We build e-commerce AI that handles real merchant complexity
Your AI feature works on test accounts. Then real merchants connect their stores. Shopify instances with 50,000 SKUs. WooCommerce setups with plugins that break standard APIs. Order histories spanning three platform migrations. We build e-commerce AI that handles this reality.
Building AI for e-commerce platforms is harder than it looks.
Demo-to-production gap?
Sample stores work perfectly. Real merchant data breaks everything – massive catalogs, custom fields, abandoned integrations nobody documented.
Merchant data chaos?
One store has 500 SKUs with clean metadata. Another has 50,000 products with descriptions copy-pasted from suppliers. Your AI needs to handle both.
Platform API complexity?
Shopify rate limits during Black Friday. WooCommerce webhooks out of order. BigCommerce pagination failing. Each platform has production quirks.
Multi-tenant scaling?
AI that works for one merchant fails for another. Different catalog sizes, data quality, edge cases. What works for 10 merchants breaks at 1,000.
User expectations vs reality?
Merchants don't care about AI limitations. They connected their store and expect magic. Explaining why it failed doesn't save the relationship.
Global compliance requirements?
Your merchants sell globally. GDPR in Europe, CCPA in California, PCI DSS for payments. Your AI touches customer data from all of them.
We've shipped AI features for e-commerce platforms handling millions of merchant requests – customer support automation, product intelligence, and order management systems.
What we bring to e-commerce AI products:
Testing against real merchant chaos
Massive catalogs, legacy data, custom configurations. We test AI against stores your merchants actually run – not sanitized sample accounts.
Architecture built for merchant diversity
AI that adapts to each merchant's data quality and catalog structure. Isolation, performance, and reliability whether they have 500 products or 500,000.
Platform integrations that survive traffic spikes
Shopify, WooCommerce, BigCommerce, Magento – integrations that handle rate limits, webhook chaos, and API inconsistencies at scale.
Graceful handling when AI hits its limits
Clear confidence signals, intelligent fallbacks, and escalation paths. Your users get value even when the AI can't fully solve their problem.
Observability across your entire merchant base
See exactly how AI performs across all tenants. Catch degraded performance, identify patterns, debug issues before they become support tickets.
Compliance infrastructure from the start
Data handling, consent tracking, and audit trails for global e-commerce. Not retrofitted when enterprise prospects ask about SOC 2.
Most teams ship AI features that impress in demos. We build AI that keeps working when thousands of merchants depend on it daily. Get the AI Launch Plan or schedule a call to discuss your product.
Complete e-commerce AI engineering capabilities.
E-Commerce Conversational AI
Support Automation
AI agents for order inquiries, returns, and product questions. Multi-tenant architecture at scale.
Shopping Assistants
Chat and voice agents that help shoppers find products, check availability, and complete purchases.
Product Q&A
Intelligent recommendations and availability checks. Accurate answers from messy data.
E-Commerce Data & Operations AI
Order Intelligence
Routing optimization, fraud signals, and fulfillment prioritization. AI that handles messy merchant operations.
Inventory Systems
Demand forecasting, restock alerts, and stock optimization. Accurate predictions with limited historical data.
Document Processing
Invoice extraction, PO matching, and returns processing. Automate merchant paperwork at scale.
E-Commerce AI Infrastructure
Platform Connectors
Shopify, WooCommerce, BigCommerce, Magento – plus ERPs and shipping providers.
Tenant Analytics
AI performance monitoring across your merchant base. Catch issues before they cause churn.
Compliance Monitoring
GDPR, CCPA, PCI DSS, SOC 2 readiness. Enterprise-grade data handling for global merchants.
Full-Stack AI Integration
Connect AI capabilities to your existing platform, APIs, databases, and workflows. Complete system integration and custom development.
B2B SaaS Platform Development
Complete platform development – interfaces, APIs, databases, authentication, integrations, billing, and other foundational features.
Infrastructure & Deployment
Production deployment, monitoring, scaling, CI/CD pipelines, and security implementation for AI systems and SaaS platforms.
"We've built our entire B2B SaaS platform together, and I genuinely can't imagine working with anyone else"
— Ryan Tabb, Ex-Founder, Bullseye (Exited)
"Their transparency about AI capabilities has been crucial for making informed strategic decisions about our product."
— Kevin M.A. Nguyen, Co-Founder, Proximo AI
Here's how we work together:
Intro Call
We dig into your goals and challenges and figure out if we're the right fit. No sales pitch, but a honest conversation about what needs to happen.
"Softcery's approach is exceptionally thoughtful - they consider the complete business context, not just the immediate technical requirements."
— Kevin M.A. Nguyen, Co-Founder @ Proximo AI
Shaping Phase
We define exactly what gets built, document edge cases, and lock in timeline + budget. This is where we prevent the "6 months later..." nightmare.
"Softcery treated our project with the same care we would - validating every assumption and researching every angle before building."
— Charley Cohen, Director @ TIC.uk, Founder @ Ticitz
Build Phase
We deploy to production in the first weeks and iterate from there. Weekly updates, working functionality you can see and test immediately as we build.
"Softcery combines deep product strategy with technical execution - they don't just follow instructions, they challenge your approach and find better solutions."
— Ryan Tabb, Ex-Founder, Bullseye (Exited)
Launch & Scaling
We finalize the production system, document everything properly, and either hand off cleanly or stick around as your ongoing AI partner. Your choice.
"Softcery was fantastic throughout our re-build of a recently acquired B2B SaaS. Highly recommend in you are in need of high quality AI and SaaS engineering."
— Chris Riley, Acme Studio (Cuppa AI, Bullseye, Experts Ink)
Ready to ship AI features your merchants can depend on?
The AI Launch Plan covers how we approach e-commerce AI – from handling platform complexity to scaling across thousands of merchants. Or schedule an intro call to discuss your specific product.
The Founder's Guide to AI Engineering
In-depth coverage of AI engineering for B2B SaaS founders. Analysis, technical breakdowns, and implementation guides from the field.
Agentic Systems
AI Agents Break the Same Six Ways. Here's How to Catch Them Early.
Works in staging. Fails for users. Six architectural patterns explain the gap, and all of them show warning signs you can catch early.
You Can't Fix What You Can't See: Production AI Agent Observability Guide
Failures you can't reproduce. Error logs that tell you nothing. Three observability pillars solve this: tracing, monitoring, and evaluation.
The AI Agent Prompt Engineering Trap: Diminishing Returns and Real Solutions
Founders burn weeks tweaking prompts when the real breakthrough requires a few hours of architectural work.
Choosing LLMs for AI Agents: Cost, Latency, Intelligence Tradeoffs
Demos work. Production reveals $47 conversations, 2-second pauses, unpredictable failures. Three dimensions help choose.
We Tested 14 AI Agent Frameworks. Here's How to Choose.
Your use case determines the framework. RAG, multi-agent, enterprise, or prototype? Here's how to match.
Voice Agents
How to Choose STT and TTS for Voice Agents: Latency, Accuracy, Cost
Every provider claims low latency and high accuracy. Real differences show up in production. Here's what actually matters.
Real-Time (S2S) vs Cascading (STT/TTS) Voice Agent Architecture
Both architectures work in demos. Different problems emerge in production. Here's what determines the right choice.
Choosing an LLM for Voice Agents: Speed, Accuracy, Cost
Fast models miss edge cases. Accurate models add 2 seconds. Cheap models can't handle complexity. Here's how to choose.
Why Voice Agents Sound Great in Demos but Fail in Production
Understanding why AI voice agents break down is the first step to building a solution that actually works in real life.
Testing Voice Agents: Methods, Metrics, and Tools
Controlled tests pass every time. Real users break agents with accents, noise, and bad networks. Here's what to test for.
Featured
Agentic Coding with Claude Code and Cursor: Context, Memory, Workflows
Agents go in circles without project context. The same agent ships production code daily with proper structure. Here's the system.
8 AI Observability Platforms Compared: Phoenix, Helicone, Langfuse, & More
AI agents fail randomly. Costs spike without warning. Debug logs show nothing useful. Eight platforms solve this differently.
US Voice AI Regulations: TCPA, BIPA, COPPA, HIPAA, & State Privacy Laws
Legal compliance sounds expensive and complex. Most voice AI startups need eight laws and a 5-step framework to ship safely.
11 Voice Agent Platforms Compared: Vapi, Ultravox, Retell, & More
Platforms promise easy setup. Production reveals control limits, concurrency caps, and cost scaling. Match your constraints before choosing.
SOC 2 for Voice AI Agents: Security, Confidentiality, and Quick Wins
Enterprise deals stall without SOC 2. Formal audits cost months and $50k+. Eight steps align your startup now before compliance blocks revenue.