AI-Driven Products
Your product needs AI that works at scale, not just in a demo. We build AI-powered features and platforms engineered for production — reliable, performant, and built to evolve.
THE CHALLENGE
The demo worked. Production is a different problem.
Your competitors are shipping AI features. Your board is asking about your AI roadmap. You need AI woven into your product experience, not a bolt-on chatbot, but intelligent features that make your product genuinely better for the people who use it.
Or maybe you've already started. You launched an AI feature six months ago and it's not moving the needle. It works in controlled conditions but breaks on edge cases, degrades under load, or produces outputs your team can't explain or improve. The gap between a working demo and a production AI feature is where most initiatives stall.
The model is rarely the hard part. The hard part is the architecture around it: how you handle failures gracefully, how you evaluate quality at scale, how you monitor drift, and how you build something your team can iterate on without re-architecting every time the model changes.
OUR APPROACH
Engineered for production, not just for launch day.
We've shipped AI features that handle 200,000+ messages a day, serve enterprise clients like Banco Santander and Danone, and run in production across 40+ countries. The patterns we apply come from that experience, not from theory.
Architecture for Resilience
Multi-model orchestration with automatic failover. Dynamic model selection based on cost, latency, and availability. Graceful degradation when things go wrong - because in production, they will. The architecture decisions made on day one determine whether your AI features still work at 10x the users.
Evaluation from the Start
If you can't measure it, you can't improve it. We build observability, evaluation, and traceability into AI features from the beginning, not as an afterthought when something breaks. Turn-level, session-level, and cohort-level: understanding not just whether the AI responded, but whether it helped.
Model-Agnostic
LLMs evolve fast. We design architectures that let you swap models as capabilities improve, without rebuilding your product around them. Compartmentalized agents with clean interfaces, so the intelligence layer can evolve independently.
Production Hardening
Prompt engineering for consistency across thousands of real-world inputs. Range testing against variable data quality. Explicit UX patterns that make it clear when content is AI-generated. The difference between a prototype and a product is the work that happens after the model works.
CAPABILITIES
AI capabilities, built for real users.
LLM Integration & Agentic Architectures
Multi-agent pipelines, conversational AI, intelligent document processing. From single-model features to orchestrated agent systems where specialized components handle distinct tasks, with the traceability to debug and improve each one.
Intelligent Search, Recommendation & Personalization
AI that understands context, not just keywords. Behavioral scoring, real-time recommendations, and personalization engines that learn from usage and improve over time.
ML Model Training, Deployment & MLOps
From training to deployment to monitoring in production. The infrastructure to keep models performing as data evolves, not just the model itself.
Computer Vision & NLP
Image recognition, natural language understanding, voice processing, and translation at scale. Production features that handle the messy reality of user-generated content.
AI Performance Monitoring & Iteration
Systematic evaluation frameworks connecting quality measurement to root cause diagnosis. Observability, evaluation, and traceability. So when something drifts, you know where to look and what to fix.
AI IN PRODUCTION
AI products we've built and shipped.
Scale-upVC Fund
From AI Prototype to Production Pipeline in Under Two Weeks
WS LabsAIScribe for Jira
Combining AI and automation to reduce documentation time by 75%
Whitespectre is a company you can rely on for high-quality, well-architected products.
Rodrigo Guzman
Co-founder, Hubble_s
Scale-upHubble_s
Transforming enterprise companies with AI-driven insights
WHY US
We build products, not prototypes.
We've written about the gap between AI demos and production systems, and we've shipped on the production side of that gap, repeatedly. The difference is engineering rigor: compartmentalized architectures, systematic evaluation, production monitoring, and the discipline to test against messy real-world inputs, not clean sample data.
13+ years of production engineering for platforms serving millions of users (Fabletics: 7M weekly views, Beachbody: hundreds of thousands of users). We apply the same rigor to AI: because an AI feature that breaks at scale is worse than no AI at all.
