The model is a small piece of a much larger engineering system. The data pipelines, training infrastructure, inference layer, and monitoring around it are what determine whether machine learning delivers value inside your product or just inside a demo. Every system we build is designed for production from day one. We build ML systems end-to-end - data pipelines, model development, inference infrastructure, monitoring - with one senior team that owns the entire stack. And for the record, AI is a marketing term. The engineering discipline is machine learning.
[ WHO WE WORK WITH ]
>> 01
You're building a product where ML is the value.
The machine learning is the product. You need a team that understands both the research and the production engineering to ship a system your users can depend on every day.
>> 02
You're adding machine learning features to an existing product.
You have a product that works and you want to make it smarter - recommendations, search, predictions, or automation - integrated properly without disrupting what's already generating revenue for you.
>> 03
Your ML initiative has stalled or failed in production.
You've invested the time and the budget. The model works in a notebook, the demo looked great, and now you need a team that can get it running in production.
>> 04
You're replacing manual processes with machine learning and automation.
Your team spends hours on work that a well-built ML system could handle in seconds. You need it built, integrated into your daily operations, and running reliably from day one.
[ OUR OFFERINGS ]
>> 01
Computer Vision
Computer vision systems need to work under real-world conditions - variable lighting, noisy input, and edge cases the training data didn't cover. We build detection, classification, segmentation, and pose estimation systems that run reliably in production environments. Our founders published peer-reviewed research in human pose estimation and object pose estimation, and our team brings that research depth to every engagement. We handle the full pipeline from data labeling and model training through deployment and monitoring.
>> 02
Predictive Models & Analytics
Forecasting, scoring, classification, anomaly detection - models that take your data and turn it into decisions your team or your product can act on. The challenge is rarely the algorithm. It's building data pipelines that keep features fresh, training infrastructure that retrains automatically as patterns shift, and monitoring that catches model drift before your predictions go stale. We build the full system so the models stay accurate in production, not just in the initial evaluation.
>> 03
Recommendation Systems
Recommendation systems are deceptively hard to build well. The model is the easy part - the real engineering challenge is the full pipeline: candidate generation, ranking, re-ranking, real-time feature serving, and A/B testing infrastructure that lets you measure what's actually working. Most teams ship a basic collaborative filter and wonder why engagement doesn't move. We build the full production stack from data pipelines through real-time inference, designed to improve continuously as your user base grows.
>> 04
LLMs, AI Agents and Integrations
Large language models are powerful and unpredictable. The engineering challenge is building reliable systems around them that are cost-efficient and controllable - retrieval-augmented generation, structured output parsing, guardrails, prompt management, evaluation pipelines, and fallback logic for when the model gets it wrong. We build LLM-powered features and agentic workflows that integrate into your product with the same production rigor we apply to any ML system: monitored, tested, and designed to degrade gracefully under real conditions.
[ OUR PROCESS ]
We make right technology choices.
[ TESTIMONIALS ]
>> Know more about us
Algorithmic was started by Sanket and Yuriy while they were studying together in Genova, Italy. They've both spent over a decade each in software engineering and machine learning. They specialized in two distinct verticals of advanced computer vision - Sanket in human pose estimation, Yuriy in object pose estimation - which gave them complementary expertise in how machines interpret the physical world, and more importantly, what it takes to build robust software systems that make these algorithms work in production.
They started Algorithmic to bring this industry and academic expertise to a broader range of ambitious founders and teams seeking for senior professionals to support them in getting things right, in the first attempt. That project became a studio with a team of senior engineers, PhDs, and published researchers who share the same standard.
If it's worth building, build it well.
[ CASE STUDIES AND SOLUTIONS ]
We understand the space, fill in the gaps fast, and get real systems out the door.
OFF