Applied AI, machine learning and mathematical modeling, built to work on real data and hold up in production.
Start a project01 / Approach
Most of our AI work starts as a hypothesis: a pattern in your data, a bottleneck in a process, a system that can be described mathematically. We test it, measure it against a baseline, and keep only what holds up. The result is a model you can reproduce and explain, with evidence that it works.
02 / What we build
Assistants that answer from your own documents, models fine-tuned to your domain language, and agents that carry out multi-step tasks across your systems. Built with evaluation sets, access control and cost limits from the start.
Models that predict what users, customers or systems will do next. Each one is measured against a simple baseline before anyone relies on it.
Detection, tracking and recognition on images and live video, from cloud pipelines to models running on drones and robots.
Differential equations, stochastic processes and Monte Carlo simulation for physical, financial and operational systems. Useful when data is scarce but the underlying mechanics are known.
Linear, mixed-integer and constraint optimization for scheduling, routing, pricing and resource allocation. The output is a decision your team can act on, with the trade-offs visible.
Training pipelines, model registry, deployment, monitoring and drift detection on Azure and Databricks, so a model keeps performing after launch.
03 / Capability matrix
| Capability | Typical question | Core techniques | Tooling | Deliverable |
|---|---|---|---|---|
| Generative AI & LLM Systems | Can our staff get reliable answers from our internal documents? | Retrieval-augmented generation, fine-tuning, tool-using agents, evaluation | Azure OpenAI, Azure AI Search, Hugging Face, custom orchestration | Assistant, agent or API with an evaluation suite |
| Predictive & Behavioral Modeling | Which customers are ready to buy more? | Gradient boosting, deep learning, sequence and time-series models | Python, scikit-learn, PyTorch, Databricks | Prediction service and model report |
| Computer Vision & Autonomous Systems | What is happening in this video stream, right now? | Object detection, tracking, classification, edge inference | PyTorch, OpenCV, ONNX Runtime | Vision pipeline, in the cloud or on the device |
| Mathematical Modeling & Simulation | What does this change cost us downstream? | ODEs / PDEs, stochastic processes, Monte Carlo methods | Python, NumPy, SciPy, custom solvers | Validated simulation model |
| Optimization & Decision Engines | Which schedule meets every constraint at the lowest cost? | MILP, constraint programming, metaheuristics | OR-Tools, custom solvers | Decision or scheduling engine |
| MLOps & AI Platforms | Is the model in production still as accurate as it was at launch? | Automated retraining, monitoring, drift detection, CI/CD for models | Azure ML, Databricks, MLflow, Docker, Kubernetes | Automated training and deployment pipeline |
04 / Selected work
A learning platform with AI built into the core of the product.
Prediction models running inside iGaming platforms.
Models that predict user behavior from activity data.
AI software for robotic and drone platforms.
Vision systems that detect and recognize what is in an image or video.
Real-time voting over live video streams.
05 / Method
Turn the business question into a testable hypothesis and agree on how success will be measured.
Assess the data, set a baseline, and find out early whether the idea is feasible.
Develop the model or system in increments, each one evaluated against the baseline.
Ship it into your environment on Azure, Databricks or the platform you already run, with monitoring in place.
Track accuracy and drift, retrain when the data changes, and report what the model is actually doing.
06 / AAS Devel Labs
This practice is backed by our Labs team of scientists, mathematicians and applied AI/ML engineers. They test whether an approach is feasible, build research prototypes, and validate methods before they reach a client's system.
“The imagination of nature is far greater than the imagination of man.” Richard Feynman
Tell us what you need to predict, detect, optimize or automate. Our applied AI team will review it and come back with questions, not a sales deck.
Start a project →