We build custom dashboards and reporting tools for operations, finance and product teams, turning data from your warehouse and business systems into screens people can read, filter and act on.

KPI dashboardsOperations control roomsEmbedded analytics

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Dashboards and AnalyticsWhat we build

01KPI dashboards

02Operations control rooms

03Embedded analytics

01Our Approach

We design every chart to answer a question someone asks daily, and make every number traceable back to its source.

02Overview

Dashboards built around decisions, not charts

01KPI dashboards
02Operations control rooms
03Embedded analytics

Dashboard work runs the full path from raw data to a screen someone trusts. We define the metrics with your team, model and aggregate the data, build the API that serves it, and design the interface in React with charts, tables, filters, drill-downs and exports. Access rules decide who sees which rows, and every figure links back to the query that produced it.

A custom build is the right call when a general BI tool cannot express your workflow, when the dashboard is part of a product your customers use, or when people need to act on the data and not only look at it. We start with the decisions each role makes, sketch the screens around those, and validate with real data before polishing visuals.

Talk to us about your project

03What We Do

Capabilities, End to End

Metric definition

We work with finance, operations and product leads to agree on what each KPI means, how it is calculated and which source is authoritative, then document it.

Custom charts and tables

Charts in D3.js, Recharts or Apache ECharts and data grids with sorting, grouping, pinned columns and virtual scrolling are built to your design system, not a vendor theme.

Drill-down and filtering

Global filters, date ranges, segments and saved views let a user move from a company-wide total to a single record without leaving the page or losing context.

Live data streams

Operational screens update over WebSockets or server-sent events, with thresholds, alerts and stale-data indicators so an operator knows when a number can be trusted.

Data access control

Row-level and column-level permissions are enforced in the query layer, so regional managers, executives and external partners each see only the data they are entitled to.

Exports and scheduled reports

PDF and CSV exports, scheduled email reports and shareable links reproduce exactly the filters on screen, which keeps board reports and audits consistent with the dashboard.

04In Production

What It Looks Like When It Ships

app.novapay.io/overview

Live

Nova Pay

Accounts / Overview

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⌘K

WORKSPACE

Accounts

Payments

Cards

Invoices

Reports

Team

Settings

Usage

72% of plan

Overview

Updated 2m ago

7d30d90d

Export

Balance

$12,480.20

+4.2%

Transactions

3,904

+11%

Approval rate

98.6%

+0.3%

Disputes

0.4%

-0.1%

Balance over time

This periodPrevious

Breakdown

Operating

61%

Reserve

24%

Payroll

15%

Activity

Payout scheduled - $18,400

6m ago

Card approved - A. Rivera

22m ago

Customer

Detail

Amount

Status

Salary

Deposit - Acme Inc.

+$4,200.00

Cleared

Spotify

Subscription

-$9.99

Posted

Alex Rivera

Transfer

+$250.00

Pending

AWS

Infrastructure

-$1,204.10

Posted

KPI dashboards

Operations control rooms

Embedded analytics

Drill-down chartsSaved viewsThreshold alertsOne-click export

05What You Receive

Handed Over, Ready to Run

SCROLL

Deployed dashboard application

Documented metric definitions catalog

Data models and aggregation queries

Reusable chart and table components

Role and data permission matrix

Export and scheduled report templates

Data source connectors and pipelines

Source code and query documentation

06How It Runs

From Kickoff to Launch

Metric workshop

Data layer

Prototype with data

Build and verify

Metric workshop

We sit with each role to learn which decisions they make and what they check first, then define the metrics, dimensions and sources in a catalog everyone signs off on.

Data layer

Engineers profile the source systems, design aggregate tables or materialized views for the heavy queries and build the pipelines, reconciling totals against the reports you already trust.

Prototype with data

Layouts are prototyped with real figures instead of placeholders, because outliers, empty states and long labels only show up with production data, and users review them early.

Build and verify

Screens are built, connected and load tested, every number is checked against its source query, and permissions are tested per role before the dashboard is released to the wider organization.

07Technology

The Stack We Use

InterfaceReactTypeScriptTanStack TableAG GridTailwind CSSDataPostgreSQLClickHouseSnowflakeBigQueryRedisInterfaceReactTypeScriptTanStack TableAG GridTailwind CSSDataPostgreSQLClickHouseSnowflakeBigQueryRedis

VisualizationD3.jsRechartsApache EChartsMapbox GL JSPlatformNode.jsGraphQLWebSocketsAWSDockerVisualizationD3.jsRechartsApache EChartsMapbox GL JSPlatformNode.jsGraphQLWebSocketsAWSDocker

08Who It Is For

Built for Teams Like Yours

FLAGSHIP

Your team decides from spreadsheets stitched together by hand

Numbers live in several systems, someone exports them every reporting cycle, and by the time a report circulates it is already out of date. You need one place where each role sees current figures, can investigate a change and trusts that the definitions match across departments.

Executive KPI overviews

Logistics and fleet monitoring

Financial reporting with audit trails

Customer-facing analytics in SaaS

Sales and pipeline reporting

Support and service desk metrics

09Questions

What buyers ask before they start

If yours is not here, ask us directly. The first conversation is free and useful whether or not we work together.

Book a call

If analysts exploring data are the audience, a BI tool is often the better and cheaper answer, and we will say so. Custom makes sense when the dashboard must live inside your own product, match your brand, enforce complex permissions, update live, or let people act on what they see, for example approving, assigning or editing records. Per-viewer licensing at scale is another common reason.

Yes. We read from warehouses such as Snowflake, BigQuery and Redshift, from operational databases, and from the APIs of tools like your ERP, CRM or billing system. Where sources disagree, we agree on which one is authoritative for each metric and document it. If you have no warehouse yet, we can build a lean reporting database fed by scheduled or streaming pipelines as part of the project.

The browser should never receive more rows than it can draw. Heavy calculations run in the database against pre-aggregated tables or a columnar store such as ClickHouse, results are cached, and tables page or virtualize their rows. Charts downsample long series, and filters query the server instead of loading everything up front. We test with production-sized data before release, not with a sample.

Yes, this is one of the main reasons to build custom. The analytics become components in your own application, styled with your design system and served by your API, so customers never leave the product. Tenant isolation is enforced in every query, each customer sees only their own data, and usage limits or plan-based features can decide which reports a given account is able to open.

Freshness is a design decision made per metric. Operational figures such as open orders or vehicle positions can stream to the screen as events arrive. Financial and strategic metrics are usually refreshed on a schedule, because they depend on reconciled data. Every panel shows when it was last updated, so nobody mistakes an hourly figure for a live one, and stale sources are flagged visibly.

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