Most AI projects stall in the same place: a demo that impresses in a meeting and falls apart under real users, real data, and real cost. The distance between those two states is engineering, and that is the part we do.
We start by finding where AI actually earns its keep in your product. Often it is one workflow, not a platform. Then we build it properly — evaluated, observable, cost-modelled, and with a sensible fallback when the model is wrong.
We also take over prototypes you already have. If a promising thing was vibecoded into existence and now needs to survive contact with production, that is a well-worn path for us.
Capabilities
AI Discovery Workshops
Find the AI features worth building, and the ones that are not. A few structured days that replace a backlog of speculative ideas with a short, costed roadmap.
testVibecoded to Production
Your AI built it. We make it safe to run. We take AI-generated prototypes that already work and turn them into software you can put in front of customers.
learn moreData Engineering
Pipelines you can trust the numbers from. We build the infrastructure that turns scattered operational data into something a business can actually make decisions on.
learn moreML & Analytics
Models and dashboards that change what people do. We build analytics tied to decisions, and machine learning only where it beats a simpler answer.
learn moreFrom idea to a feature you can trust.
Find the leverage
One workshop to separate the AI features that pay from the ones that demo well.
Prototype
The narrowest useful version, measured against a real evaluation set.
Harden
Guardrails, fallbacks, cost controls, and observability before anyone depends on it.
Operate
Monitoring drift, quality, and spend once it is live.
Evals before opinions
If we cannot measure whether it got better, we will not ship it.
Cost is part of the design
Token spend is modelled up front, not discovered in the first invoice.
Model-agnostic
We pick per workload and keep the swap cheap as the field moves.
Common questions
What types of AI projects do you work on?+
We help with AI discovery, productionizing prototypes, data engineering, machine learning, analytics, and AI features embedded into existing digital products.
Can you help us identify where AI is actually worth using?+
Yes. We usually start by finding the workflows where AI can create measurable value, then narrow that into a practical roadmap rather than forcing AI into places where it does not belong.
Can you take an existing AI prototype into production?+
Yes. We often take over prototypes or vibecoded concepts and harden them with testing, evaluations, observability, cost controls, fallbacks, security, and production infrastructure.
How do you measure whether an AI feature is good enough to ship?+
We define evaluation criteria before launch and test against real or representative data. If we cannot measure whether the feature is improving, we do not consider it production-ready.
Do you work with a specific AI model or provider?+
We are model-agnostic. We select models based on the workload, quality, latency, privacy, and cost requirements, and design the system so switching providers later is practical.
How do you control AI costs in production?+
Cost is considered during architecture and prototyping. We model expected usage, monitor spend, optimize prompts and model selection, and put controls in place before usage scales.