The numbers your team chases on the first of the month, on tap. We build the dashboards, automated reports, and KPI alerting that turns the monthly scramble into background noise — so your leads stop pulling spreadsheets and start running on real-time data.
Indicative timeline. Final scope and dates agreed after the intro call.
Live dashboards and Slack-first KPI access mean the answer is one query away — not a tribal-knowledge hunt across three Notion docs and a stale spreadsheet.
Pipeline-to-dashboard or pipeline-to-PDF eliminates the manual copy-paste step that creates most of those errors. The number on the board deck matches the warehouse, every time.
The gap isn’t analytics maturity — it’s whether the right number is in front of the right person at the right time. The build closes that gap without a six-month data-platform programme.
Cleaning happens once at the pipeline, not in every dashboard. Single source of truth, single point to fix when an upstream definition changes.
The build eliminates the weekly and monthly grind so the team you have can support 3× the report consumers without hiring. Scheduled refresh, scheduled delivery, no Friday-night fire drills.
Source connectors (Stripe, HubSpot, GA4, your DB, custom APIs) into your warehouse. dbt-style transforms with tests, scheduled refresh, and metric definitions in version control. The unglamorous foundation that stops every report being a Friday-night fire.
Real dashboards in Metabase, Looker, Mode, Hex, or your tool of choice. Built around the questions your leads actually ask — not “every metric we have access to.” Permissioned by team and role.
Scheduled PDF and email reports for board updates, customer success reviews, and finance close. Generated from the same warehouse the dashboards run on, so the numbers always agree across channels.
Anomaly alerts on the metrics that matter — revenue, churn, signup velocity, error rates, cost spikes. Threshold and statistical anomaly detection per metric, configurable noise floor, owner tagged on the alert. No alert fatigue.
One working session with your ops, finance, and analytics leads. We inventory the sources you actually report on, surface the metric definitions everyone argues about, and pick the dashboard that’ll get used in week two.
Source connectors landing into your warehouse, base transforms with tests, and the first dashboard built against agreed metric definitions. Your leads are looking at real numbers in your BI tool by end of week one.
Scheduled board and ops reports running off the same warehouse, Slack KPI alerts wired with per-metric noise floors, and the second wave of dashboards built around the metrics surfaced in week one.
Expand permissions to the broader team, ship the meta-dashboard (freshness, pipeline health, alert noise), and walk your analytics or ops lead through the runbook. You leave week three with the build live and a baseline you can keep measuring.
Indicative timeline. Many sources, custom-built source systems, or strict compliance requirements can stretch this; we confirm dates after the kickoff session.
Defined scope, agreed in writing before kickoff. No metered hours, no surprise add-ons, no scope creep mid-build. The first week sets the bar — we ship to it, and you see the build running against your real data and live in your BI tool by week three.
You own the repo, the transforms, the metric definitions, and the warehouse. Warehouse and BI vendor spend are billed by the providers directly to your account — no markup, no reseller margin, no vendor lock-in to us.
Investment is sized to source count, transform complexity, and report surface area after the intro call. We come back with one number, in writing.
Start a projectBI tools are the front end. The build addresses what’s behind them: the warehouse plumbing, the transforms, the metric definitions, the scheduled refresh, and the alerting layer most BI tools don’t ship with. We integrate with what you have — Metabase, Looker, Mode, Hex, Tableau — rather than replacing it.
No. The first deliverable is a baseline pass: pull the sources you actually use, standardise the metric definitions everyone argues about, and ship one dashboard against the cleaned data. Cleanup happens at the pipeline, once, rather than in every dashboard.
The warehouse and metric layer are open. Your analysts and ops leads can query directly, build new dashboards, or pipe the same definitions into a notebook — without going through us. The build is leverage, not gatekeeping.
In infrastructure you control. Warehouse, transforms, and orchestration deploy in your cloud account (AWS, GCP, Azure) or your existing warehouse (Snowflake, BigQuery, Redshift, Postgres). We don’t sit in the middle as a data broker.
Three numbers, baselined in week one: hours per week your team spends building reports manually, freshness of the metrics in front of leadership (median lag from event to dashboard), and reduction in “what does this number mean” Slack threads. The dashboard ships with the build.
Most reporting needs are batch — hourly or daily refresh is fine and dramatically cheaper. For metrics that genuinely need real-time (incident dashboards, fraud signals, live ops), we wire streaming or sub-minute polling on the specific metric. We don’t pay for real-time on the 95% that doesn’t need it.
Three options: (1) take the repo and run it internally, (2) keep us on for monitoring, new metric onboarding, and dashboard iteration, (3) scope a follow-on build (forecasting, attribution modelling, customer-facing analytics). No pressure to continue.
Tell us which reports eat the most hours today, and which decisions are running on stale data. We’ll come back within one business day with the next step.
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