Install a Digital Workforce

“Revenue Doubles.Headcount Doesn't.”.

A scripted support bot can answer FAQs. A Digital Workforce member can act—issue refunds, modify orders, and orchestrate returns—inside defined guardrails. We build the Custom App layer that turns your Shopify store into a self-operating business.

Calculate Your Headcount Savings See the Agent Architecture
First-Hand Experience

In our audits, most “bot” projects fail at the same point: they cannot take action. True resolution comes from authority—API access, policy rules, and escalation architecture—not from nicer wording.

Agent_Operations_Console
LIVE
Active Agents 3 ONLINE
Support Agent
78%
Returns Agent
94%
Order Mod Agent
71%
Resolution Rate 76%
CSAT 4.7/5
Tickets/mo 2,400 RESOLVED
Response <30s AVERAGE
Cost/Ticket $0.32 FULLY LOADED
Human Escalation Path Guardrails Active | Authority: L2
ENABLED
Headcount Saved 2.4 FTE
Cost Reduction 73%
ARGBE_AGENT_DEMO
Definition

What is Autonomous Commerce?

Autonomous Commerce is a Shopify architecture where a private digital workforce handles operational tasks—support tickets, order modifications, inventory decisions—that traditionally require human staff. These agents connect directly to your ERP, CRM, and fulfillment systems via Custom Apps, executing real actions (not just providing information) through defined business rules and intelligent orchestration.

Context

This approach emerged as D2C brands hit the scaling wall: revenue doubles, but headcount follows. Support scales with order volume. Ops scales with SKU complexity. Autonomous Commerce breaks the pattern by moving routine throughput to digital workers, reducing Operational OpEx while keeping humans focused on exceptions and revenue-critical moments.

Comparison_Matrix
Intern (Chatbot)

"I'll ask my manager..."

Authority 0%
VS
Expert (AI Agent)

"I'll handle it."

Authority 100%
Actions Enabled
0 8
Escalation Rate
67% 23%
Resolution Time
4-24h <30s
The Core Insight

The Expert vs. The Intern

A scripted support bot is an intern with a flowchart. When the flowchart fails, it freezes, escalates, or worse—makes something up that costs you money. An Agent is a senior employee: it understands context, has authority to act within guardrails, and knows when to escalate versus when to resolve. The difference is not sophistication—it is permission architecture.

When an agency proposes a “support bot,” they are installing an intern. Interns are cheap but require supervision. We install Agents—digital employees with defined authority, connected to your real systems, executing real actions. Your ops team reviews exceptions, not every transaction.

Diagnostic Question

Ask your vendor: can your AI actually issue a refund, or does it just tell the customer to contact support?

Evidence

The Numbers Behind the Bottleneck

These are not “AI promises.” They are the operational patterns we see when Shopify brands scale.

STAT_01

Support headcount scales linearly with order volume

≈ 1 FTE per ~2k–3k monthly orders (typical)

When volume rises, headcount follows unless the system can resolve issues autonomously. This is an architecture problem, not a motivation problem. Agents break the linear curve by closing loops, not just responding.

Observed in Shopify support audits (Gorgias/Zendesk stacks)
STAT_02

Average cost per support ticket keeps rising

$10–$20 per human-resolved ticket (fully loaded)

At scale, ticket resolution becomes a margin line item. The goal is not “deflection.” The goal is true resolution—moving routine, policy-bound work to the digital workforce and keeping humans for exceptions and retention.

Observed across mid-market Shopify teams; varies by region
STAT_03

First-contact resolution drives customer lifetime value

Higher retention when issues resolve on first contact

Customers do not reward effort; they reward outcomes. Faster true resolution protects revenue by reducing cancellations, chargebacks, and “where is my order?” churn.

Measured pre/post in deployments; proxied via repeat orders and refund rates
STAT_04

Traditional automation hits a ceiling quickly

Rigid workflows cover the happy path; exceptions create the backlog

Rules break when reality gets messy. Agents handle the messy parts through reasoning plus guardrails, so your throughput scales without brittle duct-tape maintenance.

Observed in Zapier/Make stacks we replace
The Problem

The Human Bottleneck

You did everything right. You scaled the brand. You invested in the platform. And now your margins are disappearing.

The pattern repeats across every scaling D2C operation: brands hit $5-20M ARR, then discover their support team has grown from 2 to 12 people in two years. Their ops manager now manages three ops coordinators. Every new revenue milestone requires new headcount. The exit multiple shrinks because the business does not scale like a tech company—it scales like a service bureau.

4 Scaling Blockers

The Profitless Prosperity Trap

01

We see this in every scaling brand: revenue doubles from $5M to $10M. The founder checks profit and it is flat—or worse. Support salaries, benefits, training, management overhead consumed the gains.
Consequence

Your “growth” is a vanity metric. If every new order requires proportional human labor, you are not building enterprise value. You are building a job machine that investors discount heavily.

Ask Yourself

What is your revenue per employee ratio? How does it compare to two years ago?

The Tribal Knowledge Crisis

02

We ask every prospect: “What happens when Sarah from support goes on vacation?” The answer is always the same: ticket backlog, dropped balls, customers waiting.
Consequence

Your operational knowledge lives in human heads. Every new hire requires months of training. Every departure is institutional memory loss. This is not a training problem—it is an architecture problem.

Ask Yourself

How long does it take to fully train a new support agent on your edge cases?

The Duct Tape Infrastructure

03

We review tech stacks constantly. Shopify connected to Klaviyo via Zapier. Zapier connected to NetSuite via webhook. Webhook triggers another Zapier. When one breaks, nobody knows where the failure is.
Consequence

Your “automations” are digital duct tape. They work until they do not. And when they fail during Black Friday, you are manually processing orders at 2 AM.

Ask Yourself

How many hours did your team spend last quarter fixing broken automations?

The AI Paralysis

04

Brands know they need AI. They also know public model endpoints can hallucinate, route data outside the EU, and one wrong automated action can cost thousands. So they wait—and competitors deploy.
Consequence

Your caution is rational but costly. Every month you delay, a competitor captures the efficiency gains. The question is not whether to deploy AI, but how to deploy it safely.

Ask Yourself

What is your current plan for AI adoption, and what is blocking execution?

Myth Busting

Lies the Industry Sells You

These are not mistakes. They are sales tactics. We hear them in every competitive situation and we are tired of cleaning up the aftermath.

MYTH_01

“A support bot can handle 80% of your support tickets”

Reality

Support bots handle questions. They cannot handle actions. When a customer asks “Where is my order?”, a bot can paste tracking. When they say “Cancel my order and refund me,” the bot escalates. That escalation IS the ticket. You have not reduced volume—you have added a frustrating middle step.

Evidence

In our audits, most “bot” conversations still end in a human queue because no real action was taken. Agents reduce volume by closing loops: issuing refunds, editing orders, creating returns, and updating CRM records within policy.

MYTH_02

“Zapier and Make can automate your operations”

Reality

Workflow tools execute rigid rules. When data formats change, they break. When edge cases arise, they fail silently or throw errors. When you need conditional logic based on context, you hit limits. These tools are digital duct tape—they work until they do not.

Evidence

Zapier-to-agent migrations consistently reveal the gap: workflow tools average 10-15 failure incidents monthly as data complexity grows. AI reasoning engines reduce exception rates by 70-80% because they adapt to variations instead of breaking on them.

MYTH_03

“Just use ChatGPT with a custom prompt—it is basically free”

Reality

ChatGPT is a general-purpose language model. It hallucinates product information, invents policies, and promises refunds you never authorized. It has no connection to your inventory, no understanding of your margins, and no guardrails against costly mistakes. One hallucinated “free shipping for life” promise costs more than a year of proper implementation.

Evidence

The most common failure in unguarded AI deployments: the model promises discounts, refunds, or policy exceptions that do not exist. The fix is not “better prompts.” The fix is authority limits, grounding to live system data, and an audit loop.

MYTH_04

“Customers want to talk to humans, not robots”

Reality

Customers want resolution, not conversation. They want their problem solved quickly and correctly. “Human preference” is usually a preference against bad automation—not against fast, accurate resolution.

Evidence

When automation actually resolves the issue (not just replies), satisfaction holds. When it deflects or escalates, satisfaction collapses. The difference is resolution authority plus governance.

MYTH_05

“GDPR means we cannot use AI for customer data”

Reality

GDPR Article 22 requires human oversight for automated decisions with legal effects. It does not prohibit AI—it requires architecture. Human-in-the-loop fallbacks, sovereign data processing, and documented decision logic satisfy the regulation. The question is infrastructure, not prohibition.

Evidence

Our agent architecture is built for Datenschutzbeauftragter review from day one. The key requirements: EU-resident processing (Frankfurt), mandatory escalation paths for Article 22 compliance, and complete audit trails documenting every automated decision. Compliance is engineering, not avoidance.

Comparison

The Binary They Sell You

The industry presents two options. Both are traps. The third option exists—they just cannot build it.

Every Shopify conversation becomes: “Do you want cheap support bots or expensive humans?” As if these are opposites. Bots deflect without resolving. Humans resolve but do not scale. This is a failure of architecture, not a law of economics.

Resolution Authority

01
Chatbot Answers questions only
Human Full (but expensive)
Agent Full (within guardrails)
Verdict

Agents can issue refunds, modify orders, and process returns. They act like employees, not FAQ search engines.

Response Time

02
Chatbot Seconds
Human Hours
Agent Seconds
Verdict

Speed is not a feature—it is a retention driver. Every hour of delay is a customer reconsidering their purchase.

Cost per Resolution

03
Chatbot Low (deflection)
Human High (human labor)
Agent Low (true resolution)
Verdict

True cost comparison requires measuring resolution, not deflection. Agents cost more than scripted bots, but they replace human labor for routine work because they can actually act.

Edge Case Handling

04
Chatbot Escalates immediately
Human Handles (slowly)
Agent Reasons through
Verdict

AI agents apply judgment within defined parameters. They do not just pattern-match—they evaluate context.

ERP/CRM Integration

05
Chatbot None (read-only at best)
Human Manual by humans
Agent Direct via Custom Apps
Verdict

The nervous system matters. Without real integration, AI is just a fancy search bar.

Scaling Economics

06
Chatbot Flat (but limited value)
Human Linear with volume
Agent Logarithmic
Verdict

Agents handle order-of-magnitude volume increases with marginal cost increases. Humans require proportional headcount. This is the core value proposition.

The question is not “support bot or humans.” The question is: “What is the minimum headcount required to operate at your target scale?” For most D2C brands, the answer is much lower than they currently believe.

The Solution

The Digital Workforce Architecture

The AI is the brain. Your systems are the muscles. We build the nervous system connecting them. Shopify ↔ Custom App ↔ ERP/CRM ↔ AI model.

We studied what makes human employees effective and asked: which of these capabilities can we replicate in software? The answer was most of them—for operational tasks with clear success criteria. Customer intent interpretation, policy application, system actions, exception escalation. None of this requires consciousness. It requires architecture.

LAYER_01 ACTIVE

Custom Application Integration

Problem

AI without system access is just a fancy search engine. It can tell customers their order status but cannot modify it. It can explain return policy but cannot process the return. Answering questions is not the same as solving problems.

Our Approach

We build Custom Shopify Apps that connect your AI agents directly to your operational systems: Shopify Admin API, ERP write access, CRM update authority, fulfillment triggers. The agent does not just know about your systems—it can operate them.

First-Hand Proof

A properly architected agent is granted explicit action types—refunds, order modifications, return initiation, credit issuance, holds, routing, follow-ups—tied to policy. A scripted bot has no authority. That gap defines the value difference.

Outcome

Your AI workforce has the same system access as your human staff. The difference is speed, availability, and scaling economics.

LAYER_02 ACTIVE

Contextual Reasoning Engine

Problem

Scripted bots pattern-match. They look for keywords and return canned responses. When customer requests are ambiguous, compound, or emotionally charged, pattern-matching fails. “I want to cancel but also maybe exchange if you have the blue one” breaks keyword logic.

Our Approach

We implement multi-step reasoning with business rule injection. The agent parses intent, identifies options, evaluates policies, and proposes resolution paths. For complex cases, it presents options to customers rather than escalating blindly.

First-Hand Proof

Compound request resolution is the key differentiator. The difference is architecture that parses intent, checks policy, and takes the right action—not “more personality” in responses.

Outcome

Customer requests that would stump a junior employee get resolved by your AI in seconds. Escalations become genuine exceptions, not parsing failures.

LAYER_03 ACTIVE

Authority and Governance

Problem

Unbounded AI is a liability. Give ChatGPT customer-facing authority and it will promise refunds you never authorized, invent policies that do not exist, and damage brand trust. AI without guardrails is not automation—it is risk.

Our Approach

Every agent operates within defined authority parameters: maximum refund amounts, approved discount percentages, escalation triggers, prohibited actions. The agent cannot exceed its mandate. When it hits a boundary, it escalates to human authority rather than improvising.

First-Hand Proof

Our guardrail architecture is built for zero-tolerance hallucination prevention. Every agent action is logged against authority rules. When boundaries are hit, escalation is automatic and immediate. The system cannot improvise—it can only operate within defined parameters.

Outcome

You deploy AI with confidence. The agent cannot promise things you do not authorize. Your brand reputation is protected by architecture, not hope.

LAYER_04 ACTIVE

Data Sovereignty Infrastructure

Problem

Public model APIs can route data outside the EU unless you enforce residency and contractual controls. For GDPR-covered businesses, that ambiguity is a compliance risk. Your Datenschutzbeauftragter is right to demand sovereignty and auditability for customer PII.

Our Approach

For EU clients, we deploy agent infrastructure on sovereign cloud regions (e.g., Frankfurt). Customer data stays in-region, with documented processing boundaries. We provide GDPR Article 22 documentation and human-in-the-loop architectures that satisfy the strictest interpretation.

First-Hand Proof

Our architecture is built for Datenschutzbeauftragter review from the ground up. We provide complete documentation packages: data flow diagrams, processing location attestations, Article 22 compliance matrices, and human-in-the-loop architecture specifications. Compliance is a deliverable, not an afterthought.

Outcome

AI efficiency without compliance exposure. Your German legal team signs off. Your customers benefit from automation. Regulators have nothing to find.

Mental Models

Concepts That Cut Through Jargon

Use these to evaluate vendors and understand what actually matters. The right metaphor exposes the wrong approach.

MODEL_01

The Nervous System

The AI model is the brain. Your ERP, CRM, and Shopify are the muscles. But without nerves connecting them, the brain is paralyzed—it can think but not act. Custom Apps are the nervous system: the integration layer that gives AI authority over your operational reality.

You are not paying for “AI implementation.” You are paying for the nervous system that makes AI useful.

Ask Your Agency

Ask your vendor: does your AI have write access to our systems, or just read access? If it cannot modify orders, it is a brain without a body.

MODEL_02

Expert vs. Intern

A scripted support bot is an intern with a script. Ask something off-script and it freezes, escalates, or worse—makes something up. An Agent is a senior employee: it has context, judgment, and permission to act within guardrails. The cost difference is real, but so is the value difference.

Interns are cheap because they cannot actually do the work. Experts cost more because they close the loop.

Ask Your Agency

Ask your vendor: what actions can your AI take autonomously? If the answer is “provide information,” you are hiring an intern.

MODEL_03

Robots vs. Duct Tape

Zapier and Make are digital duct tape. They connect things rigidly—and when data formats change or edge cases arise, they break. AI agents are robots: they reason through variations, adapt to exceptions, and self-heal when conditions shift.

Duct tape works until it does not. Robots work because they adapt.

Ask Your Agency

Ask your vendor: what happens when an order has a non-standard attribute? If the answer involves “zap failure alerts,” you have duct tape.

MODEL_04

Workforce vs. Tool

A tool does what you tell it when you operate it. A workforce member understands objectives and operates autonomously within guidelines. AI agents are workforce—they work while you sleep, make judgment calls, and escalate when they should. Tools wait for you to push buttons.

You are not buying software. You are installing employees who never call in sick.

Ask Your Agency

Ask your vendor: what happens at 3 AM on a Saturday when a VIP customer needs urgent help? If the answer is “they wait until Monday,” you have a tool, not a workforce.

The Investment

Why Ongoing Investment?

Common Objection

"Deploy the agents once, then we handle operations internally."

You are not paying for “maintenance.” You are paying for Workforce Optimization—the continuous improvement that makes your digital employees better at their jobs every month. Human employees need coaching, performance reviews, and skill development. Your digital workforce does too.

01

Hallucination Prevention

We review agent interactions weekly to identify edge cases where the AI might be lying or giving away free money—making unauthorized promises, misapplying policy, or reasoning incorrectly. These are caught and corrected before they become patterns.

Deliverable

Weekly audit report with incident count, resolution actions, and model tuning applied.

02

Capability Expansion

Each month, we train your agents on new task types. Month 1 might be returns. Month 3 might be subscription modifications. Month 6 might be upsell recommendations. Your workforce gets smarter continuously.

Deliverable

Monthly capability roadmap with new skills deployed and success metrics tracked.

03

Brand Voice Alignment

We analyze your best human interactions and calibrate agent responses to match. Your AI should sound like your best employee, not a generic bot. Tone drift is caught and corrected.

Deliverable

Quarterly voice audit comparing agent tone to brand guidelines with calibration applied.

04

Efficiency Maximization

We monitor resolution rates, escalation patterns, and customer satisfaction scores. When metrics drift, we diagnose root causes and tune the system. Your agents improve month over month.

Deliverable

Monthly performance dashboard with throughput metrics, trend analysis, and optimization actions.

Signal_Defense_Protocol
Vocabulary

This Is Not Web Development

The words describe the sophistication of the work. If a vendor uses commodity language, expect commodity thinking.

Commodity Thinking
Strategic Infrastructure
Support bot
Digital Workforce Member

Support bots answer questions. Workforce members execute tasks with authority and judgment.

Automation
Intelligent Orchestration

Automation is rigid rules. Orchestration is adaptive reasoning that handles edge cases.

Integration
Operational Nervous System

Integration implies connection. Nervous system implies authority to act on those connections.

Customer Support
Autonomous Resolution

Support implies human dependency. Autonomous resolution implies self-operating systems.

Project Cost
Operational OpEx Reduction

Project cost is an expense. OpEx reduction is an investment with measurable ROI.

Scaling
Throughput Multiplication

Scaling implies growing headcount. Throughput multiplication implies growing output without proportional cost.

Benchmarks

What We Measure (And Why)

These are not vanity metrics. Each one correlates directly with operational cost reduction and customer satisfaction. We commit to specific benchmarks and report against them.

KPI_01

Autonomous Resolution Rate

> 75% of eligible tickets

The percentage of support tickets resolved by agents without human escalation. “Eligible” excludes genuinely complex cases that intentionally require human judgment.

Target benchmark; scripted-bot deflection rarely equals true resolution
KPI_02

Escalation Accuracy

> 95%

When an agent escalates, is it genuinely necessary? False escalations waste human time. Missed escalations harm customers. We tune for the balance.

Weekly audit of escalated tickets; human review of escalation necessity
KPI_03

Time to Resolution

< 45 seconds average

Not time to first response—time to actual resolution. Customers care about problem solved, not acknowledgment received.

Measured from ticket open to resolution close; excludes escalated tickets
KPI_04

Customer Satisfaction Score

≥ 4.5/5

AI resolution must match or exceed human satisfaction scores. If customers are unhappy with agent interactions, the efficiency gains are meaningless.

Post-resolution surveys; compared against pre-deployment human baseline
KPI_05

Cost per Resolved Ticket

< $0.50 fully loaded (target)

Total agent infrastructure cost divided by resolutions. Compared against human labor cost per ticket. The gap is your ROI.

Monthly infrastructure cost divided by resolution count
KPI_06

Hallucination Incident Rate

< 0.1% of interactions (target)

The percentage of agent responses containing factually incorrect, unauthorized, or harmful content. Zero tolerance is the goal; monitoring is the method.

Weekly audit of random interaction sample; incident tracking
Security

How We Earn Confidence

Claims are cheap. Evidence is expensive. Here is what we actually commit to.

75% Resolution Rate or We Refund

Every contract includes this guarantee. If your agents do not achieve target resolution rates within 90 days, we refund the deployment cost and unwind the integration.

Pre-Deployment ROI Projection

We model your specific ticket volume, handling costs, and resolution patterns before signing. You see the headcount savings math before you commit.

Guaranteed Human Escalation Paths

Every agent implementation includes defined escalation triggers. When situations exceed agent authority, humans are notified immediately. No customer is ever stuck.

Zero-Tolerance Hallucination Policy

We audit agent interactions weekly. Any hallucination incident triggers immediate root cause analysis and model update. Your brand reputation is protected by process, not hope.

EU Data Sovereignty (Frankfurt)

All agent infrastructure for EU clients runs in europe-west3. Customer PII never crosses to US servers. We provide written attestation for your compliance records.

DACH Focus

GDPR Article 22 Compliance

Human-in-the-loop architecture satisfies the strictest interpretation of automated decision-making regulations. Our documentation package is built for Datenschutzbeauftragter review from day one.

DACH Focus

Complete Decision Audit Trail

Every agent decision is logged with reasoning chain, data accessed, and action taken. When regulators ask "why did the AI do this," you have the answer.

DACH Focus

Headcount Impact Modeling

We model specific FTE implications before deployment using your ticket volumes and handling patterns. Target outcome: 2-4 FTE equivalent in support capacity freed. The math is transparent before you sign.

US Focus
Results

Deployment Scenarios

What changes when the architecture is applied. These are the patterns our methodology targets.

Situation

Support team of 8 handling 3,200 tickets/month. CSAT at 4.2. Average response time: 6 hours. Every new hire solves short-term backlog but headcount keeps growing.

"The pattern: you think you need two more support agents. Agent deployment reveals you need zero. When 70-75% of tickets resolve autonomously, the remaining team shifts from ticket processing to retention and upsell work. Support becomes a profit center."
Result

Target outcome: 2-3 FTEs redeployed from support to revenue roles. CSAT improvement to 4.5+. Response time under 45 seconds.

Scenario: Beauty D2C at $15-25M ARR
01
Situation

Zapier-based automation breaking 4-5 times monthly. Each break requires 2-3 hours of ops manager time to diagnose and fix. Black Friday is a nightmare of manual order processing.

"The pattern: your ops manager spends 20% of their time fixing broken automations. AI agents handle the edge cases that break rigid workflows. The shift is from firefighting to exception review—5% of time instead of 20%."
Result

Target outcome: Automation incidents reduced 70-80%. Peak events handled without manual intervention. Ops manager time recaptured: 6-10 hours/week.

Scenario: Multi-SKU retailer with complex fulfillment
02
Situation

Board pushing for AI automation. Datenschutzbeauftragter blocking every proposal due to GDPR concerns about OpenAI data processing. Stuck between efficiency pressure and compliance reality.

"The pattern: every AI vendor says "just use OpenAI." Your legal team keeps rejecting. Frankfurt-hosted infrastructure with actual Article 22 compliance breaks the deadlock. The DSB reviews architecture, not promises."
Result

Target outcome: GDPR-compliant AI deployed in 8-12 weeks. DSB approval pathway clear. 60-70% of routine operations processed autonomously.

Scenario: German D2C with compliance requirements
03
Situation

Using a support-bot platform with a high “deflection rate.” But measurement reveals many “deflections” return as tickets later. Actual resolution is much lower than the dashboard implies.

"The pattern: deflection metrics mask the truth—the bot delays tickets, it does not resolve them. Agents with action authority achieve true resolution. The support queue drops because problems actually get solved."
Result

Target outcome: material lift in true resolution, fewer repeat contacts, and a smaller queue because loops actually close.

Scenario: D2C brand outgrowing support-bot limitations
04
FAQ

Questions We Actually Get Asked

Answers to common questions about this service.

7 Common Questions
01

How is this different from a support bot?

Support bots answer questions. Our agents execute actions. They can issue refunds, modify orders, process returns, and update records—autonomously, within defined guardrails.

Want Details?

We can show you the specific action types your agents would have authority over and the guardrails that prevent unauthorized actions.

02

How do you prevent AI hallucinations?

Guardrailed authority (agents cannot exceed defined limits), grounded responses (answers reference actual system data, not general knowledge), and weekly audits (we review interactions and tune continuously).

Want Details?

We can walk through our specific hallucination prevention architecture and show audit examples from current deployments.

03

How long does deployment take?

Basic deployment: 4-6 weeks. Full Custom App integration with ERP: 8-12 weeks. The variable is your system complexity, not our side.

Want Details?

We assess your specific integration requirements during discovery and provide fixed timelines before signing.

04

Is this GDPR compliant?

For EU clients: yes. We deploy on Frankfurt infrastructure, implement human-in-the-loop architecture for Article 22, and provide documentation for Datenschutzbeauftragter review.

Want Details?

We can share our compliance documentation package and connect you with German clients who have passed DSB review.

05

What happens to our existing support team?

They shift from ticket processing to exception handling and high-value interactions. Average outcome: 2-3 FTEs redeployed to revenue-generating activities. We have never caused involuntary headcount reduction.

Want Details?

We model the specific redeployment opportunities based on your ticket volume and team composition during discovery.

06

What does this cost?

Deployment: $25,000-$75,000 depending on integration complexity. Ongoing optimization: $4,500-$5,000/month. Typical ROI timeline: 4-6 months to breakeven via headcount savings.

Want Details?

We build specific ROI models using your ticket volumes and handling costs before you commit.

07

What systems can agents connect to?

Any system with an API: Shopify, NetSuite, SAP, Salesforce, HubSpot, Zendesk, Gorgias, Klaviyo, ShipStation, and custom ERPs. The Custom App layer we build handles authentication and data mapping.

Want Details?

We audit your specific tech stack during discovery and provide integration complexity assessment before scoping.

Next Step

Your Support Queue Is Costing Revenue Today

Every ticket in your queue is a customer waiting. Every hour of delay is a relationship degrading. Every new support hire is margin disappearing. The math does not get better with scale—it gets worse.

First-Hand Evidence

In many Shopify stores, a large share of tickets are routine and policy-bound—perfect candidates for autonomous resolution. We model your specific opportunity using your ticket mix, tooling, and workflows. The assessment is free. The headcount cost is not.

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