Putting 10 years of monthly investor reporting on autopilot
Rig's founder Toby has written investor reports every single month for a decade, roughly 120 updates, without fail. Good month or bad, his investors got the picture. The hard part was never the writing, it was pulling together all the data and context behind it, and finding the hours to do it well. Here's how he runs that whole process on Rig now, automatically.
How I do investor reporting at Rig
The challenge was never the writing, it was the data and the time.
Ten years and roughly 120 reports, through Lingumi and now Rig, kept a loyal group of investors with the journey through good months and tough ones. But every report meant the same uphill pull.
Top-line numbers, cash flow, the funnel, product usage by customer, all living in different tools and never joined up in one place.
The answer was often no. Reports got scrambled together at night, or went out below the standard Toby and his investors expect.
Every report needed both the numbers and the story behind them. Doing that by hand, monthly, for ten years, is relentless.
The monthly report writes itself
Rig acts as a data brain that understands the business, giving not just the top-line numbers but the story behind them. Three pieces make that work.
Rig ingests data from finance, contracts, product analytics, the CRM, calls and banking, then builds context, semantic metrics and usage rules on top so the data is ready for AI to use.
A monthly investor skill, saved on Rig so the whole team can access it, tells Claude exactly what to produce and how to process it. Toby just runs it.
Rig pulls the usage rules, semantic metrics and live data and Claude returns the finished update: headline numbers, business summary, features and how customers are using the product.
And investors can go deeper themselves
The monthly report is the start. Investors can serve their own reporting needs and figure out how to help the business, without having to ask.
Rig's investors sit in a Slack channel where they can talk to Rig directly and query internal data, with governance restrictions on what they can see.
One investor pulled pipeline status on a live deal one morning, found a way to help move it along because he knew someone there, and got accurate answers, all without Toby having to intervene.
The infrastructure that makes it reliable
So none of this depends on babysitting a janky skill file. Rig brings the data together, governs it, and keeps the whole pipeline reliable.
Bring an existing warehouse or provision one Rig manages. Connect an LLM, then connect sources: CRM, emailing tool, call recordings, banking data, HubSpot and more.
Rig pre-builds the aggregations, tables, models and metrics so AI doesn't YOLO API calls, burn tokens and get joins wrong. The data is contextualized behind the scenes.
A context graph maps how all the data connects and the rules for using it, down to which fields are PII or sensitive and excluded for certain users.
On top, Toby builds data apps (like a funnel metric tree) and reusable skills, including the monthly investor skill that guides Claude through each update.
The result: Toby runs one skill, Rig does the work behind the scenes, and the monthly update arrives prepared, along with tools investors can use to go deeper on their own.