We were facing the same challenge and had to build something that delivers consistent, near-99.99% accuracy — it’s called LiveFix (livefix.ai).
It’s a drop-in proxy between your app and your LLM. Every response is corrected during generation, not after. One API call. No retries.
Each response returns with a trust status: *verified*, *needs_review*, or *requires_human* — no silent failures.
We’re seeing a ~99% pass rate across thousands of clinical documents. Budget models are matching premium-level accuracy at ~75% lower cost. Benchmarked against top-tier budget and frontier models, with performance improving across the board — benchmarks are published.
The approach that has worked for us in production is correction during generation, not after.
The model verifies its output against the rules in the prompt as it generates and corrects itself within the same API call — no retries, no external validator. If there are still failures the model cannot fix at runtime, those are explicitly flagged instead of silently producing wrong output.
This does not mean hallucinations are completely solved. It turns them into a measurable engineering problem. You know your error rate, you know which outputs failed, and you can drive that rate down over time with better rules. The system can also self-learn and self-improve over time to deliver better accuracy.
We built a correction layer that does this — the model verifies its output against your prompt during generation, not after. Same API call, no retries.
Budget models without it: 40-50% accuracy. With it: 95.7% on 10k+ clinical documents. Hallucinations aren't eliminated — some might still fail — but every failure is explicitly flagged. No silent errors. and it improves over time to give you better results next time.
It doesn't make hallucinations "solved. 100%". It makes them an engineering problem with a measurable - very low error rate you can drive down over time.
We're calling it LiveFix — livefix.ai. Benchmarked across all frontier and budget models.
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It’s a drop-in proxy between your app and your LLM. Every response is corrected during generation, not after. One API call. No retries.
Each response returns with a trust status: *verified*, *needs_review*, or *requires_human* — no silent failures.
We’re seeing a ~99% pass rate across thousands of clinical documents. Budget models are matching premium-level accuracy at ~75% lower cost. Benchmarked against top-tier budget and frontier models, with performance improving across the board — benchmarks are published.
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