We design, train, and deploy custom machine learning systems that solve the specific problems your business faces. From first architecture to production deployment, senior ML engineers handle every stage.
Discuss your ML challengeYour data science team built a model in a notebook that shows promising results. Now it needs to handle real traffic, integrate with your existing systems, retrain automatically, and not break at 3am. That gap between prototype and production is where most AI investments stall — and where we start.
We select the right architecture for your specific data characteristics, constraints, and performance requirements. Not every problem needs a transformer — sometimes a well-tuned gradient boosting model outperforms a deep learning approach at a fraction of the cost. We evaluate options, train, validate, and iterate until performance targets are met.
Demand forecasting, churn prediction, anomaly detection, recommendation engines, dynamic pricing. These are high-value ML applications where model accuracy translates directly to revenue or cost savings. We build them with production constraints in mind from day one — latency requirements, data freshness, fallback logic when predictions are uncertain.
Some applications can't wait for batch predictions. Fraud detection needs millisecond inference. Recommendation engines need to respond before the page loads. We build streaming inference pipelines, low-latency serving infrastructure, and edge deployment packages for use cases where speed is the feature.
A model deployed today will degrade over time as the real world changes. We build the systems that detect when performance drops, trigger retraining on fresh data, validate new models against the old ones, and promote updates to production without downtime. Your AI system doesn't just launch — it improves.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints — even if it means building something nobody's built before.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting. Not by cutting corners — by using AI for the volume work while senior engineers focus on decisions that matter.
Timeline
8-12 weeks from kickoff to production deployment
Team
2-3 senior ML engineers, each with 10+ years of experience
Deliverables
Production model, serving API, monitoring dashboard, retraining pipeline, documentation
After launch
Optional MLOps retainer for ongoing model monitoring and improvement
A mid-market fintech company processed 50 million daily transactions but relied on rule-based systems for fraud detection — catching only 60% of fraudulent activity with a 15% false positive rate. Their data science team had a promising prototype in a Jupyter notebook but couldn't get it to production. We architected a real-time inference pipeline, retrained their model on 18 months of labelled transaction data, built automated retraining triggers, and deployed to production with shadow-mode validation. The system now runs at sub-10ms latency with a 94% detection rate and 3% false positive rate.
Representative of a typical engagement. Specific metrics reflect achievable outcomes based on our experience with similar projects.
We work with your data as it is. Part of our engagement includes data assessment — understanding what you have, what's usable, what needs cleaning, and whether you need additional data sources. If significant data engineering is needed, we scope that as a separate workstream so it doesn't slow down the ML work.
We're framework-agnostic — we select based on your requirements. For most enterprise ML, we work with PyTorch, TensorFlow, scikit-learn, XGBoost, and Hugging Face. For serving, we use TorchServe, Triton, or custom FastAPI endpoints depending on latency and throughput needs. For MLOps, we integrate with MLflow, Weights & Biases, or your existing tooling.
Every engagement includes documentation and knowledge transfer. We deliver runbooks, architecture diagrams, and training materials so your team can operate the system independently. We also offer optional MLOps retainers where our engineers monitor model performance, handle retraining, and implement improvements on an ongoing basis.
That's exactly what our initial consultation is for. We'll assess your problem, your data, and your constraints and give you an honest answer — including recommending a simpler approach if ML isn't justified. We'd rather tell you not to build something than build the wrong thing.
Tell us what you're trying to solve. We'll give you an honest assessment of what's buildable, what timeline to expect, and whether ML is the right approach.
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