Artificial intelligence startup Probably has secured nine million dollars in seed funding from Andreessen Horowitz to develop a novel architecture for eliminating large language model hallucinations. Led by founder Peter Elias, the company is engineering a deterministic validation framework designed to achieve near-perfect factual accuracy, a reliability threshold historically reserved for non-probabilistic software systems rather than generative AI. The company’s inaugural product is a data science platform that generates rapid insights from complex datasets while attaching citations and complete audit trails to every output. To prevent erroneous or fabricated responses from reaching end users, the system employs what Elias describes as a data science mech suit. This architecture routes an initial language model response through a strict deterministic validator that cross-references the output against the source dataset, discarding any unverified claims. The underlying model is trained specifically to align with this validator, creating a tightly coupled ecosystem optimized for speed, traceability, and precision. Elias emphasizes that robust harness engineering fundamentally alters AI deployment economics. By strictly defining the operational context and filtering ambiguity, the system allows significantly smaller language models to perform reliably, reducing dependence on compute-heavy frontier architectures. The current iteration operates on a model substantially less capable than industry-leading alternatives, yet delivers production-ready accuracy. This architectural shift enables deployment on local desktop hardware rather than specialized data centers, dramatically lowering token consumption and infrastructure expenses at a time when enterprise technology teams are aggressively reassessing AI budgets. The validation engine is designed for modular expansion beyond data science, targeting high-stakes sectors such as accounting, healthcare, and legal compliance where precision is non-negotiable. Elias notes that major artificial intelligence laboratories have largely overlooked this approach, partly because their commercial models depend on volume-based token pricing and iterative user correction cycles. Probably’s framework inverts that dynamic by prioritizing upfront accuracy over post-generation refinement, aligning generative performance with enterprise-grade reliability standards. With fresh capital backing its development, Probably aims to establish a new engineering paradigm that decouples AI precision from exponential compute scaling. The company’s methodology signals a broader industry pivot toward deterministic safeguards and optimized model orchestration, offering a cost-effective alternative to the prevailing arms race for increasingly large language models.