AI that fails loudly, not quietly
LLMs are excellent at language and terrible at arithmetic. The most common failure we see is an AI feature that produces confident, plausible, wrong output, and nobody notices until a customer does.
Our approach is simple: let deterministic code compute every number, and let the model do what it is good at: classifying, extracting and writing. Around that we add JSON schema validation, retries, post-processing overrides and spot-checks that reject bad output before it reaches a user.
What we can build
- Forecasting and analytics narratives. Numbers from your engines, explanations from the model.
- Classification and routing. Tickets, leads, documents or products sorted into your categories.
- Semantic search. Find the right record even when users don't use the right words.
- Self-hosted LLMs. Keep data in-house by running open models such as Qwen or DeepSeek on your own GPUs.
Proof
We built a forecasting pipeline for an ad-tech platform that produces 30-day impression, CPM and reach forecasts for 13 ad products, using self-hosted models on CUDA GPUs. When the model got numbers wrong, we redesigned it so it never touches the math.