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    // For B2B SaaS

    Let every team answer their own data questions

    Rig sits on the Snowflake, BigQuery or Databricks you already run and gives sales, finance, CS and marketing plain-English access to it. The numbers are defined once, so everyone gets the same answer. No rip and replace, no new modelling project.

    // Birdie, homecare software
    7 to 19
    active Rig MCP users, month on month
    4,000+
    MCP tool calls in the last 30 days
    15+
    dashboards and agents in weekly use

    // In plain terms

    Rig is AI-native data infrastructure. Think Stripe or Vercel, but for data.

    We connect all your sources, build a brain that sits on top of them, and let your team and your AI tools reach that data safely. For a B2B SaaS company that means the warehouse you already pay for stops being a thing only the data team can use, and net revenue retention, churn and CAC payback mean the same thing in every deck.

    // What you'd build

    What a B2B SaaS company builds first

    These are projects, not features. A company at Series A or later usually has the data already: the warehouse runs, the models exist, the CRM is clean enough. What is missing is a way for the people who need an answer to get one without booking analyst time.

    Net revenue retention, defined once

    NRR, gross churn and logo churn agreed as one definition and certified, so the number in the board pack, the number in the sales deck and the number finance reports are the same number. This is the one that usually has three answers.

    Snowflake · BigQuery · Stripe · Salesforce · HubSpot

    Churn early warning

    Accounts with usage falling three weeks running and a renewal inside ninety days, surfaced before the renewal call rather than found in the post-mortem. Birdie runs churn reporting and escalations through Rig.

    Mixpanel · PostHog · Zendesk · HubSpot

    Win/loss from the whole record

    Why a deal actually died, read across the tickets, the calls, the product usage and the invoices rather than from the two words a rep had time to type into the closed-lost field.

    Salesforce · HubSpot · Zendesk · Stripe · Jiminny

    Seat utilisation and expansion signals

    Seats provisioned against seats actually active, by account and by plan, so expansion conversations start from what the customer is already using rather than from a renewal date.

    Mixpanel · PostHog · Stripe · Salesforce

    CAC payback by cohort

    What each cohort cost to acquire and when it paid back, blended and paid-only, against the pipeline that actually closed rather than against attributed leads.

    Google Ads · Meta Ads · HubSpot · Stripe

    Call coaching from transcripts

    Rep calls compared against the top performers, with objection-handling patterns and coaching opportunities surfaced from outcomes. Novakid coaches country leads this way instead of relying on a manager's memory.

    Jiminny · Fireflies · Granola · Fathom

    The board and investor pack

    Assembled from the source systems against the certified definitions each cycle, rather than rebuilt by hand from last quarter's deck with the dates changed.

    Stripe · Xero · NetSuite · Salesforce

    Governed access for every team and agent

    Rig MCP switched on org-wide so Claude, ChatGPT, Cursor and Codex read the same governed layer a person would, each scoped to its own role and logged. Birdie went from 7 to 19 active users this way in a month.

    Rig MCP · Claude · ChatGPT · Cursor · Codex

    // In production

    "The result is going to be consistent every single time, and now I can't imagine operating without it."

    Judy, Chief Customer Officer at Birdie

    Partner
    BirdieHomecare software · Series B · UK · on Snowflake
    Challenge
    Sales packs needed a manual pull and a slide build before every review. Finance re-derived the same numbers from scratch each cycle because nothing had certified them. Account knowledge lived in one person's head, and marketing reports were out of date by the time they landed. Querying Notion, Snowflake, Intercom and HubSpot through general agent tools gave answers that changed with the phrasing.
    Solution
    A governed context layer over the Snowflake they already ran, with certified metrics across sales, revenue and retention, Rig MCP switched on org-wide, and a workflow builder per function so each team automated its own reporting. The buyers were commercial leads rather than the data team.
    • 7 to 19 active Rig MCP users month on month, across sales, finance, marketing, CS and the exec team
    • 4,000+ MCP tool calls in the last 30 days
    • 15+ dashboards and agents in active weekly use, including 6 live for sales pipeline and forecasting
    • Churn reporting, escalations and commercial attainment now run through Rig rather than through HubSpot's limits

    // In production

    Novakid

    Partner
    NovakidOnline education · Series B · 50+ countries · on BigQuery
    Challenge
    A 5,000-table BigQuery warehouse that only the data team could use, with country leads queueing for conversion answers and sales coaching depending on whichever calls a manager happened to remember.
    Solution
    A governed context layer over the whole warehouse, with Rig MCP given to commercial users directly. Country leads ask their own conversion questions, and call transcripts are compared against the top managers worldwide so coaching comes out of what actually happened on the calls.
    • 5,000+ tables in the governed context layer
    • 10 commercial users building on Rig MCP, up from one
    • 2,500+ MCP tool calls in the last 30 days
    • Conversion diagnostics, call coaching, campaign performance by country and send-time, and forecasting against plan, all self-served

    Where it fits in the stack you already bought

    Rig is headless by design here. Ingestion is optional, because you already have pipelines. If you run a dbt Semantic Layer or Cube project, Rig syncs those models in rather than asking you to define everything twice. We extend, we don't replace.

    What Rig owns is the layer nobody else is maintaining: the context that tells an agent what a table means, which metric is the certified one, and who is allowed to see it.

    // Connects to
    Warehouses
    Snowflake, BigQuery, Databricks, Redshift, Postgres
    CRM
    Salesforce, HubSpot, Attio, Pipedrive, Gainsight
    Calls
    Jiminny, Fireflies, Granola, Fathom
    Product
    Mixpanel, PostHog, GA4
    Delivery
    Linear, Jira, GitHub, Sentry
    AI surfaces
    Claude, ChatGPT, Cursor, Codex, Gemini

    // Proof

    B2B SaaS companies on Rig, and the companies alongside them

    Birdie, Novakid, deskbird, Vita Mojo and Frontline are the SaaS deployments, each sitting on a warehouse the company already had. Every tile says what that company actually does.

    BirdieHomecare softwareNovakidOnline education
    deskbirdWorkplace management
    Vita MojoHospitality tech
    Frontline EducationK-12 school software
    SuriSustainable oral care
    Hunter & GatherFood and supplements
    CleoConsumer fintech

    // Built on Rig

    What B2B SaaS teams build on Rig

    Real examples assembled from Rig's building blocks, starting from the CRM, product and billing data you already have in the warehouse.

    Lost deals · re-analysed
    Acme · £40kRep: PriceNo champion
    Globex · £22kRep: TimingStatus-quo
    Hooli · £31kRep: FeatureBudget cut
    Pattern: 6 of 10 "price" losses were really champion gaps
    App

    Closed-lost & win-loss analysis

    See why deals really die, not just what the rep typed

    Pull the full context around every deal from CRM, support, product and finance, so you can see what actually slowed deals down and what moved win rate.

    Salesforce logoHubSpot logoZendesk logoStripe logoBirdie
    How to build
    Forecast roll-up
    Commit
    £840k
    Best case
    £1.3m
    Coverage
    3.1x
    JanFebMarAprMayJun
    “Commit at £840k, 3 deals flagged: champion quiet, close date pushed twice.”
    Dashboard

    Pipeline forecasts

    A forecast built from deal evidence, not stage percentages

    Roll your pipeline up into a forecast that weighs each deal's real signals (activity, engagement, history), so the number you take to the board is one you can defend.

    Salesforce logoHubSpot logoGong logo
    How to build
    Renewal risk · next 30 days
    Acme CorpUsage −42% · 3 tickets92
    Globex71
    Initech38
    QBR pack generated ↗
    Dashboard

    Churn-risk & renewals prep

    Spot at-risk accounts before the renewal call

    Score renewal risk across product usage, support tickets, CRM and finance, then auto-assemble the renewals or QBR pack so CSMs walk in prepared.

    Zendesk logoStripe logoSalesforce logoBirdie
    How to build
    Account health · Globex Inc
    78
    Health
    Usage+18%
    Support−2 tickets
    Spend+£4k
    Expansion readyPropose Tier 3
    Dashboard

    Account health & expansion

    One health score across product, support and finance

    A live account health view that blends usage, support load and revenue signals to flag churn risk early and surface the accounts ready to expand.

    Segment logoZendesk logoStripe logo
    How to build
    Quote-to-cash
    Closed-won
    Invoice
    Reconcile
    Paid
    Acme
    £42k
    Paid ✓
    £42k collected
    Workflow

    Quote-to-cash & invoicing

    Automate the path from closed-won to cash collected

    A workflow that picks up every closed-won deal, raises and reconciles the invoice across CRM, billing and your ledger, and chases what is overdue.

    Salesforce logoStripe logoXero logo
    How to build
    Board pack · June
    ARR
    £4.1m
    Net burn
    £180k
    Runway
    16m
    JanFebMarAprMayJun
    “ARR up 12% MoM, net burn down 8%, runway extended to 16 months.”
    Report

    Investor & board reporting

    Auto-assemble the board pack from source data

    Generate the monthly board and investor update straight from finance, product and CRM data, with the numbers and commentary built in minutes, not days.

    Stripe logoXero logoHubSpot logominutes, not days
    How to build
    Ask for a dashboard
    MRR by plan, last 12 monthsBuild
    MRR
    £342k
    Churn
    2.1%
    EnterpriseGrowthStarter
    JulSepNovJanMarMay
    Dashboard

    Self-serve dashboards

    Let anyone build a custom dashboard in plain English

    Business teams ask for the report they need and Rig builds a governed, custom dashboard on your modelled data, so the data team stops being a ticket queue.

    Snowflake logoBigQuery logoCleo
    How to build

    Common questions

    No. Birdie runs Snowflake and Novakid runs BigQuery, and Rig sits on top of both. Rig reads your existing models and syncs a dbt Semantic Layer or Cube project in rather than asking you to rebuild it. What Rig adds is the context layer that keeps definitions current as the schema drifts, governed access for people and agents, and the hosting layer for what they build.

    Commercial teams, not the data team. Birdie went from 7 to 19 active Rig MCP users month on month, across sales, finance, marketing, customer success and the exec team, and runs 15+ dashboards and agents weekly. Novakid has 10 commercial users building on Rig MCP, with country leads asking their own questions rather than queueing for an analyst.

    A text-to-SQL tool guesses at your schema every time it is asked, so the same question phrased two ways returns two answers. Birdie hit exactly that before Rig, querying Notion, Snowflake, Intercom and HubSpot through general agent tools. Rig grounds every query in a governed context layer with certified metrics, and runs it in a sandbox with role-based access and an audit trail.

    Yes. Salesforce, HubSpot, Attio, Pipedrive and Gainsight on the CRM side, Jiminny, Fireflies, Granola and Fathom for calls, Mixpanel and PostHog for product analytics, and Linear, Jira, GitHub and Sentry for delivery. Novakid uses call transcripts to compare a rep's calls against top managers worldwide and surface coaching opportunities.

    Rig builds the context layer over the warehouse you already have, you certify the metrics that matter to your commercial teams, then MCP goes on for the org and each function builds its own workflows. Birdie's buyers were commercial leads rather than the data team, which is the usual shape here.

    Access is role based, per person and per agent, so a rep sees their accounts and finance sees the ledger. Queries run in a sandbox and every one is logged. See the security page for what is in place today, including our current SOC 2 status.

    Put a governed door on the warehouse you already run

    Bring your schema. We'll show you what the context layer builds over it on a call.