Webtunes
A live music discovery platform that brings concerts, bar shows, and festivals into a searchable experience.
Read the project storyAI systems
AI with a defined job and a way to judge it.
AI features, retrieval systems, document workflows, and predictive models designed with evaluation, permissions, and real product value in mind.
Where AI can support a defined task
This service tends to make sense when:
- You're making forecasting or planning decisions based on gut feel or basic averages, with real cost to getting it wrong.
- Your team spends significant time manually classifying, tagging, or reviewing data that follows learnable patterns.
- Your team needs to search approved documents, extract information, or review material that is difficult to process manually.
- You need to detect anomalies or fraud at a scale and speed manual review can't match.
Define what a useful answer looks like.
Approved data, evaluation examples, and a route for uncertain outputs shape the feature before it reaches users.
See it in shipped work.
Project captures and the decisions behind them.
Tangible work. Clear deliverables.
Problem framing & feasibility assessment
An honest evaluation of whether ML is the right approach, what data is required, and what accuracy is realistically achievable.
Data pipeline & preparation
Building the pipelines needed to collect, clean, and structure the data the model depends on.
Model development or integration
Custom model training, or integration of proven pre-trained models, depending on what the problem actually requires.
Production integration
The model is integrated into your actual workflow or application — not left running in a notebook nobody uses.
Monitoring & evaluation setup
Ongoing accuracy monitoring so model performance degradation is caught early, not discovered after it's caused damage.
How the work comes together.
Problem definition & feasibility
We define the specific decision or task to improve and assess whether the available data can realistically support it.
Data pipeline development
Building reliable pipelines to collect and prepare the data the model needs, often the most underestimated part of ML projects.
Model development & validation
Building, training, and rigorously validating the model against real-world data, not just a clean test set.
Production integration
Integrating the model into your existing application or workflow so it's actually used, not just demoed.
Monitoring & handover
Setting up performance monitoring and handing over full documentation and code ownership.
Tools chosen for your product.
Your requirements, existing systems, and future team shape our technical choices.
ML frameworks
Python / PyTorch / TensorFlow / scikit-learn
Data infrastructure
Pandas / Apache Airflow / PostgreSQL / Elasticsearch
Deployment
AWS SageMaker / Google Vertex AI / Docker / REST/gRPC model serving
Monitoring
Model performance tracking / Data drift detection
Questions before the first conversation.
How much data do we need before an ML project is viable?
It depends heavily on the problem, but this is exactly what the feasibility assessment phase determines — we won't recommend a custom ML approach if your data volume or quality can't realistically support it.
What if the model isn't accurate enough to be useful?
Validation happens against real-world data before any production integration, so this is caught early. If accuracy targets genuinely can't be met, we'll tell you honestly rather than shipping something unreliable.
Do you use pre-trained models or build from scratch?
Whichever is appropriate. Many problems are well served by fine-tuning existing models, which is faster and cheaper than training from scratch — we only recommend custom training when it's genuinely necessary.
Does this include generative AI and retrieval systems?
Yes. We cover retrieval and LLM-based features alongside forecasting, classification, and other machine learning applications. The task, available information, evaluation criteria, and operating cost determine the approach.
What happens after the model is deployed?
We set up monitoring for performance and data drift as part of the deliverable. Ongoing retraining or support can be scoped separately if needed.
Let’s make it work
Let’s work out what your product needs.
Bring the idea, the current product, or the challenge. We’ll help define the next step and the right delivery approach.
Start a projectClarity from the first conversation.
Start with your business problem
Agree the scope before the build
Own your code and infrastructure