How BTB builds minds that act.
Most AI models are trained on what sounds right. Ours are trained on what is proven right. That single difference runs through everything below.
Verifier-first training
Every capability of BTB Blitz is trained against a machine that can check the answer. Mathematics is independently recomputed. Code is executed. Optimisation results are certified by solvers. Facts are checked against sources. An answer that merely sounds convincing earns nothing; an answer whose reasoning survives verification becomes training signal. The model learns from its own verified reasoning — so its ceiling is not set by any teacher.
Training covers more than twenty separately verified domains — from mathematics, finance and strategy to critical thinking, persistence and style — and each domain must pass a sealed acceptance exam before it counts as done.
Optimum — provably best decisions
Optimum is the core technology of the BTB model line. Given a real situation — a budget, a schedule, a portfolio, a plan — it builds a formal model of the decision, solves it, and returns the answer together with the reason it cannot be beaten. Not "a good option": the provably best one available.
Chained step by step, Optimum becomes something larger: each proven-best decision creates a new situation, which gets its own proven-best decision. The future is not predicted — it is built, one certified step at a time. Optimum is in active development as the core of BTB Blitz and will also be offered as a standalone decision service.
Zero hallucination — three classes of proof
A BTB answer is required to know how sure it is. Every conclusion carries one of three labels: proven (mathematically or factually certain), proven with a probability (the model states the number), or honestly uncertain (said plainly, never dressed up as fact). Presenting the unproven as proven is treated as a defect of the model — and trained against, mercilessly.
Acting, not advising
BTB models are built to finish tasks, not to describe them. The execution layer gives the model swarms of up to 50 parallel agents and 45 integrated tools — live web and news search, deep research, open-source intelligence, browsing, mathematical solvers, simulation, documents and messaging — so "done" means done, with the result in your hands.
Memory that does not reset
Through its memory architecture the system holds an effective working context of over 4 million tokens: conversations, facts, decisions and contacts persist across sessions and return exactly when needed. You never re-introduce yourself.
BTB Micro — measured efficiency
BTB Micro is our inference-efficiency layer for organisations that run AI on GPUs: the same GPU serves up to 2× more concurrent users at the same response speed — measured on NVIDIA B200 against a well-tuned vLLM baseline, with answer quality confirmed by blind judging. It runs inside the customer's own infrastructure; nothing leaves their environment. Details →
Privacy by architecture
Passwords, documents and personal identifiers are held in a vault on the user's own device — Secure Enclave on iPhone, Keystore on Android. Everything sent to our servers is encrypted on the device first; the servers store ciphertext they cannot read. This is not a privacy promise. It is a property of the architecture: we can help you, but we cannot betray you — even technically.