Commerce · Customer Experience
Cut First-Response Time 90% in Ecommerce Customer Service Automation
DTC founders, marketplace operators, growth teams, and ecommerce managers usually arrive here with two questions: what does AI-native customer service automation actually ship, and what does it cost. Both are answered below, alongside the operating posture and the governance frame.
Projects from $15k · Refundable 7 days · Kickoff within 5 days
Early access: we work with a small first cohort. Engagements are scoped, priced, and shipped end-to-end by our team — not referred to third parties.
In one sentence
AI-native customer service automation for ecommerce — Production customer service automation for ecommerce delivered in vertical slices, each gated by the labelled test set captured during Discovery, each handing operational ownership progressively to your team. Expected delta on first contact resolution: −78%.
Key facts
- Industry
- Ecommerce
- Use case
- Customer Service Automation
- Intent cluster
- Customer Experience
- Primary KPI
- first contact resolution, support cost per case, CSAT, and backlog age
- Top benchmark
- Time-to-value for new customer: 18 days → 4 days (−78%)
- Systems integrated
- Shopify, marketplaces, PIM
- Buyer
- DTC founders, marketplace operators, growth teams, and ecommerce managers
- Risk lens
- incorrect product claims, privacy, ad policy violations, inventory promises, and margin erosion
- Engagement timeline
- Discovery 2 weeks → Build 6 weeks → Run continuous
- Team size
- 1 senior delivery + founder oversight
- Discovery price
- $5k · 2-week sprint
- Build price
- $18k–$25k · 6-9 weeks
Primary outcome
reduce support volume while improving response quality
What we ship
AI service desk, escalation paths, knowledge workflows, and quality dashboards
KPIs we report on
first contact resolution, support cost per case, CSAT, and backlog age
Why Ecommerce teams hire us for this
CAC, LTV, conversion rate, AOV, repeat purchase rate, and support cost per order. That is the line that gets quoted in the board deck for ecommerce, and that is the line our work moves. Everything we ship on customer service automation — the workflow design, the prompt library, the reviewer queues, the evaluation harness — exists to push that metric. If a deliverable does not connect to it, we strip it out of the SoW.
Zendesk and Salesforce CX research show that ecommerce customers tolerate AI-assisted service when the escalation path to a human is fast and obvious. We design the escalation surface before we design the automation.
Industry context: Mid-market and enterprise operators face the same fundamental tradeoff: AI must compress operational cycle time while remaining auditable and integrable with existing systems of record.
Benchmarks we hit
Reference benchmarks from production deployments of customer service automation in ecommerce-comparable contexts. Sources noted per row. Your actuals are measured against the baseline captured in Discovery.
| Metric | Industry baseline | AI-native typical | Delta |
|---|---|---|---|
Time-to-value for new customer Personalized onboarding paths assembled from customer signal + product graph | 18 days | 4 days | −78% |
First-contact resolution rate Zendesk CX Trends benchmark; lift attributed to context retrieval before agent touch | 54% | 78% | +24 pts |
Median response time AI handles 80% of intents; humans handle the 20% that need judgment | 4h 22min | 47s | −99.7% |
Benchmarks are reference values from comparable engagements and authoritative sector benchmarks. Your engagement's baseline is captured during Discovery and actuals are reported weekly during Run against that baseline.
How we operate the workflow
Three commitments anchor how we run customer service automation in production for ecommerce: every output is grounded in an approved source, every action is logged with the prompt and model version that produced it, every reviewer decision feeds the next iteration. Drop any one of the three and the workflow degrades within weeks — we have seen it happen, so we ship all three from week one.
What we build inside the workflow
The Build phase for customer service automation in ecommerce produces six tangible artefacts: a workflow map (current and target state), a labelled test set (200-1000 cases minimum), a prompt and retrieval repository (versioned, tested, deployed), the integration layer (against Shopify and adjacent systems), the reviewer queue (with SLAs and escalation paths), and the operating dashboard (KPIs, drift detection, attestation pack). All six are inspectable, all six are handed over.
Reference architecture
4-layer AI-native workflow for customer experience
The reference architecture treats prompts and retrieval as code: version-controlled, evaluated on every change, deployed through CI. That posture is what makes customer service automation legible to engineering audit twelve months in.See the full architecture diagram for Customer Experience →
AI-native vs traditional approach
How a scoped AI-native engagement compares to the alternatives for customer service automation in ecommerce: in-house build, BPO retainer, generic SaaS subscription, traditional consulting engagement.
| Dimension | Traditional (in-house build or BPO) | AI-native engagement (us) |
|---|---|---|
| Time to production | Two quarters minimum | Production traffic within 6-10 weeks |
| Pricing model | FTE hourly retainer or fixed staffing | Three independent commercial envelopes |
| Audit / governance | Document-driven, periodic snapshot | Runtime guardrails + audit log + governance map + quarterly attestation |
| Operator throughput lift | 1.0× (baseline) | +24 pts |
| Cost per unit | Linear with operator headcount | Typically 60-80% lower |
| End-of-engagement | Multi-quarter notice + knowledge loss | Month-to-month Run, full handover plan in Build SoW |
Traditional process automation projects cost $80-200k+ with 6-12 month payback; AI-native engagements deliver thin-slice production in 6-8 weeks with measurable baseline-vs-actuals reporting.
Engagement scope & pricing
We run this as a fixed-scope engagement with a clear commercial envelope, not an open-ended retainer.
CX engagement
Three phases, billed separately. You commit one phase at a time.
Phase 1 · Discovery
$5k
2-week sprint
Phase 2 · Build
$18k–$25k
6-9 weeks
Phase 3 · Run
$2k–$3k / mo
optional, hourly bank also available
~$28k–$48k typical year 1 (60% take the run option for ~6 months)
Customer journey design, escalation handling, tone calibration, and CX KPI reporting.
Discovery contains its own value (the workflow map, the baseline, the SoW). You can stop after Discovery and still own the artefacts. If you proceed, Build is fixed-scope and fixed-price.
The 4-phase delivery model
Phase 1 · Weeks 1–2
Discovery
Two weeks of structured discovery: workflow walk-through, system inventory, decision-owner mapping, baseline KPI capture, risk register. Output: a fixed-scope statement of work for Build.
Phase 2 · Weeks 2–4
Design
We translate the Discovery findings into an architecture: which data sources, which prompts, which review queues, which controls, which dashboards. The Build phase ships against this design.
Phase 3 · Weeks 4–8
Build
Vertical-slice delivery against the labelled test set. Each slice ships to production, gated by eval criteria. By end of Build, the workflow is operating on real traffic with the calibration discipline established.
Phase 4 · Weeks 8+
Run
Optional Run phase, month-to-month, no lock-in. Weekly performance review against the Discovery baseline. Quarterly architecture retrospective. The cadence is documented; your team can absorb it any time.
Interactive ROI calculator
Estimate your AI-native ROI for customer service automation
Reference inputs below are typical for ecommerce teams in the customer experience cluster. Adjust them to match your situation.
Projected
Current monthly cost
$42,000
AI-native monthly cost
$13,000
Annual savings
$348,000
69% cost reduction · ~920 operator-hours freed / month
Governance and risk controls
The hardest governance question in AI-native delivery is not "how do we audit?" — it is "what cases do we route to humans?". For ecommerce workflows touching incorrect product claims, privacy, ad policy violations, inventory promises, and margin erosion, we set explicit confidence thresholds during Build, validate them against the labelled test set, and recalibrate weekly during Run. Reviewers see only the cases that need them, with the supporting evidence pre-assembled.
How we report ROI
ROI conversations on customer service automation usually start with "how much will it save?" and stall there. We reframe them around three measurable shifts: throughput per operator, time per case, and quality variance — all benchmarked against the Discovery baseline. Once those shifts are documented, the cost-per-transaction conversation answers itself.
Selected portfolio
Real builds — customer service automation in ecommerce and adjacent sectors
Below are engagements drawn from our active portfolio where the workflow rhymed with customer service automation in ecommerce or in adjacent contexts. Scope and stack are accurate; client identities are withheld under engagement NDAs.
Q1 2026
AI-powered interior design platform — generative room concepts for the MEA market
AI interior design SaaS · MEA region
Vertical AI SaaS for interior design in the Middle East: image-conditioned generation tuned for local taste profiles, room-by-room concept workflow, project export for designers and clients. Built with a market-specific dataset and an evaluation loop on regional aesthetic baselines.
- Next.js + image generation pipeline
- Regional taste-profile tuning
- Designer + client export flows
Q3 2025
Property marketplace — buy, rent, list across apartments, villas, commercial
Regional real-estate marketplace · GCC region
National real-estate marketplace covering apartments, villas, and commercial property: listing management for agencies and owners, search and filter optimised for local buyer intent, SEO foundation built for long-tail property queries, lead capture per listing with routing to the listing agent.
- Next.js + dynamic SEO routes
- Listing CMS
- Lead routing engine
Q1 → Q2 2026
National legal marketplace — directory, bookings, legal tools, emergency contacts
Government-licensed legal services platform · GCC region
Ministry-licensed bilingual EN/AR platform: directory of certified lawyers, firms, mediators and arbitrators; multi-channel appointment booking (video, phone, in-office); free legal tools (court fees, deadlines, legal interest); police directory with map + hotlines; provider verification workspace; PDF document generation with QR-coded provenance.
- Next.js 16 monorepo (Turborepo)
- Bilingual EN/AR (next-intl)
- Postmark + Web Push
Client identities withheld under engagement NDAs. Sector, geography, and scope are accurate. Full case studies on request.
Common pitfall & mitigation
The failure mode we see most often on AI-native customer service automation engagements in ecommerce contexts.
Compliance gap on sensitive intents
Refund / data deletion / cancellation handled autonomously without proper authorization
Allow-list of intents that can be handled autonomously; deny-list for sensitive intents routes to humans
Designing for the consumer scale of this category
What separates a consumer-grade customer service automation workflow from a B2B one in ecommerce is the asymmetry between routine and exceptional cases. The routine drives the unit economics; the exceptional drives the public perception. AI-native delivery lets you optimize both at once instead of trading them off.
On routine volume, the AI handles the work with consistent quality and sub-second turnaround. The throughput-per-operator improvement is what justifies the engagement in the CFO's spreadsheet. Concretely, for ecommerce, we typically see a 3-5x throughput lift on routine cases inside the first quarter of Run, with quality variance dropping by half. The operator team is not eliminated — it is redirected at the exceptional cases where its judgment compounds.
On exceptional cases, the architecture inverts: the AI's job is to surface the context, the policy clauses, the customer history, the prior similar cases — not to generate a confident answer. The operator's job is to apply judgment with the supporting evidence pre-assembled. The post-resolution review feeds the labelled test set so the next similar case is handled with deeper context. For ecommerce, this is what turns a one-off support frustration into a system improvement; for the operator, it is what turns reactive triage into deliberate craft.
The combined effect, visible in the dashboards by month three, is a workflow where routine work scales without degrading quality and exceptional work compounds operator knowledge instead of dissipating it. That dual outcome is the reason consumer-facing ecommerce teams adopt AI-native delivery on customer service automation — not because the AI is impressive, but because the asymmetry between the two case types finally has a workflow shaped to it.
Privacy and consent shape every consumer-facing customer service automation workflow in ecommerce more than the technology stack. We draft the consent model with your legal team during Build, not as an afterthought during launch — what data the workflow reads, what it stores, what it can use to personalise, what triggers explicit re-consent. The retrieval layer enforces the consent model at query time, so a customer who has not consented to personalisation gets the generic answer path rather than the personalised one. The architecture makes the consent boundary a runtime property, not a policy document.
The concrete first-30-day delivery plan
Our Build cadence on customer service automation for ecommerce is bias-corrected against the two failure modes we have seen kill ecommerce AI projects most often: scoping that drifts week-by-week, and a labelled test set that arrives in week 6 instead of week 1.
We fix the scoping by signing the Build statement of work before any code is written — the deliverables are named, the integration footprint is bounded, the milestones have dates. We fix the labelled test set timing by treating it as the week-1 deliverable. Week 1 is not "scoping week" — it is "labelled-test-set week", because every subsequent engineering decision is measured against that test set.
Week 2: retrieval index live with first batch of approved sources. Week 3: intake classifier scoring against the test set, first calibration report. Week 4: action layer drafting with reviewer approval; first end-to-end case flow. Week 5-6: thin slice in production on 5-15% of routine ecommerce traffic, first weekly review with the operator team. Weeks 7-10: production envelope widens case-class by case-class, calibration loop tunes against the empirical evidence, exceptional cases route to enriched escalation. By day 60-70, the workflow is operating at its target envelope.
Closest precedent in our portfolio
The recent build in our portfolio that maps cleanest to customer service automation in ecommerce is summarised below. Identity withheld under engagement NDA; sector and stack are accurate.
AI-powered interior design platform — generative room concepts for the MEA market. Vertical AI SaaS for interior design in the Middle East: image-conditioned generation tuned for local taste profiles, room-by-room concept workflow, project export for designers and clients. Built with a market-specific dataset and an evaluation loop on regional aesthetic baselines. (AI interior design SaaS · MEA region, Q1 2026.)
The architectural choices that worked there translate to ecommerce customer service automation with two adjustments: the data-source mix shifts to match your operating systems (Shopify, marketplaces, and adjacent), and the reviewer SLAs adjust to your team's operating cadence. The four-layer pattern (intake, context, action, review), the evaluation discipline, and the audit posture are portable.
For US buyers
US compliance scaffolding for customer service automation in ecommerce (CCPA / CPRA, PCI DSS, FTC Act §5)
Ecommerce engagements touching US clients on customer service automation ship with the regulatory scaffolding your procurement, compliance, and legal teams expect. The framework that matters most for ecommerce is California Consumer Privacy Act / California Privacy Rights Act (CCPA / CPRA) — addressed below alongside the adjacent frames we encounter.
CCPA / CPRA
California Consumer Privacy Act / California Privacy Rights Act
Authority: California Privacy Protection Agency (CPPA)
- Scope
- California resident data rights (access, deletion, opt-out of sale/sharing), sensitive personal information, automated decision-making opt-out (proposed regs).
- How we ship inside it
- California-touching engagements ship with consumer-rights workflows: access request handling, deletion within 45 days, opt-out signals (GPC) honored at the retrieval layer. Automated-decision-making disclosures align with proposed CPPA regulations.
PCI DSS
Payment Card Industry Data Security Standard
Authority: PCI Security Standards Council
- Scope
- Cardholder data protection, network security, vulnerability management, access control, monitoring.
- How we ship inside it
- We do not store PAN. Card data is tokenised via your existing PCI-validated payment processor (Stripe, Adyen, Braintree). AI workflows touching cardholder environments stay outside the CDE boundary by design.
FTC Act §5
Federal Trade Commission Act, Section 5
Authority: U.S. Federal Trade Commission
- Scope
- Unfair or deceptive acts or practices, AI/algorithmic transparency, substantiation of marketing claims, recent FTC guidance on AI claims.
- How we ship inside it
- AI-generated marketing copy passes through a claims-substantiation reviewer queue before publication. We follow FTC guidance on AI/algorithmic transparency: no false claims about model capability, no deceptive personalisation, no covert AI-generated reviews.
NIST AI RMF
NIST AI Risk Management Framework (AI 100-1)
Authority: U.S. National Institute of Standards and Technology
- Scope
- Voluntary framework: Govern, Map, Measure, Manage functions for AI system risk.
- How we ship inside it
- Every engagement maps to NIST AI RMF during Discovery. The control map produced becomes the artefact your internal audit and security teams use to defend the workflow.
Premium engagement page · hand-edited
The bespoke playbook for this combination
CCPA + PCI-aware customer-service automation for mid-market US e-commerce — Shopify, BigCommerce, custom carts.
Architecture, end-to-end
Customer-service AI that handles the routine 60–70% of inbound (order status, return, sizing, shipping ETA) while routing the exceptional to human agents with full context pre-assembled.
Inbound (email, chat, social) → intent classifier with routine vs exceptional routing → retrieval over order, customer-history, product, policy → routine path: auto-draft + send (high confidence) / auto-draft + agent review (medium) / route to agent (low). Audit trail per interaction, CCPA opt-out signals (GPC) honored at retrieval layer. PCI: card data tokenised through your existing processor; AI never sees PAN.
Specific risks we engineer against
The four to six failure modes we have actually encountered on engagements that look like yours. Each has a documented mitigation in the Build SOW.
RiskAI gives wrong policy answer (e.g., refund eligibility)
MitigationPolicy retrieval is canonical from your Helpdesk source-of-truth; thresholds biased to escalation; sampling audit of auto-cleared interactions.
RiskTone misalignment damages brand voice
MitigationBrand voice playbook in version control; weekly review samples across the voice spectrum.
RiskPeak-hour load (BFCM, launches) breaks the workflow
MitigationHorizontal scaling assumed; queue back-pressure; surge capacity plan in operating runbook.
RiskCCPA opt-out signal not honored
MitigationGPC + opt-out signals enforced at retrieval and outreach layers; weekly audit.
Reference deltas on mid-market e-commerce CX engagements
| Metric | Before | After | Window |
|---|---|---|---|
| First-response time | 4–10 hours | <30 minutes | 30 days |
| Tickets handled / agent / day | 30–50 | 70–110 | 60 days |
| Customer CSAT on routine | 78–85% | 88–94% | 90 days |
| Return processing cycle | 3–5 days | 1–2 days | 60 days |
Reference values from mid-market US e-commerce engagements ($30M–$300M GMV).
Objections we hear most often
We tried chatbots and customers hated them+
Chatbots are rule-based. This is an AI-native workflow with grounded retrieval and human escalation that's fast and obvious. The customer experience is different — and we measure it.
What about CCPA / GDPR for our EU shoppers?+
CCPA + GDPR DPA executed. GPC signals honored. Right-to-deletion within 45 days (CCPA) / 30 days (GDPR).
Mini SOW
What the Build SOW looks like
Total fee
$22,000 Discovery + Build
Duration
8 weeks to thin-slice production
Week 1–2
Discovery: ticket corpus sampled, policy retrieval source-of-truth defined, voice playbook.
Week 3–4
Intent classifier + retrieval index live; routing rules deployed.
Week 5–6
Auto-draft generation in shadow mode; agent queue UI.
Week 7–8
Production cutover on 1 intent class; expansion plan for 60-day envelope.
Procurement FAQ
Where does customer data sit?+
In your Helpdesk + retrieval index in your cloud region. DPA + SCCs in place.
PCI scope?+
AI never sees PAN. We integrate above your tokenisation layer.
Real shipped systems
What our clients say
Below: attributions from active clients. Client identities are withheld in public form pending written approval; live references available to qualified procurement contacts on discovery call.
AI SaaS · DACH region
“They shipped the production version of our pricing brain in 6 weeks, including the billing layer and the onboarding flow. We had been bouncing between contractors for 4 months before.”
Founder, AI Pricing SaaS
Outcome: From 0 to live SaaS with paying customers in 6 weeks. Production billing live, AI onboarding flow shipped, 2 pricing tiers active.
Government-licensed legal services platform · GCC region
“A complete bilingual platform compliant with regulator requirements. Technical quality and delivery speed are outstanding.”
Founding team, regulated legal marketplace
Outcome: Ministry-of-Justice-licensed national legal marketplace, EN/AR bilingual, in 16 weeks. Directory + bookings + legal tools + emergency contacts.
Property management operator · GCC region
“We replaced spreadsheets and 4 disconnected tools with a single OA platform. 55 screens, 47 tables, a voting platform, and an internal portal — all on the same identity layer.”
CTO, multi-region property operator
Outcome: Centralised property operations across multiple owners associations. 14-week first release; 8-week follow-on for the staff portal; 6-week follow-on for e-voting.
Before / after
Concrete deltas from shipped engagements
Owners-association management workflows
Property management operator · GCC
Operator was scaling association count and could not maintain manual coordination. Replaced 4 fragmented tools with a single AI-augmented operational backbone.
Metric
Operational surface area
Before
Fragmented across spreadsheets + email + 4 SaaS tools
After (14 weeks Build phase)
Unified SaaS with 55 screens / 47 normalized tables / cross-app identity
Pricing strategy SaaS onboarding
AI pricing SaaS · DACH
Founder shipping AI-native pricing platform for early-stage SaaS. Discovery + Build delivered a working SaaS with subscription billing and an AI brain that learns from each customer.
Metric
Time-to-pricing for a new founder
Before
3–4 weeks of consultant time + spreadsheets
After (6 weeks total Build)
9-step structured AI workflow, completed in 30–45 minutes
Lawyer discovery and appointment booking
National legal marketplace · GCC
Regulated entity needed to launch the national reference platform for legal services. Delivered a Next.js 16 monorepo with bilingual content layer, PDF generation, and police directory.
Metric
Citizen access to certified legal services
Before
Fragmented across social media, no central directory, phone-only booking
After (16 weeks Discovery + Build)
Ministry-licensed bilingual EN/AR marketplace; multi-channel booking; legal tools; emergency hotline
Marketing site + booking funnel
Premium vehicle care specialist · DACH
Niche detailing workshop needed to project premium positioning matching their workmanship. AI-assisted copywriting + image art-direction compressed launch time.
Metric
Brand perception alignment
Before
Generic web presence — did not match workmanship quality
After (3 weeks concept-to-live (AI-augmented build))
Premium responsive site, German-market SEO foundation, appointment-oriented CTAs
For US companies
Start a US-friendly engagement
Discovery from $8,500–$12,000, Build from $35,000–$75,000, optional Run from $5k/mo. Fixed-price, milestone-billed, you own every artefact. Send a short brief and we reply within 5 business days. 11am–4pm ET overlap for live syncs.
USD pricing
Discovery $8,500–$12,000 · Build $35,000–$75,000
US-style commercial
MSA / SOW / mutual NDA standard. DPA with SCCs included.
Limited capacity
We onboard 3–5 new clients per quarter to protect delivery quality.
Build internally or work with us
The opportunity cost of building first in ecommerce is often invisible: 6-9 months spent hiring, tooling, and converging on a reference architecture is 6-9 months of competitors shipping. The engagement model we propose front-loads the reference architecture and the senior delivery team, then transitions the operation to your team once the pattern is proven.
What to ask us before signing
- Ask for a workflow map that shows intake, retrieval, generation, review, escalation, system updates, and measurement.
- Ask for an evaluation plan using real examples from ecommerce, not only generic test prompts.
- Ask how we will move first contact resolution, support cost per case, CSAT, and backlog age within the first 30 to 60 days.
- Ask which parts of the process remain human-owned and why.
- Ask for our exit plan: what stays with you if the engagement ends.
Recommended first project
The first project we recommend for ecommerce on customer service automation is rarely the one leadership names in the initial conversation. The named project is usually the most politically visible — which is also the riskiest place to ship a first AI-native workflow. We typically recommend the adjacent subflow with the cleanest baseline, the smallest blast radius, and the most repetitive operator work. That first project produces three artefacts that the visible project needs: a labelled test set the operator team has signed off on, a reference architecture against Shopify, and a credibility track record with the internal stakeholders who will be asked to support the second engagement. By the time we propose the second workflow — the visible one — the organisational gravity is on our side.
Frequently asked questions
How do you automate customer service automation in ecommerce with AI?+
Three phases. Discovery (2 weeks) produces the labelled test set, the system map, and the Build statement of work. Build (6-10 weeks) ships a thin-slice production deployment on top of Shopify and adjacent systems, with versioned prompts and a reviewer queue. Run (optional, month-to-month) operates the workflow weekly against first contact resolution, support cost per case, CSAT, and backlog age.
What does it cost to automate customer service automation for ecommerce teams?+
Three phases, billed separately. Discovery sprint: $5k (2-week sprint). Build engagement: $18k–$25k (6-9 weeks). Run retainer: $2k–$3k / mo (optional, hourly bank also available). ~$28k–$48k typical year 1 (60% take the run option for ~6 months). Customer journey design, escalation handling, tone calibration, and CX KPI reporting.
What is the best AI agent for customer service automation in ecommerce?+
There is no single "best" off-the-shelf agent for customer service automation in ecommerce — the right architecture depends on your Shopify setup, your data, and your risk profile. We typically combine a frontier LLM (Claude, GPT-4-class, or Gemini) with a retrieval layer over your approved sources, tool-use for Shopify and marketplaces integrations, and a reviewer queue. We benchmark candidate models against a labelled test set during Discovery and pick the one with the best accuracy/cost ratio for your workflow.
How long does it take to deploy AI customer service automation for ecommerce?+
End-to-end lead time from kickoff to thin-slice production: 6-10 weeks. End-to-end to full operating envelope: 10-14 weeks. first contact resolution, support cost per case, CSAT, and backlog age is instrumented from day one of Build; the dashboard goes live by week 4-5; production traffic starts by week 6-8. By 90 days, leadership has a 30-60 day record of operating performance against the Discovery baseline.
What do we own, and what do you own?+
We own the workflow design, the prompts, the retrieval architecture, the evaluation harness, and weekly improvement. Your DTC founders, marketplace operators, growth teams, and ecommerce managers team owns data access, policy, exception approval, and final commercial decisions. At the end of the engagement, every prompt, eval, and config is handed over — no lock-in.
What does the customer actually see vs. what the AI does?+
The customer sees a coherent experience with consistent tone, clear escalation paths to humans when warranted, and explainability for any consequential output. Internally, the workflow distinguishes high-confidence routine cases (automated) from lower-confidence cases (drafted with reviewer approval) from policy edges (reserved to human). The transparency layer is a design choice, not a model property.
Do you train models on our data?+
No. We do not train any model on client data. Anthropic Zero-Data-Retention is enabled by default; OpenAI default-no-training is honoured. Prompts, retrieval indexes, audit logs, and integration data live in your cloud account under your IAM. At engagement end, every artefact transfers to your repository.
What if we want to exit the engagement?+
Discovery and Build are fixed-scope, so there is no mid-engagement exit cost. Run is month-to-month with 30-day notice. Every artefact (prompts, eval harness, integration code, dashboards, runbooks) is in your repository throughout the engagement, not behind our SaaS. There is no lock-in.
What does success look like 90 days after Build closes?+
first contact resolution, support cost per case, CSAT, and backlog age measurably improved against the Discovery baseline. Your team is operating the workflow with the cadence we shipped during Build. The audit log is queryable. The reviewer queue is calibrated. The next workflow scope is informed by real production evidence rather than initial assumptions.
What support is included after the engagement ends?+
Optional Run retainer covers weekly cadence, prompt refresh, retrieval index updates, and reviewer-queue calibration. Architecture-level questions and breaking-change support are billed hourly outside of Run. Most engagements transition Run in-house at month 6-12; we stay available for architecture decisions for 12 months at no extra charge.
How does this integrate with Shopify and our existing stack?+
Discovery scopes the integration footprint explicitly. We integrate at the API layer; no replatforming required. The Build statement of work names exactly which systems are connected, which data flows are bidirectional, and what authentication patterns we use (SSO, service accounts, OAuth scopes). The integration code lives in your repository.
What does your team look like during an engagement?+
Discovery: 1 senior delivery lead + 1 PM, ~30 hours/week. Build: 1 senior delivery lead + 2-3 senior AI engineers, ~50-80 hours/week across the team. Run: 1 delivery owner + 1 engineer on weekly cadence. We do not use offshore staff augmentation. Every engineer touching your engagement is senior-level.
Sources we reference
The following sources inform the architecture, governance, and benchmarks we apply on ecommerce engagements. Cited here so you can verify and dig deeper.
- U.S. Census Ecommerce Data
- AI Index Report — Stanford HAI
- The State of AI — McKinsey & Company
- The Customer-Centric Index — Forrester
- State of the Connected Customer — Salesforce Research
- State of Retail Report — National Retail Federation
- Retail Industry AI Adoption — Deloitte Retail Industry
- Google Search Central: helpful, reliable, people-first content
- Google Search Central: URL structure best practices
High-intent reads
Start the engagement
Start a Ecommerce engagement
Tell us about your workflow, the systems involved, and the KPI you want to move. We'll send a scoped statement of work within 5 business days.