Do we need a data warehouse first?

Not necessarily. At moderate data volumes we can model directly against replicated source data. A warehouse becomes worthwhile as volume and source count grow, and we will say when you have crossed that line rather than selling it upfront.

How do you handle metric definition disagreements?

By forcing the decision early and writing it down. Most dashboard projects fail on undecided definitions rather than on engineering, so the definitions are version-controlled and visible in the interface.

How is this different from a standard BI tool?

The dashboard layer is similar. The difference is the work underneath — unifying systems that do not agree with each other — plus a question interface for people who will never write a query, and commentary that explains movement rather than just plotting it.

How fresh is the data?

Configurable per source. Operational metrics usually run near real-time; anything pulled from a rate-limited third-party API syncs on a schedule. Every panel shows its own last-updated timestamp so nobody acts on stale numbers unknowingly.

AI Analytics Dashboards · SaaS

AI Analytics Dashboards for SaaS

Everyone quotes a different number for the same metric.

Finance, sales, and product each compute retention slightly differently, and every meeting starts by reconciling the discrepancy. A semantic layer defines each metric once, so the dashboard, the board pack, and the sales forecast all agree by construction.

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Data freshness: LiveData freshness
Source of truth: OneSource of truth
In plain language: AskIn plain language

Typical stack

Next.js
Postgres
dbt
OpenAI
Recharts

Use cases

What this handles for SaaS companies

Define MRR, churn, retention, and expansion once and use them everywhere
Combine product usage, billing, and CRM data into a single account view
Track cohort retention and expansion without rebuilding the query each quarter
Detect and flag unusual movement in usage or revenue automatically

One agreed definition per metric, so meetings start from the number rather than about it.

Context

The saas problem underneath it

SaaS teams rarely lack AI ideas. They lack the specialist capacity to build them properly alongside an existing roadmap — retrieval that is actually accurate, agents that are safe to let loose in a customer account, and inference costs that do not quietly destroy gross margin.

The AI feature on the roadmap keeps slipping behind committed work
A prototype exists but nobody is confident enough to put it in front of customers
Inference cost per account is unmodelled, so pricing is guesswork
Support volume grows with the customer base and headcount follows

What we build

Inside an AI analytics dashboard

We define the metrics before we build anything, because most dashboard projects fail on disagreement about what a number means rather than on engineering. Once definitions are agreed, we pull data continuously, model it once, and expose both a fixed executive view and an ad-hoc question interface.

Connectors pulling from CRM, billing, ads, support, product, and operational databases
A single semantic layer so a metric means the same thing everywhere it appears
Live executive dashboards with drill-down to the underlying records
Natural-language querying for ad-hoc questions without SQL
Anomaly detection that flags unusual movement rather than waiting to be asked
Scheduled digests into email or Slack with plain-language commentary on what changed

Deliverables

What ships with the engagement

Agreed metric definitions documented and version-controlled
Data pipeline with freshness monitoring and failure alerts
Dashboard covering the decisions you actually make weekly
Natural-language query interface over the modelled data
Scheduled digest configuration per team

FAQ