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Experiência digital e engenharia de produto
Engenharia de painéis SaaS
Painéis SaaS ricos em dados, concebidos para a clareza e a rapidez: visualização de dados, atualizações em tempo real, análise integrada, desempenho multi-inquilino e gráficos acessíveis.
Visão geral
Dashboards are where SaaS products prove their value to customers, and where many of them disappoint. Charts load slowly, numbers disagree with exports, real-time views stutter under load, and screens full of metrics leave users unsure what to do next.
We engineer dashboards as product features with their own architecture: query layers designed for tenant-scale data, visualizations chosen to answer specific questions, real-time updates where they genuinely help, and performance budgets enforced in testing. Clarity and correctness come before decoration.
Aplicações de negócio
- 01
Customer-facing analytics
Give SaaS customers dashboards that show usage, outcomes and trends in their own data, with filters, drill-downs and exports that hold up to scrutiny from their analysts.
- 02
Real-time operational views
Stream live events such as transactions, device telemetry and agent activity into views that update smoothly through WebSockets or server-sent events without overwhelming browsers or databases.
- 03
Embedded analytics for platforms
Embed governed analytics inside a product, using an embedded BI tool or custom components, with tenant isolation, row-level security and consistent metric definitions across every view.
- 04
AI and agent activity dashboards
Show what automated agents and AI features are doing, including actions taken, outcomes, costs and exceptions, so customers can supervise automation and decide how far to trust it.
Abordagem de engenharia
- 01
Define questions and metrics
With product and customer-facing teams, we agree the questions each dashboard answers and the precise definition of every metric it displays.
- 02
Design the data and query layer
Pre-aggregation, materialized views or analytical stores such as ClickHouse are introduced where needed, so dashboards stay responsive as tenants and data volumes grow.
- 03
Choose visualization deliberately
Chart types are selected for the comparison users must make, with accessible color palettes, readable labels, text alternatives and tables available alongside graphics.
- 04
Engineer real time and performance
Updates are streamed or polled according to need, rendering is virtualized and throttled, and performance budgets for load time and interaction are tested continuously.
- 05
Instrument usage and iterate
Product analytics show which views and filters customers use, guiding refinement and the removal of panels that add noise without informing any decision.
Tecnologias e plataformas
Visualization
- D3.js
- Apache ECharts
- Recharts
- visx
- deck.gl
Front end and real time
- React
- Next.js
- TanStack Query
- WebSockets
- Server-sent events
Analytical data
- ClickHouse
- PostgreSQL
- TimescaleDB
- Cube
- dbt
Embedded analytics
- Metabase
- Apache Superset
- Power BI Embedded
- Looker
Os nomes das tecnologias são marcas registadas dos respetivos proprietários e não implicam parceria nem aprovação.
O que recebe
- Metric definitions and dashboard question map
- Analytical query layer with tenant isolation
- Dashboard components built on an accessible charting library
- Real-time update mechanism with load test results
- Performance budgets enforced in continuous integration
- Usage instrumentation and iteration backlog
Perguntas frequentes dos líderes
Should we build custom dashboards or embed a BI tool?
Embedded BI tools deliver breadth quickly and suit exploratory analysis. Custom dashboards suit core product experiences, where design, performance and interaction must match the rest of the product. Many SaaS products use both, and we help decide where each one belongs.
How do you keep dashboards fast as data grows?
By designing the data layer for the queries users actually run: pre-aggregations, appropriate indexes, analytical databases where warranted, caching with clear invalidation, and pagination or sampling for large result sets. We test with realistic data volumes and track performance budgets so regressions are caught before release.
Are your charts accessible?
We design charts to meet WCAG 2.2 AA where it applies: sufficient contrast, labels or patterns in addition to color, keyboard access to interactive elements, and text or tabular alternatives for screen reader users. Complex visualizations receive an explicit accessibility review rather than assumed compliance.
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