E-commerceExpertise
We build storefronts, checkout flows, and AI-driven support and personalization engineered for real traffic and a real catalog, not just a clean demo.
Trusted by engineering teams across 15+ countries
From startups to Fortune 500 companies
Production Reality
Where SaaS AI Projects Fail
Only practitioners who've shipped real systems handling live tenant accounts can flag these accurately.
Multi-tenant data leakage through agent context
We architect strict tenant isolation into retrieval and agent memory from day one, not bolted on after a security review.
Support agents that don't keep up with your release cadence
We build retrieval pipelines that sync with your docs and changelog automatically, not a one-time training snapshot.
Usage-based billing automation that miscounts
We build idempotent event processing and reconciliation checks into every billing pipeline.
No audit trail for automated account actions
We build structured logging and audit trails into every automated account action.
Is this you?
Three situationsthat bring teams to us
If any of these sound familiar, we've solved this in production, not just in theory.
Your support team is answering the same predictable questions by hand, every day.
You have catalog and order data that could personalize or automate decisions, but it's still manual.
You're adding AI to your storefront or support flow and hitting reliability issues once real customers hit it.
Delivery Structure
POC to Production Process
Discovery
POC Build
Production Architecture
Deploy & Monitor
Technical Stack
Models & Frameworks
Preferred for deterministic business workflow agents where state management and branching logic matter.
Multi-agent conversations where agents critique and revise each other's outputs.
Role-based agent teams with defined task sequences and human-in-the-loop checkpoints.
Managed vector store for production workloads that need fast ANN search at scale.
Hybrid search combining BM25 + vector, when keyword precision matters alongside semantic similarity.
Local and dev-stage retrieval for rapid prototyping before committing to a managed store.
GPT-4o for tool-use tasks requiring fast structured output and broad API ecosystem compatibility.
Claude for long-context tasks, instruction following, and workflows where refusal behavior needs to be controlled.
Multimodal inputs — document, image, and video understanding in a single model call.
REST and GraphQL integrations so agents can read and write to your existing systems without rebuilding them.
Event-driven triggers that let agents respond to external signals in real time.
Direct database read access for agents that need structured data alongside unstructured retrieval.
The Team
Engineers Behind the Work
Discipline-specific AI engineers not generalists. Each has shipped real systems in production.
Builds offline-first sync engines that resolve data conflicts seamlessly when connectivity returns.
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Leads architectural decisions under ambiguous requirements that hold up under years of scale.
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Bridges legacy server-rendered PHP backends with modern Angular SPAs through clean API layers.
https://www.inapps.net/contact-usCommon Questions
Everything you needto know
Which industries do you specialize in?
We have deep expertise in Fintech, Healthcare, Logistics, and E-commerce. Each practice area is staffed with specialists who understand the regulatory requirements, architecture patterns, and competitive landscape of that industry, so your team isn't educating ours from scratch.
Not sure if E-commerce AI is right for your use case?We'll tell you in 30 minutes.
No pitch. No obligation. Just an honest answer about whether we can help.