Analytics Dashboard MVP for SaaS Metrics

Analytics Dashboard MVP for SaaS Metrics

How a SaaS founder got a single source of truth for product and revenue metrics with an AI-powered analytics dashboard MVP.

Analytics Dashboard
SaaS founders, RevOps teams, Product leaders
$18k–$35k
SaaS
Industry
AI MVP
App Type
3–4 weeks
Timeline
Web
Platforms

Project Overview

1

What We Built

  • An analytics dashboard MVP that pulls in Stripe, product events, and CRM data into curated KPIs with an AI assistant that explains trends in plain language.
  • Leaders wanted to make decisions on clean, near real-time data—without waiting for manual reports or data teams.
  • Ideal for: Founders juggling spreadsheets for weekly metrics, RevOps teams without a consolidated metrics stack, Early-stage products that need dashboards before a full BI team
2

The Challenge

  • Teams scattered metrics across Stripe, spreadsheets, analytics tools, and CRM, making it hard to answer basic questions like MRR or activation rates.
  • Weekly metrics meetings started with data reconciliation instead of decisions
  • No shared source of truth for product and revenue metrics
  • Non-technical leaders couldn’t self-serve insights without SQL or BI tools
3

Our Solution

  • Start with a narrow but opinionated metrics set (MRR, churn, activation, expansion) and add an LLM layer for natural-language questions.
  • Curate a small set of trustworthy metrics instead of a generic BI layer
  • Pre-compute aggregates to keep the interface fast
  • Use AI for explanations and comparisons, not for raw metric calculations
4

Results & Impact

  • Weekly metric reviews shifted from spreadsheet wrangling to decision-making, and the founder used the dashboard in investor updates.
  • Validates demand for a metrics and insights product
  • Replaces scattered spreadsheets with a single dashboard
  • Creates a foundation to productize analytics for customers

Key Features & Scalability

Essential features and scalability measures that make this MVP powerful, user-friendly, and ready to grow.

Learn More

How We Built It

Our step-by-step development process from concept to deployment, ensuring quality and efficiency at every stage.

01

Data Mapping & Contracts

Agreed with the founder on exact metric definitions, event naming, and how to treat edge cases like refunds and discounts.

02

Dashboard UX & Components

Designed layouts around weekly and monthly review rituals with fast switching between cohorts and segments.

03

AI Insights Layer

Added a constrained AI layer that can only query pre-computed metrics and safe views, avoiding direct SQL generation.

04

Design System

Reusable card, chart, and filter components.

05

Wireframes

Founder-friendly layouts optimized for clarity on key numbers.

06

Handoff Process

Close collaboration between data, design, and engineering for metric accuracy.

Core Product Modules

1

User App

  • KPI Overview

    Single-screen view of MRR, churn, expansion, active users, and top-of-funnel metrics with trends.

  • AI Insights Panel

    Ask questions like “Why did MRR move last month?” and get narrative answers with supporting charts.

2

Admin Panel

  • Data Source Manager

    Connect Stripe, Segment, and CRM tools and map fields into a standardized schema.

  • Metric Definitions

    Define and lock metric definitions so everyone across the company sees the same logic and filters.

Technology Stack

We use modern tools to build AI apps that grow with you. We pick the best tools for each project, like React, Next.js, Python, and Go.

nextjs
nextjs
Learn more about nextjs technology
react
react
Learn more about react technology
nodejs
nodejs
Learn more about nodejs technology
postgres
postgres
Learn more about postgres technology

Performance & Security

Built with enterprise-grade optimization and security measures to ensure fast, reliable, and secure operation.

Frontend Performance

Client-side caching for chart data, Skeleton loading states for smooth UX

Frontend Performance

Backend Performance

Pre-computed aggregates, Batched data ingestion jobs

Backend Performance

Database Performance

Partitioned tables where needed, Indexed filters for key segments

Database Performance

Authentication

Role-based access for founders, finance, RevOps, and product.

Authentication

Data Protection

Encrypted connections to all data sources, Row-level access patterns scoped to the company or workspace

Data Protection

Security Best Practices

Principle of least privilege for metric views, Separate read/write connections for ingestion vs dashboard access

Security Best Practices

Project Timeline

1

Week 1 – Metrics & Data Contracts

1 week

  • Metric spec
  • source mappings
  • event schema
2

Week 2 – ETL & Dashboard

1 week

  • Pipelines to ingest data
  • core dashboard screens
3

Week 3–4 – AI Layer & Pilot

1–2 weeks

  • Question templates
  • pilot rollout
  • feedback-driven tweaks

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