Foresight by Lightning Rod

Predict anything with AI

API

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Artificial Intelligence

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calibrated forecast api is a sharp angle, way more usefull than asking a generic llm 🔮 whats the use case ppl reach for first?

I like that you're tackling forecasting as its own problem instead of assuming a general-purpose LLM is the right tool for everything. An OpenAI-compatible API with lower inference costs also makes it much easier for developers to experiment without reworking their existing workflows. Beyond sports and politics, which real-world forecasting domains have you found Foresight performs especially well in, and are there any areas where you'd still recommend using a frontier model instead?

Really like this idea! Clean, practical, and solves a challenge that almost every AI team faces. Looking forward to seeing how this evolves—best of luck with the launch! 🚀

Congrats on the launch! 🚀 Training data is still one of the biggest bottlenecks in building reliable AI systems, so it's great to see a solution focused on turning real-world data into high-quality, traceable datasets instead of relying on manual labeling. The provenance and quality-scoring features really stand out. Excited to see how teams use this for domain-specific fine-tuning. Wishing you an amazing launch! 👏

The OpenAI-compatible API shape seems like a smart choice here, because forecasting is often something an agent wants to call inside a larger workflow, not a separate dashboard.

One product detail I’d look for as a developer is whether the calibration stays visible after the API call: confidence interval, data/source freshness, and a short reason why the model thinks the probability changed. For agent workflows, the forecast is useful, but knowing when not to trust it may be even more useful.

Predicting anything is a bold promise, and I like that you're not boxing it into one niche like sports or markets. That open-ended framing is fun to play with.

OpenAI-compatible is a smart wedge — I can point an existing client at it and test forecasting without rewriting my stack. The thing I'd check first: when the model is genuinely uncertain, does the API return a calibrated probability or confidence interval I can threshold on, or just a point prediction I have to trust blindly? For anything I'd wire into a real workflow, knowing when to ignore the forecast matters more than the forecast itself.

Really interesting product. Do you see Foresight being used for cybersecurity risk prioritization... for example forecasting whether a vulnerability or exposed service is likely to be exploited within 30/60/90 days based on threat intel, EPSS/KEV, asset criticality, and exposure context? Curious what inputs improve calibration most, and how you handle high-consequence cases where a ‘low probability’ event still needs action.

One thing I'm curious about: if a lot of forecasting agents end up using the same underlying model, their predictions naturally become more correlated. That's fine for a single application, but it changes things in systems that rely on independent signals. Has that come up with customers using Foresight at scale? Congrats on the launch!

Love that this is trained on real-world outcomes rather than just text patterns, making it a purpose-built forecasting layer that general LLMs simply cannot replicate.

Ensembling to get bands around the probabilities is exactly the part I'd reach for, since in a decision loop a miscalibrated tail costs you asymmetrically more than a wrong point estimate. The tension I keep hitting: ensembling N models fights the cheap-inference pitch the moment an agent is forecasting thousands of markets a run. Do you expose the band per-call in the API response, or is it a heavier mode you opt into when the stakes justify the extra passes?

The OpenAI-compatible interface is the right call. It means teams can drop this into existing agent pipelines without touching their orchestration layer. We've hit the same problem with general LLMs hallucinating probabilities. They'll say '70% chance' with no calibration behind it. How do you handle calibration drift as events resolve? Is accuracy validated continuously against a live benchmark, or is it a periodic evaluation cycle?

Interesting idea. is there a public demo where developers can try a few forecasts before integrating the API?

Love the focus on forecasting agents. This feels like a missing piece for agentic workflows. Congratulations!

Have you compared it against prediction markets directly, or only against LLMs?

The calibration angle is the part that actually matters, and the part most forecasting tools skip. When we plugged raw LLM probabilities into a decision loop, the point estimates were fine but the confidence was wildly off at the tails, so the expected-value math downstream was garbage. Two things I'd want to know: do you return a calibration band or just a point probability, and how does calibration hold up under regime shift, when the future stops resembling the outcomes you were scored on?

can developers fine-tune forecasts for specific domains like fiance or healthcare?