OpenSymbolic makes the model write a plan, then lets ordinary code execute it, so the work that can’t afford to be wrong becomes cheap, repeatable, and auditable. It runs alongside your existing agents, on the workflows that actually need it.
Standard agents are great for open-ended, low-stakes work: drafting, summarizing, exploration. But when a workflow is well-defined and a wrong answer is expensive (contracts, compliance, financial extraction), improvising through it is how you get silent errors and runaway bills. OpenSymbolic is the engine for that work.
The high-stakes, defined quadrant (document QA, compliance, financial extraction) is where OpenSymbolic runs.
We help you put the right engine on each workflow, and run the high-stakes ones deterministically.
BoonAI tested OpenSymbolic against their production RAG on 104 real queries over their own document corpus, scored by an independent LLM judge.
Same or better answers: their data, their judge, not ours.
Biggest gains on multi-hop and document-comparison queries, where the baseline’s context overflows caused silent failures.
We deploy on a single high-value workflow and measure it head-to-head against what you run today: cost, error rate, latency. All we need is one point-of-contact engineer and a representative set of queries. The framework is open source and free (MIT, pip install opensymbolicai-core), so your team can read every line; no black box, no lock-in. We work with a small number of design partners on paid pilots.
Book a pilotAn independently-verified framework and 49 production-grade tutorials, built by a small team, in the open. We work with a handful of design partners at a time, so you get direct access and a real say in the roadmap.
More about us →The winners won’t have the biggest model budget; they’ll spend it precisely. We make that precision your default.
Book a pilot