
Culina's priorities.

Outcomes. Velocity.
Best-in-class.

A department-first
AI organisation.

Every department.
Measured outcomes.

Where each agent fits
across Culina's clusters.

Go deep on one.
Then scale.

Proven at scale.
In logistics.

Six differentiators no generic
AI platform can match.

Architecture & key
components.

Modular and customisable
for every SOR use case.

Logistics-specific policies.
Built for ops, not retrofitted.

8 building blocks
driving agent actions.

Continuous improvement loop
from pre-deployment to production.

Trigger framework — maps ops
signals to executable agent work.

Diagnostic and business case
driven from day one.

Configure, clone, and deploy
in minutes.

System of Action
over System of Record.

Built for logistics.
Not adapted for it.

From conversation
to live agent — in weeks.