Service 03
Machine learning models, LLM-powered applications and intelligent automation — designed around your data, evaluated rigorously and deployed into real products.
We help teams put AI to practical use. That might be an assistant grounded in your own documents, a model that forecasts or classifies from your data, or automation that removes repetitive work from a process.
We start with the problem and the data, not the model. Every system is evaluated against clear criteria, deployed behind a proper API and monitored in production, so it stays useful long after launch.
Capabilities
LLM applications: assistants, copilots and document question-answering
Retrieval-augmented generation (RAG) over private knowledge bases
Predictive models for classification, forecasting and recommendations
Data pipelines, feature engineering and model training
Evaluation, guardrails and cost and latency optimization
Model deployment as APIs, with monitoring in production
Technologies
Use cases
A support assistant that answers from your documentation
Automated extraction and classification of documents
Demand forecasting or churn prediction from historical data
Intelligent search or recommendations in an existing product
Process
Five stages, tailored to this discipline.
01
Frame the problem, audit the available data and define how success will be measured before choosing an approach.
02
Select models and architecture, build the evaluation set, and plan for privacy, cost and latency.
03
Prototype quickly, measure against the evaluation set, then harden the pipeline for production.
04
Deploy behind a versioned API with logging, monitoring and human review where it matters.
05
Track quality in production, refresh data and models, and improve as real usage reveals edge cases.
FAQ
Mobile apps built with Flutter, websites and web applications built with React and Next.js, and applied AI work such as LLM applications, retrieval-augmented generation (RAG) and predictive models. If your idea doesn't fit neatly into one of these, tell us about it anyway.
With a conversation about what you want to achieve. From there we scope the work together — goals, users, constraints and priorities — and agree on a plan before any development begins.
Yes. We can extend, modernize or stabilize an existing application. We usually begin with a review of the codebase and architecture, so our recommendations are grounded in how the system actually works.
Every project is different, so we estimate after scoping rather than selling fixed packages. Once goals and priorities are clear, we propose a plan with milestones, so you know what will be delivered and in what order.
Start here
Share what you're building and where you're stuck. We'll help you figure out the right next step.
