VeriLM puts your question to multiple AI models independently, then rigorously validates their answers against each other. You see where they agree, where they disagree, and why — so you can trust the result.
Currently in private beta with researchers, engineers, and professionals.
Same question. Three models. Each asked on its own, the way you'd normally use them — on their paid plans.
| Model | Target Date 2025 | Target Date 2030 | 80/20 Stock/Bond |
|---|---|---|---|
| GPT-5.2 Thinking | 7.87% ✓ | 8.66% ✓ | 11.87% ✓ |
| Sonnet 4.6 | 8.4% ✗ | 9.3% ✗ | ~11.9% |
| Gemini 3.1 Pro | 7.45% ✗ | 8.66% ✓ | 11.87% ✓ |
| VeriLM Verified | 7.88% ✓ | 8.66% ✓ | 11.87% ✓ |
The dangerous takeaway? That one model is infallible.
The outlier was identified and set aside. The B/C consensus was adopted as the verified result.
Same question — GPT-5.2 got it right. GPT-5.4 got it wrong — with a real number from the right source for the WRONG time period.
You're not imagining it — the best AI models are still “confidently wrong” often enough that no single answer can be fully trusted. Yet, the question isn't whether to use them — that ship has sailed. The question is how to use them safely. That's what VeriLM does.
Before anything runs, VeriLM works with you to understand what you actually need. It asks the right questions, clarifies ambiguities, and crafts a precise prompt — the kind most people don't have time to write themselves.
That prompt goes to multiple models simultaneously, each working the problem in isolation. No model sees another's output. This eliminates the groupthink that undermines simpler approaches.
An independent model evaluates all responses — confirming where they converge, identifying where they disagree, and explaining why. You get a verified answer with a clear confidence assessment.
Then keep going. VeriLM is multi-turn — once you have your validated answer, you can follow up with the full panel or drill into a single model for focused exploration.
Complex derivations, trade-off analyses, and technical problems where a single model's blind spots can send you down the wrong path for days.
Differential diagnoses, drug interaction checks, and literature synthesis — areas where hallucinated confidence is genuinely dangerous.
Modeling assumptions, regulatory interpretation, and quantitative reasoning where models routinely produce plausible but contradictory conclusions.
Case law analysis, contract review, and regulatory questions where citation accuracy and logical consistency are non-negotiable.
Everything above is about verification — getting reliable answers to known-hard questions. But we're curious about what happens at the boundary, where nobody knows the answer yet. Independent models reasoning about open problems might disagree in ways that are more useful than any single model's confident answer. If you're a researcher working at the edge of your field, we'd love to find out together.
Rich inputs. PDFs, images, tables, and text — bring the actual source material.
Code execution & web search. Models can write and run code, and search the web when a problem requires it.
Frontier model access. Claude Opus 4.6, GPT-5.4 Pro, Gemini 3.1 Pro — used together or individually.
Right-sized analysis. Not every question needs frontier models. VeriLM scales from fast to thorough.
Multi-turn conversations. Follow up with the full panel or drill into a single model for focused work.
Your data stays yours. Queries are not used to train any model.
If you've been burned by AI hallucinations — or if you've just been doing the multi-tab comparison dance — we built this for you.