There is a property we are proud of at Zeretis: results are instant. That is approximately how long it takes to solve a problem on a standard laptop. Not a server farm. Not a GPU cluster. A regular machine. This post is about what that means for you — and what it means for the environment.
What most AI math tools actually cost
When you type a question into an AI-based math tool, your request travels to a data centre, runs across specialised hardware drawing hundreds of watts, and returns a result seconds later. The energy cost of a single AI query for a math problem is roughly equivalent to running a lightbulb for several seconds. Multiply that by millions of queries per day and the environmental footprint becomes significant.
This isn't a criticism of AI in general — it's the right trade-off for tasks that genuinely need it. But solving a quadratic equation doesn't need it. Computing an integral doesn't need it. These are problems with exact mathematical answers that have been solvable by computers for decades. Using a heavyweight AI system to answer them is like using a cement mixer to stir a cup of coffee.
How Zeretis is different
Zeretis uses a fundamentally different approach to computation — one that doesn't require specialised hardware, doesn't need a data centre, and doesn't burn significant energy per query. The result is a solver that uses approximately 99.9999% less energy than GPU-based AI inference for the same mathematical result.
That's not a rounding error. It's the difference between an approach that scales sustainably and one that doesn't. At Zeretis, results are also perfectly consistent — the same problem always gives the same answer, which means results can be reused rather than recomputed. This compounds the efficiency further as usage grows.
What this means in practice
For you as a user, the most immediate effect is speed. Problems appear solved almost instantly — there's no spinner, no wait, no loading state. You type, you get the answer.
For the environment, using Zeretis instead of an AI-based math tool has a meaningfully smaller footprint. We're not claiming to solve climate change with a math solver — but we do think that choosing the right tool for each job matters. When a problem has a provably exact answer, reaching for a computationally expensive AI system to approximate it is the wrong choice.
Our approach also means Zeretis doesn't require a large data centre to operate. The solver runs efficiently on standard server hardware — the kind that's far more widely available, more energy-efficient per unit, and doesn't require the rare materials and cooling infrastructure that GPU clusters demand.
No GPU. No approximation. No compromise.
The three things AI-based math tools trade away — energy, exactness, and consistency — are the three things Zeretis preserves. Results are exact, not approximate. Every run produces the same output. And the energy cost per query is a fraction of what AI inference requires.
We built Zeretis this way because it's the right approach for mathematical computation, not because it was easier. Exact answers are harder to produce than statistical ones. But for a tool that students, teachers, and engineers are trusting with real work, exact is the only acceptable standard.