About Hurricane Works

We add AI to products and workflows where the output has to be trustworthy — assistants that answer from your own data, tools that draft content, classify information, or summarise what customers are saying.

The substance is in the guardrails. A language model will happily produce a fluent, confident answer with nothing behind it: fine for a demo, dangerous in production. So we build our AI features around an evidence check — the system has to ground each answer in a real result from your data before it can present it, and it visibly holds back when it can't. We handle the model's rough edges too: retrying and falling back across models when one is slow or overloaded, and capping oversized inputs so a single large response can't blow the context.

We validate the same way we build everything — against a fixed set of real questions with known answers, scored automatically, so gains are measured rather than assumed.

We build models that turn a business's own history into operational decisions — how much stock to hold, when to reorder, what cash is likely to land and when, which leads are worth chasing.

The approach is deliberately grounded, not black-box. For demand and inventory work we separate the signal in the data — the underlying trend, the seasonal shape of the year, and one-off spikes — and forecast each explicitly, with prediction intervals so you see the range of likely outcomes rather than a single point. From that we derive service-level reorder points and safety stock: enough cover to hit the availability you're targeting without tying up cash in stock you don't need. Where it earns its place, we fold in an external signal — weather, say — as a bounded influence: enough to nudge a forecast, never enough to let a noisy variable run it.

Before any of it goes live, we backtest against your own history — training on older data, checking the forecast against what actually happened, then replaying past decisions to answer one question: would the model have agreed with the calls you actually made?

We help you find the right prospects and reach them with a message that proves you've actually looked at their business — then automate the repetitive parts so your team spends its time on conversations, not admin.

Underneath, it's a pipeline. We build targeted prospect lists and enrich them through several data sources in sequence — each filling the gaps the last one left — so you end up with complete, deliverable contact records rather than a patchy spreadsheet. The outreach is genuinely personalised: messages lead with the prospect's own figures and situation, drawn from live research and grounded strictly in real facts rather than invented flattery. From there the system schedules and sends follow-up sequences, respects send limits and unsubscribes, and tracks opens, replies and conversions so you can see what's actually working.

It's built to run at scale without falling over — the same approach can keep a dataset of tens of thousands of records fresh while making a fraction of the data-provider calls a naive version would.