Production-ready AI for e-commerce & business
AI that sells, supports, and scales — from day one.
We build production AI systems for brands that want real results: intelligent search, knowledge chatbots, WhatsApp stores, visual matching, and private LLM infrastructure. One backend powers everything.
Our Proof
Built by the team behind Cabina AI
We don’t just talk about multi-LLM orchestration — we built a product around it. Cabina AI is our own multi-model AI platform that aggregates 25+ LLMs in one interface: commercial giants like GPT-5, Claude Opus, Gemini, alongside open-weight models like LLaMA 4, Mistral, and DeepSeek.
3+ years in production. Thousands of daily queries. The same architecture we deploy for our clients.
Visit cabina.aiThe reality check
AI is transforming business.
But the way most companies adopt it is broken.
Three problems every business faces when integrating AI — and most don’t realize until they’re already locked in.
AI Costs Are Exploding
Commercial AI APIs charge per token (roughly ¾ of a word). Frontier models like GPT-5 or Claude Opus cost $15–$75 per million tokens.
A busy chatbot handling 10K conversations/day can easily hit $3,000–$10,000+/month. And as your business grows, costs scale linearly.
Your Data Leaves Your Servers
Every API call to OpenAI, Google, or Anthropic sends your data to third-party servers. Customer PII, business logic, pricing strategies, proprietary documents — all transmitted externally.
For regulated industries (finance, healthcare, legal), this is a compliance nightmare. GDPR, CCPA, HIPAA — all require you to control where data goes.
Vendor Lock-in Is a Trap
Build everything on one provider — GPT, Claude, Gemini — and you’re at their mercy. Price hikes, API changes, service outages — you have zero control.
Switching providers means rewriting integrations, retraining workflows, re-testing everything. That’s months of work and risk.
Our answer
Take back control of your AI.
Model-agnostic. Future-proof.
Every system we build runs on a model-agnostic orchestration layer. Your applications connect to one API — behind it, we route to the best model for each task. Switch providers with zero code changes. No migration. No rewrite. No risk.
Better model launches? Switch in minutes, not months.
Use GPT-5 for complex tasks, LLaMA for simple ones — automatically.
Self-host open-weight models. Your data never leaves your servers.
Smart routing sends cheap queries to cheap models. Save thousands monthly.
What This Means For Your Budget
Real numbers at 10,000 AI requests per day
All queries to OpenAI / Anthropic. No optimization. Full price for every request.
Simple queries → cheap models. Complex → frontier. Automatic, transparent.
Open-weight models on your hardware. Sensitive data never leaves your servers.
Solution 01
AI-Powered Search
Your customers search by meaning, not keywords. RAG-based semantic search that actually understands what people want.
Traditional search fails when customers don’t know the exact product name. Our RAG (Retrieval-Augmented Generation) engine converts your entire catalog into vector embeddings and matches queries by intent, context, and semantic similarity — not keyword matching.
How It Works
- Product catalog, descriptions, and metadata converted into vector embeddings
- Queries matched by semantic similarity — not exact strings
- LLM re-ranks results and generates explanations when needed
- Real-time catalog sync — new products searchable in minutes
- Handles synonyms, typos, multilingual queries across 120+ languages
Business Impact
- +35–60% search-to-product conversion rate
- –40% “zero results” searches
- Customers find products they didn’t know how to describe
- Works across every language your customers speak
Vector Embeddings & Tensor Operations
Every product in your catalog is transformed into a high-dimensional vector (tensor) — a mathematical representation that captures meaning, not just words. We use transformer-based embedding models (OpenAI Ada, Cohere Embed, or open-weight alternatives like BGE/E5) to convert product titles, descriptions, attributes, and even images into dense vectors of 768–1536 dimensions.
These tensors are stored in a vector database (Pinecone, Weaviate, Qdrant, or pgvector) optimized for approximate nearest neighbor (ANN) search using HNSW indexing. When a customer searches, their query is embedded into the same vector space, and we perform cosine similarity search across millions of product vectors in <50ms.
Training & Fine-Tuning Pipeline
Off-the-shelf embedding models give you 70–80% accuracy. To get to 95%+, our ML engineers fine-tune models on your specific domain:
- 1.Data collection: We harvest your product catalog, search logs, click-through data, and customer reviews to build training pairs
- 2.Contrastive learning: We train with triplet loss — teaching the model that “gold pendant with sun motif” is close to your Sun Medallion, but far from a silver bracelet
- 3.Domain-specific tokenization: Industry terms like “pavé setting”, “belcher chain”, or “Hyvä theme” get proper embeddings instead of being treated as unknown tokens
- 4.Evaluation & iteration: We measure nDCG@10, MRR, and recall metrics against your actual search logs, iterating until quality targets are met
This process typically takes 2–4 weeks of our senior ML engineers' time. The result: a custom embedding model that understands your product domain better than any generic solution.
The Full RAG Pipeline
Retrieval Layer
Hybrid search combining vector similarity (semantic) + BM25 (keyword) + metadata filters (price, category, availability). Results are fused using Reciprocal Rank Fusion (RRF).
Re-ranking Layer
Cross-encoder model (trained on your click data) re-scores top-50 results for precision. This is where the “magic” happens — turning good results into great ones.
Generation Layer
LLM generates natural-language explanations: why each product matches, alternative suggestions, and follow-up questions. Routed to cheap models for simple queries, frontier for complex.
Feedback Loop
Every search, click, and purchase feeds back into the system. The model continuously improves as it learns what your customers actually want.
Solution 02
AI Knowledge Chatbot
Trained on your data — Zendesk, docs, FAQs. Answers like your best employee, 24/7 in 120+ languages.
The chatbot uses the same RAG backend as AI Search. One integration — two products. Every question your chatbot answers draws from the same knowledge graph as your search bar. Improve one, both get better.
Knowledge Sources
- Zendesk / Freshdesk tickets
- Notion / Confluence docs
- Product catalogs (CSV, API)
- PDF manuals & guides
- Any custom data source
Capabilities
- Natural conversation flow
- Brand voice & tone matching
- Escalation to human agents
- 120+ languages
- 24/7 availability
Deploy Anywhere
- Website widget (JS)
- Shopify / Magento app
- API for custom UIs
- Slack / Teams
- WhatsApp (see below)
Shared Backend Advantage
Search and Chatbot share the same AI backbone. Any improvement to your knowledge base instantly benefits both channels. Train once — serve everywhere.
Solution 03
WhatsApp Commerce Bot
A full store inside WhatsApp. Browse, ask, recommend, and buy — all in one chat.
Same AI backend, new channel. Your customers can browse products, get AI-powered recommendations, manage their cart, and complete purchases — without ever leaving WhatsApp. 2 billion monthly active users. 98% message open rate. Zero app install needed.
Commerce Features
- Product browsing with rich cards & images
- AI-powered product recommendations
- Cart management & checkout flow
- Order tracking & status updates
- Payment integration (Stripe, local gateways)
Why WhatsApp
- 2B+ monthly active users worldwide
- 98% message open rate vs 20% for email
- Zero friction — no app download required
- Dominant in LATAM, EU, Middle East, Asia
- Seamless handoff to human support
Solution 04
Visual Product Recognition
Snap a photo of something you love. Our AI finds the closest match in your catalog.
Customer sees a medallion on Instagram, a necklace in a magazine, a bracelet on a friend. They upload the photo — our vision AI extracts visual features (shape, color, texture, style) and finds the closest products you actually sell. Works via website, chatbot, or WhatsApp.
How It Works
- 1.Customer uploads a photo (website, chat, or WhatsApp)
- 2.Vision model extracts visual features — shape, color, texture, style
- 3.Vector similarity search finds closest matches in your catalog
- 4.Results displayed with confidence score and purchase link
Perfect For
- Jewellery — find similar medallions, chains, rings by visual style
- Fashion — clothing, accessories, shoes
- Home & furniture — match style & aesthetic
- Art & decor — aesthetic similarity matching
A Note on Accuracy
Visual matching is probabilistic — not 100% exact every time. But as a wow-factor marketing feature, it drives engagement, increases time-on-site, and creates memorable shopping experiences that customers talk about. We’ve deployed this in production for luxury jewellery e-commerce and it works beautifully.
Catalog Image Processing & Feature Extraction
Every product image in your catalog goes through a multi-stage processing pipeline. We use CLIP (Contrastive Language-Image Pre-training) and custom-trained Vision Transformer (ViT) models to extract rich feature tensors from each image — 512 to 2048 dimensions capturing shape, color palette, texture, pattern, material, and style.
For a catalog of 5,000 products, initial processing takes 4–8 hours on GPU infrastructure. The result is a vector index where visually similar items are mathematically close in embedding space.
Custom Model Training
Generic vision models understand “necklace” vs “ring”, but they don’t understand the difference between a pavé-set Strength medallion and an enamel Dream pendant. Our ML specialists fine-tune the vision backbone on your specific product domain:
- 1.Data augmentation: We generate thousands of training variants — different angles, lighting, backgrounds, crops — from your existing product photography
- 2.Contrastive fine-tuning: The model learns which visual features matter for your products. Gold texture vs silver, geometric vs organic patterns, minimalist vs ornate designs
- 3.Multi-modal alignment: We align visual features with text descriptions, so the system understands that a photo of a lion motif maps to “Strength” tenet products
- 4.Precision evaluation: We measure top-5 and top-10 recall against human-labeled test sets, iterating until match quality exceeds 85%+ precision
This fine-tuning process requires 40–120 GPU-hours depending on catalog complexity, and takes our senior ML engineers 2–3 weeks of focused work. The investment pays off: your visual search becomes uniquely tuned to your brand’s aesthetic language.
Continuous Improvement
The model doesn’t stop learning after deployment. Every customer interaction generates feedback signals: which matches they clicked, which they ignored, which led to purchases. This data feeds back into the training pipeline for periodic re-training (typically monthly), steadily improving match quality over time.
Solution 05
Private AI Cloud & LLM Orchestration
Your own AI infrastructure. Private. Cost-optimized. Multi-model. No data leaves your servers.
We deploy a multi-LLM orchestration layer inside your own infrastructure — AWS, GCP, Azure, or on-prem. It connects to commercial APIs (OpenAI, Anthropic, Google) and self-hosted open-weight models (LLaMA, Mistral, DeepSeek) through a single unified API. Your applications don’t care which model answers — our router picks the best one automatically.
Private Backend Orchestrator
A central hub deployed in your environment. Connects to 25+ models — commercial and self-hosted. One API for all your AI needs. Switch models with zero code changes.
Intelligent Cost Routing
Not every query needs a $75/M-token model. Simple queries go to cheap open-weight models. Complex reasoning goes to frontier models. 60–85% cost reduction, no quality loss.
Total Data Privacy
Sensitive data processed exclusively by self-hosted open-weight LLMs. Nothing leaves your servers. GDPR, CCPA, HIPAA — compliant by architecture, not just policy.
Open-Weight LLMs
We set up and fine-tune LLaMA 4, Mistral, DeepSeek, Qwen on your hardware. Free to run — you only pay for compute. Perfect for high-volume and sensitive workloads.
Solution 06
Predictive Analytics & Personalization
Your AI backend learns your customers. Sales forecasts, dynamic campaigns, and personalized product feeds — all automatic.
The same AI infrastructure that powers search and chatbots also collects and analyzes behavioral data: what customers search for, what they click, what they buy, and what they ignore. This data feeds predictive models that transform how you sell.
Personalized Product Feeds
Knowing a customer’s browsing history, search patterns, and past purchases, the AI re-orders product listings in real time. Each customer sees products most likely to resonate with their taste — not a generic “best sellers” list.
Sales Forecasting
ML models trained on your historical sales data, seasonality patterns, and external signals (trends, weather, events) generate demand forecasts at product-level granularity. Plan inventory, marketing spend, and staffing with confidence.
Dynamic Campaigns
Trigger automated marketing campaigns based on AI-detected signals: abandoned cart patterns, repeat purchase cycles, cross-sell opportunities, price sensitivity thresholds. The right message to the right customer at the right time.
Customer Segmentation
AI automatically clusters customers into behavioral segments — not just demographics, but actual shopping behavior: impulse buyers, researchers, gift shoppers, loyal repeaters. Tailor your messaging to each segment.
Smart Recommendations
“Customers who bought Strength also love Resilience” — but smarter. AI analyzes visual similarity, price affinity, tenet alignment, and purchase sequences to recommend products that genuinely match each customer’s taste.
Continuous Learning
Every customer interaction feeds back into the models. The longer you run it, the smarter it gets. No manual rules, no static segments. Pure data-driven optimization that improves on autopilot.
Built on the Same Backend
All analytics and personalization runs on the same AI infrastructure as search, chatbot, and WhatsApp. The data collected from one channel enriches all others. A customer’s WhatsApp conversation informs their website search results — and vice versa.
One Backend — Five Products
All solutions share the same AI backbone. Build once — deploy to search, chat, WhatsApp, and visual discovery simultaneously.
Ready to add AI that actually works?
Tell us about your challenge. We’ll show you what’s possible — with real numbers, real architecture, and zero buzzwords.
Get in touch