Our pipeline is deliberately simple, which is what makes it reliable. One prompt goes in. A panel of independent models works it in parallel. A senior evaluator synthesizes the best of them into a single answer — and for verifiable domains, we measure the result against objective benchmarks.
A single prompt is dispatched simultaneously to a panel of independent models. Each works the problem on its own and returns a complete solution. No coordination, no shared scratchpad — just parallel attempts at the same target.
No model sees another model's work. We preserve the full diversity of approaches, because that diversity is the entire source of the performance gain. Anchoring and groupthink are designed out of the system from the start.
A senior evaluator model receives the original prompt alongside every candidate answer. It weighs them, extracts the strongest reasoning from each, and produces one final result that reflects the best of the panel — editing rather than inventing.
For domains like code, we measure against objective benchmarks. We do not ask whether an answer looks right. We test whether it runs and returns the correct result.
Five models attacking a problem from five angles surface more correct paths than one model attempting it once.
Synthesizing vetted candidate answers is far lighter work than generating a solution from a blank page, which is why a strong evaluator can do it quickly and inexpensively.
A panel of efficient models plus one synthesis pass frequently matches or beats a single premium model on quality, at comparable or lower total cost.
When one model is wrong, the others and the evaluator catch it. No single point of failure.
Our research is focused where output quality can be measured objectively rather than judged subjectively.