We apply predictive models, LLMs, and AI-assisted workflows to specific business problems. The work starts with the decision or process to improve, then selects the simplest reliable approach that can be tested and monitored.
A focused service should make the important work clearer, faster, and easier to measure. Here is what this engagement can cover for your team.
Forecasting and prediction models built on your actual historical data.
Integrate large language models into your existing tools and workflows.
Purpose-built AI tools for specific internal or customer-facing needs.
A clear process reduces risk: understand the current state, agree on priorities, implement in stages, test the result, and hand over a system your team can use.
Identify the specific problem AI is actually meant to solve — not a vague goal.
Assess what data is available and whether it supports the intended use case.
Choose an appropriate model or LLM integration approach for the problem.
Build the integration and test it against real scenarios.
Deploy with monitoring in place to catch issues early.
It depends on the use case — some projects use your data, others use general-purpose models. We start with a measurable use case and choose the simplest reliable approach that can be monitored after launch.
We select the model based on the task, rather than defaulting to one provider. We start with a measurable use case and choose the simplest reliable approach that can be monitored after launch.
No — many integrations are scoped for small businesses with a specific, narrow use case. We start with a measurable use case and choose the simplest reliable approach that can be monitored after launch.
Integrations are grounded in your actual data and instructed not to invent information outside it. We start with a measurable use case and choose the simplest reliable approach that can be monitored after launch.
Message us for a scoped quote and timeline based on your specific situation.
Describe the decision, process, or customer experience you want to improve. We will help separate a useful AI opportunity from an expensive experiment.