Products with real intelligence

AI Agents. Copilots. Semantic Search. Intelligent Features.

Not demos — products in production that generate revenue.

AI Agents Copilots Semantic search Personalization Data Products B2C · B2B · D2C

The Problem

85% of AI projects never reach production.

It's not a technology problem. It's an execution problem. Companies invest in proofs of concept that never scale, integrate generic tools that don't understand their business, or try to build internally without the necessary expertise.

Source: Gartner

01

The eternal PoC. Your team has spent months on a demo that works in a controlled environment. Taking it to production with real data, real volume and real users is another story. The gap between prototype and product is where most AI initiatives die.

02

The generic tool. You bought an AI SaaS that promised out-of-the-box personalization. Six months later the results are marginal because it doesn't understand your catalog, your customer or your operation. Generic AI produces generic results.

03

The team you don't have. You want to build AI products but your team has no experience with models, embeddings, RAG or fine-tuning. Hiring that talent takes months. Meanwhile, your competition is already in production.

We don't sell the future. We build products that run today.

Who it's for

Built for teams that need results, not another vendor deck.

CTO

CTO / VP Engineering

Technical team

That speaks my technical language

I need a team that understands the difference between an API wrapper and a real AI product. That integrates with my current stack without having to rewrite everything.

CPO

CPO / Product Manager

Product-market fit

Validate fast before investing

I need to know if this AI feature has product-market fit before investing 6 months in development. And I need someone who understands both the AI and the end user.

CEO

CEO / Founder

Impact on metrics

That translates into metrics

I need this AI investment to translate into more conversions, better retention and a more efficient operation. Not into pretty papers about the potential of AI.

We work with B2B, B2C and D2C companies that already have digital operations and want to use AI to create real competitive advantages. We don't sell the future: we build products that run today.

What we build

Four product categories. One goal: measurable impact on your business.

01

AI Agents & Copilots

Autonomous agents and copilots that execute complex tasks across your commercial operation. Not chatbots with canned answers: agents that understand your catalog, your inventory and your business rules.

  • Conversational sales agent that negotiates B2B pricing with predefined business rules
  • Internal copilot for customer service that suggests answers based on customer history
  • Replenishment agent that analyzes purchase patterns and generates orders automatically
  • Merchandising assistant that optimizes product layout by channel and season
02

AI Search & Discovery

Semantic search engines and discovery systems that understand user intent, not just keywords. Real personalization based on behavior, context and preferences.

  • Semantic search that interprets "something to gift my mom who loves cooking"
  • Recommendation engine that personalizes by segment, history and browsing context
  • Content personalization that adapts landing pages, banners and offers in real time
  • Visual search: the user searches with a photo and finds similar products
03

AI-Powered Features

Intelligent features that integrate into your existing product or platform. No need to rewrite your stack: they connect through an API and add a layer of intelligence to what you already have.

  • Dynamic pricing that adjusts prices by demand, competition, inventory and elasticity
  • Automatic generation of product descriptions optimized for SEO and conversion
  • Fraud detection on transactions with models trained on your historical data
  • Demand and inventory forecasting with ML that learns from your seasonal patterns
04

AI Data Products

Data products that turn your information into business assets. ML pipelines, intelligent dashboards and analytical systems that don't just show what happened, but what to do about it.

  • Customer Intelligence Platform that unifies touchpoints and predicts purchase behavior
  • Catalog enrichment pipeline that classifies, tags and completes attributes with AI
  • Prescriptive analytics dashboard that recommends specific actions based on data
  • Intelligent knowledge base that learns from internal docs and answers the team

How we work

Building is half the work. The other half is operating, measuring and improving.

Every AI product we build follows a two-phase model. A model in production without monitoring, without retraining and without continuous optimization is a model that degrades. And a degraded model is worse than no model.

Phase 01Project

AI Product Build

Per-project investment based on scope, model complexity and available data

6–12 weeksProject with phases2-week sprints

From discovery to go-live. We design, build and launch your AI product in production with real data.

  • Discovery: AI Opportunity Map + Data Readiness Assessment
  • Design: architecture, model selection and success metrics
  • Build: development in 2-week sprints with a working demo
  • Continuous integration with your existing stack
  • Launch: deploy with monitoring, A/B testing and gradual rollout
  • MVP in production + technical documentation + runbook
Start a project→
Phase 02Continuous

AI Product Operate

Custom monthly contract based on services, inference volume and SLA

Monthly contractManaged AIContinuous improvement

We keep your AI product at the frontier: monitored, optimized and evolving with every new data point.

  • Monitoring of performance, model drift and response quality
  • Continuous tracking of business metrics and KPIs
  • Model retraining and prompt tuning
  • Continuous A/B testing and inference cost optimization
  • New features and expansion to new use cases
  • Migration to more efficient models as they evolve
Operate my product→

Our live demo

We don't ask you to take our word for it. Try what we build.

Ask AI is the chatbot running on this site. It's not a product we sell: it's a working demonstration of what we build. It uses RAG over our knowledge base, understands conversational context and is connected to our services.

It's the same kind of architecture we implement for clients: language models + proprietary data + business rules + integration with existing systems. The difference is that this one you can try right now.

Tech stack

Agnostic by conviction. The best model for your case, not the one that pays us the best commission.

We work with the leading AI model and infrastructure providers. We're not married to any of them because your use case determines the technology, not the other way around. We evaluate cost, latency, quality and privacy for every project.

AI Models

  • OpenAI (GPT-4o, o3)
  • Anthropic (Claude)
  • Llama
  • Mistral
  • Specialized embeddings

Cloud Infrastructure

  • AWS (SageMaker, Bedrock)
  • Azure (OpenAI, ML)
  • GCP (Vertex AI)
  • Vercel AI SDK
  • MongoDB Atlas + Vector

Commerce Platforms

  • Shopify
  • commercetools
  • BigCommerce
  • VTEX

The process

From idea to production in 6–12 weeks. No surprises.

Every project follows a structured process that minimizes risk and maximizes speed. We don't start writing code without understanding your business. And we don't deliver without measuring results.

Week 1–2 — Discovery

We map where AI generates value

Data audit, AI opportunity mapping and stakeholder interviews. We define where AI creates real value and where it creates noise. Deliverable: AI Opportunity Map.

Week 3–4 — Design

Architecture, models and metrics

Product architecture, model selection, UX design for AI interfaces and metric definition. Deliverable: Technical Spec + Product Architecture.

Week 5–10 — Build

2-week sprints with working demos

Development in sprints with working demos. Integration with your existing stack. Testing with real data. Each sprint has a measurable deliverable. Deliverable: MVP in production.

Week 11–12 — Launch + Operate

Gradual deploy and transition to Operate

Gradual deploy, A/B testing in production, team onboarding and technical documentation. Transition to the Operate phase, with continuous monitoring and optimization.

Timeline reference

We define it in Discovery

These timelines are references for a product of medium complexity. A simple copilot can be ready sooner; a more complex product approaches 12 weeks. We define the exact scope in Discovery.

Security & compliance

ISO/IEC 27001:2022 certified. Not a slide: an audit.

ISO/IEC 27001:2022 certification Certified management system

Every AI product we build operates under our certified information security management system.

Access control to training data and auditing of prompts and responses.

Encryption in transit and at rest, with compliance to applicable data protection regulations.

Your data is never used to train third-party models. Your prompts are not shared. Your information never leaves authorized environments.

Your next AI product starts with a technical conversation, not a sales pitch.

Book a 30-minute Discovery session with our technical team. No strings, no generic decks. We talk about your case, your data and your goals. And we tell you honestly whether AI applies or not.

20 years of digital commerce. AI products that run in production, not in slides.