Industry · Retail & E-commerce
Retail AI that touches the order, not just the chat.
A bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.
- AI Agents
- Growth Marketing AI
- Generative AI & RAG
- Prototype → Production
Why now
Acquisition costs keep climbing while support volume scales linearly with orders. The two useful places for AI in retail are the ones with a direct margin line: deflecting the repetitive half of the contact queue, and making paid and lifecycle spend accountable per SKU rather than per campaign.
The problem
Where e-commerce teams lose time today.
- 01
'Where is my order' is most of the queue
The answer exists in the carrier's API. Getting it to the customer takes a human opening two tabs, several hundred times a day.
- 02
Search doesn't understand the catalogue
Keyword search fails on the way customers actually describe products, and the resulting zero-result pages are silent lost revenue.
- 03
Marketing reports impressions, finance asks about margin
Channel dashboards disagree with the ledger, so nobody can say which campaign made money after returns and shipping.
Constraints
What makes this harder than a generic chatbot.
- 01
The catalogue is the ground truth
Answers about price, stock and fit come from your product data at query time, never from what the model remembers about a product from training. Out-of-stock claims are a refund conversation later.
- 02
Peak traffic is a design input
Sale-day load is where naive AI support falls over and where costs spike hardest. We put rate limits, caching and per-conversation cost ceilings in before launch, not after the first invoice.
- 03
Returns policy is policy, not vibes
Eligibility is evaluated by your rules engine, with the agent explaining the outcome. A model improvising goodwill on returns is an unbudgeted liability.
Integrations
Systems we wire into.
- Shopify & WooCommerce
- Gorgias, Zendesk & Intercom
- Klaviyo
- Stripe & Razorpay
- Shiprocket, Delhivery & carrier APIs
- Algolia / Typesense
- GA4 & server-side tagging
- Meta & Google Ads APIs
Services
What we build for e-commerce.
- AI customer service agents for e-commerceSupport agents connected to orders, carriers and your returns rules — resolving tracking, exchanges and address changes end to end on Gorgias, Zendesk or Intercom.Read
- AI growth marketing for e-commerceServer-side tracking, margin-aware attribution and AI-assisted creative and lifecycle production — reported per SKU after returns and shipping.Read
- AI product search and catalogue RAGSemantic search and grounded product Q&A over your catalogue, reviews and size guides — with stock and price read live rather than remembered.Read
- E-commerce prototype to productionLoad, payments, inventory correctness and observability added to a storefront or marketplace prototype before real traffic finds the cracks.Read
How an engagement is judged
- A scope small enough to finish, agreed in writing in week one
- An eval suite built from your own data, versioned in your repo
- Weekly numbers on the metrics we agreed, including the bad ones
- Code, IP and repo yours from day one — no proprietary runtime of ours
We publish no per-industry result metrics because almost all client work is under NDA and we won't quote numbers we can't evidence. What we can show is the process and the in-house work on /work, which is public and running.
Pricing ladder
Three ways to start.
Sprint validates · Build productionizes · Retainer scales. The Sprint fee credits toward Build.
Step 1 · Validate
30-day Sprint
Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.
$4,500 fixed
Learn more- Most teams land here
Step 2 · Build
Prototype → Production
Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.
from $6,000
Learn more Step 3 · Scale
Managed Retainer
Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.
from $750/mo
Learn more
FAQ
AI in e-commerce: the questions we get.
Can the agent process a return without a human?
Within the rules you define — window, category, condition, customer history — yes, and it should, because that's where the volume is. Anything outside the rules is drafted for a human with the eligibility reasoning attached.
What does this cost to run at our order volume?
Inference typically lands between $0.05 and $0.50 per handled conversation depending on how much context each turn needs. We put a hard per-conversation ceiling in the code so a retry loop can't produce a surprise bill.
Will it speak our brand voice?
It'll be trained on your best historical replies rather than a tone-of-voice PDF, and the eval set includes voice as a graded dimension — otherwise the first model upgrade quietly rewrites your brand.
Contact
Talk to us about AI in e-commerce.
Tell us where you are now and where you want to be. We reply within one business day.
Or skip the form — book a Calendly slot directlyadmin@neuroxai.com · +91 70149 99768
Remote-first team across India · US · EU · HQ in Udaipur, India
Other industries
Similar constraints, different vertical.
- AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
- AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
- AI for SaaSThe model is the easy part. Multi-tenancy, per-seat cost, evals and a permissions model that doesn't leak across tenants are what turn a demo into something you can charge for.Read