Storise

Services · AI for commerce

AI where it makes
the business measurably better.

  • Agents & tool use
  • RAG & knowledge
  • Semantic search
  • Privacy-first

We implement AI where it creates real value — not where it makes a nice press release. Customer service agents, semantic search and recommendations, RAG pipelines, content generation at scale, and process automation — everything tied to measurable cost and revenue impact, and deployable on your own infrastructure when that matters.

Day oneAI considered from the first sprint, not bolted on later
MeasuredEvery feature tied to a cost or revenue KPI, with evals
On-premSelf-hosted models and gateways when data control matters
TypeScriptSame engineering standard as the rest of the stack

What we build

AI that earns its place in production.

Most AI in e-commerce is a demo that never ships, or a feature that looks smart but moves no numbers. We build the AI that pays for itself — evaluated, observable, and shipped under the same engineering discipline as the rest of your product.

01

AI agents for business processes

Agentic workflows for customer service, sales support, and back-office operations. Tool use, guardrails, human handoff, and a proper evaluation harness — built to ship, not just to demo.

02

AI-powered search and recommendations

Semantic search, hybrid retrieval, and product recommendations that understand intent, not just keywords. Measurable lift on conversion, AOV, and search exit rate — with evals that catch regressions.

03

Content generation at scale

Product descriptions, category copy, SEO content, and translations generated with brand-consistent prompts, review workflows, and automated quality gates — not copy-paste into ChatGPT.

04

RAG pipelines & knowledge bases

Retrieval-augmented pipelines over product catalogs, documentation, and internal knowledge. Versioned, observable, and accurate — with evals that block bad retrieval before it hits production.

05

Process automation

AI embedded in real operational workflows: order triage, merchandising, customer ops, reporting. Automation that removes manual work — instead of creating a new queue of AI output a human still has to review.

06

Privacy-first, on-premise

Self-hosted model gateways, local inference with open models (Llama, Qwen, Mistral), per-tenant isolation, and full data control. For regulated industries and sensitive data — not as an afterthought.

Deep-dive · AI that sells vs AI that runs the business

Two places AI pays off, done properly.

AI shows up in two distinct places in a commerce business: the customer-facing surface that drives revenue, and the internal operations layer that removes cost. We build both as first-class systems — evaluated, observable, and owned by your team.

01AI that sells

Customer-facing intelligence, measured in revenue.

Semantic search, recommendations, personalization, and customer service agents that show up where customers are — product pages, search, support, checkout. Every surface evaluated on business metrics: conversion, AOV, CSAT, and resolution rate — not on vibe-based judgement of an output.

  • Semantic and hybrid search with typo and intent handling
  • Product recommendations: complementary, similar, personalized
  • Customer service agents with tool use and human handoff
  • AI-powered merchandising and automated collection curation
  • Content generation tied into CMS, PIM, and translation workflows
  • Evaluation harness tied to revenue, CSAT, and resolution metrics
02AI that runs the business

Internal operations, without the review-queue tax.

RAG over internal knowledge, agents for operations and back-office work, content generation at scale, and process automation that actually removes steps — instead of creating a new queue of AI-generated work a human still has to review line by line.

  • RAG over docs, SOPs, products, and internal knowledge
  • Internal knowledge bases with versioning and access control
  • Order triage, merchandising, and ops agents
  • Reporting and analytics generation from natural language
  • Self-hosted model gateways for data-sensitive workloads
  • Per-tenant isolation and privacy-first architecture

How we engage

Three ways to work with us.

01

AI audit & roadmap

Find the value, then build.

A focused, time-boxed engagement to find where AI actually pays off in your business. We map the workflows, data, and realistic ROI, and hand back a prioritized roadmap with concrete costs — not a generic list of AI trends.

02

Focused AI build

One feature, shipped properly.

A defined project to ship one AI feature end-to-end: a customer service agent, semantic search, a RAG pipeline, or an internal automation. Evaluation harness, monitoring, and documentation included — not a proof of concept that quietly rots in staging.

03

Retainer & continuous AI work

Senior AI team, on demand.

A long-term partnership for teams treating AI as infrastructure. Prioritized backlog of features, evals, model upgrades, and cost optimization — with monthly reporting tied to business metrics, not token counts.

Process

From diagnosis to long-term AI capability.

Every engagement follows the same backbone — adapted to scope and stage, never skipped to meet a deadline.

  1. 01

    Diagnosis

    A paid, time-boxed audit of your workflows, data, and commercial goals. Output: where AI can realistically pay off, where it cannot, and a prioritized roadmap with real costs — not a slide deck.

  2. 02

    Architecture

    We lock the model strategy, data flows, and guardrails: which models, self-hosted vs API, retrieval strategy, evaluation harness, fallback logic. Every choice documented and justified.

  3. 03

    Build

    Short iterations, preview environments, evals running on every change, and observability from week one. AI features shipped with the same discipline as the rest of the product.

  4. 04

    Launch

    A rehearsed rollout with feature flags, shadow mode, and real monitoring. AI that touches customers or revenue is launched cautiously on purpose, with a clear rollback path.

  5. 05

    Growth

    After launch we keep improving — evals, prompt and retrieval tuning, model upgrades, cost optimization. AI is infrastructure, not a one-off launch.

Technology

A pragmatic AI stack.

We pick tools with strong observability, clean upgrade paths, and real evaluation support — not whatever was trending on Hacker News last week.

Models & providers

  • OpenAI
  • Anthropic
  • Google Gemini
  • Mistral
  • Llama
  • Qwen

RAG & retrieval

  • pgvector
  • Pinecone
  • Qdrant
  • Weaviate
  • LangChain
  • LlamaIndex

Agents & orchestration

  • Vercel AI SDK
  • LangGraph
  • Mastra
  • Inngest
  • Temporal
  • Workers AI

Evals & observability

  • LangSmith
  • Braintrust
  • Helicone
  • Langfuse
  • Arize
  • Custom evals

Ready to ship AI that actually earns its keep?

Tell us what you are working on — a support agent that cannot handle edge cases, a search that still misses intent, a RAG pipeline that hallucinates, or an AI roadmap that never leaves the slide deck. We will come back with a concrete next step, not a sales pitch.

  • We reply within 1 business day.
  • No pitch calls without context first.
  • Discovery call is 30 minutes — free.