SightSpinner

SightSpinner is an AI forecasting platform that turns your own data into predictive models you can actually question. You connect a data source, describe in plain language what you're trying to forecast, and the AI builds the model, wires up the visualizations, and encodes the underlying logic for you. From there you run what-if scenarios and watch how changing a single assumption ripples through the whole picture in seconds. The headline promise is accurate forecasts across business KPIs, market prices, and real-world events, without needing a data science background to get there. The product describes itself as forecasting you can act on, where the model is built from your own business logic rather than a generic template you have to bend to fit.

The problem it goes after is that most forecasting still lives in fragile spreadsheets or waits on a specialist to build. Business owners know their operations drive their financials, but connecting the two into a model that stays current as reality changes is slow, and the resulting spreadsheet tends to rot the moment someone changes a row. SightSpinner reads your data, infers the relationships, and stands up a forecast built from how the business actually works, so the output reflects real drivers instead of a gut-feel guess. It's positioned as a forecasting tool that understands the mechanics of your business rather than fitting a curve to a single line.

The workflow runs in four steps. You plug in a source, whether that's a spreadsheet, a database, an API, or a one-off file, then tell the AI what you want to forecast and which signals might drive it. It analyzes the historical patterns, builds the model, and hands back base, upside, and downside cases you can compare. From there you inspect the drivers, swap assumptions, and re-run, using a visual drag-and-drop interface to shape components that represent your real processes and watch data flow between them. You can also describe transformations and custom plots in plain language and let the AI agent wire them up for you. The interface leans on components you connect together to mirror how your operation runs, so the model reads less like a formula and more like a map of the business.

It's organized around two main solutions. For small and mid-sized businesses there's financial forecasting that links department-level metrics from marketing, sales, and operations directly to revenue, cost, and cash-flow outcomes, so you can project runway and working capital and see the impact of hiring, pricing, or marketing changes weeks in advance. For markets there's securities forecasting, a bottom-up approach that blends fundamentals, alternative data, satellite and web signals, and macro into testable, explainable models for equities, commodities, and rates. The same engine underpins both, framed generally as forecasting anything you can bring data to.

A theme running through both solutions is explainability. The platform makes a point that every output traces back to the assumptions and the data behind it, so there are no black boxes and you can walk an investment committee or a board through exactly why the model says what it says. It generates base, upside, and downside cases meant to be defensible with real driver data rather than a hunch, and re-running as fresh data arrives shows how a thesis holds up over time and where it starts to break down. That auditability is the part it leans on to separate itself from a one-off prediction you can't interrogate. Every forecast comes with the logic that produced it, so when a number looks off you can trace it back to the assumption or the input driving it instead of guessing.

Who it's for is finance teams, founders, and operators who want defensible forecasts without hiring a dedicated modeler, along with analysts building bottom-up cases for equities and commodities. The recurring pain it names is anyone who has bolted forecasting onto spreadsheets and watched them turn into an unmaintainable tangle nobody quite trusts. Because the AI handles the modeling, the pitch is that you bring the data and the domain knowledge and spend your time on the decisions rather than the mechanics of building and maintaining the model by hand. It's pitched at the people who make decisions on these numbers, not at data scientists, so the emphasis falls on trust and speed rather than on tuning a model yourself.

Access is freemium. There's a free tier you can start on with no credit card, a self-serve DIY plan at 100 dollars a month with credits you can top up if you run out, and a managed option where the team handles full onboarding and data setup for you and offers a free consultation. Credits roll over month to month and unused ones don't expire, and there's an API with your own keys for programmatic access alongside the interface. That range makes it approachable to try on a small forecast before committing to the DIY plan or the hands-on setup.