Data is only a product advantage when it's presented in a way users can act on.
Products built around data insights — analytics platforms, intelligence tools, reporting dashboards — require the right stack: efficient data pipelines, the right database model for analytical queries, and the charts and tables that make data accessible. We build the data-driven products that make raw data into value.
Data that exists in raw form — transactions, events, interactions — but isn't being surfaced to users as actionable insights within the product
Data products fail in two ways: the data pipeline can't deliver data fast enough to be useful, or the data is delivered but presented in a way that requires expertise to interpret.
The pipeline problem: OLTP databases (Postgres, MySQL) are optimized for transactional workloads — small reads and writes, row by row. Analytical queries (aggregations, group-bys, time-series, window functions across millions of rows) compete with the transactional workload for resources. Products that run analytical queries directly against the production transactional database discover this at scale: the dashboard loads slowly, and the slow queries degrade the product experience for transactional users.
The solution: a read replica for heavy analytical queries, or a separate analytical data store populated by a pipeline from the production database. The right approach depends on query volume and complexity.
The presentation problem: Even when data is available, the default presentation (a table of numbers) requires the user to do mental work to extract meaning. The product that presents "your conversion rate dropped from 18% to 12% in the last 7 days" is more valuable than the product that presents raw event counts the user has to compute conversion from.
Data product with efficient pipelines, analytical queries tuned for performance, and user-facing dashboards that turn raw data into decisions
Data model for analytics
If the data will support analytical queries, the schema is designed for both transactional performance and analytical efficiency — proper indexing, materialized views for expensive aggregations, and read replica routing for heavy queries.
Time-series data handling
Products with time-series data (events, metrics, logs over time) designed with time-indexed tables and efficient aggregation queries.
Dashboard with Recharts/Chart.js
User-facing visualizations: line charts for trends, bar charts for comparisons, funnel charts for conversion, and tabular data with sorting and filtering.
Computed metrics
Derived metrics calculated in the application layer: conversion rates, growth rates, rolling averages, cohort retention. The user sees the insight, not the raw data the insight is derived from.
Export and API
CSV export of dashboard data for users who want to analyze further. JSON API for programmatic access to the metrics.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
Data product with efficient pipelines, analytical queries tuned for performance, and user-facing dashboards that turn raw data into decisions
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Data product scope is defined by the data sources, the queries, and the visualizations. Fixed price after the data model design.
Questions, answered.
For most B2B SaaS applications, Postgres with a read replica handles analytical queries well up to hundreds of millions of rows. For larger scales, DuckDB (embedded analytical database), ClickHouse, or BigQuery become relevant. The right choice depends on data volume and query patterns.
AI-generated natural language summaries of dashboard data (e.g., "Your MRR grew 12% this month, driven primarily by expansion revenue") are a natural product addition once the data infrastructure is in place. The OpenAI API is the implementation path.
Dashboard + data pipeline + analytical queries: from $25k. Complex multi-source data product: from $45k. Fixed-price.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.