Tepia builds analytics and dashboards on your existing systems: one trusted view, metrics defined once, alerts included. Live in 6 to 12 weeks.
Tepia builds analytics and dashboards on top of the systems you already run: one trusted view of operations, sales or devices, with metrics defined once and screens that answer questions without a data team. US led, typically live in 6 to 12 weeks.
Tepia defines each metric once, computes it in one place and reuses it everywhere, which is what makes a dashboard trusted.
Read: Database Design & Management
The first trusted dashboard lands in 6 to 12 weeks, starting with the audience that uses it daily to flush out data problems fast.
Read: Systems Integration Services
Operations, executive, customer facing and device dashboards share one data foundation, so numbers agree across every screen.
Read: Real-time Applications
Tepia wires thresholds to email, Twilio SMS and push, so exceptions reach people instead of waiting on a page view.
Read: Internal Tools DevelopmentIndustry figures Tepia plans around when scoping analytics work.
Firms that make decisions on data outperform peers on productivity and profitability, per widely cited MIT and McKinsey research.
Nearly half of executives say they lack confidence in their own reported data, according to KPMG survey research.
Mobile generates roughly 60 percent of web traffic per Statista, so Tepia dashboards are built to read well on a phone.
Most dashboards die one of two deaths: nobody trusts the numbers, or nobody opens the page. Both trace to the same root, metrics defined in five places that disagree. Tepia fixes the root first: each metric gets one definition, computed in one place, and every screen reads from it. When revenue on the dashboard matches revenue in QuickBooks, people come back.
Tepia builds dashboards as products, with the same design discipline as its client apps: the three numbers that matter this morning on top, the drill downs behind them, and nothing decorative.
| Dashboard type | Audience | Typical content |
|---|---|---|
| Operations | Managers and dispatchers | Today’s jobs, exceptions, queue health, live status |
| Executive KPI | Owners and leadership | Revenue, retention, pipeline, trend lines with targets |
| Customer facing | Your clients, inside your product | Their usage, results and reports, embedded and branded |
| Device and IoT | Field and facilities teams | Telemetry, faults, uptime, thresholds and alerts |
All four share one data foundation, so a number means the same thing on every screen. Customer facing analytics often becomes a selling point of the product itself.
The work starts with where your data lives: the product database, Salesforce or HubSpot, QuickBooks, device feeds, and the spreadsheets that fill the gaps. Tepia builds pipelines that land those sources in one reporting store, on a schedule that matches how fresh each number needs to be, with reporting kept off your production hot path.
Then come the definitions. What counts as an active customer, when is a job late, which revenue counts this month. Tepia writes these down with your team in Discovery, because every dashboard argument is really a definition argument.
Discovery interviews the people who will open the dashboard and documents the decisions each screen must support, producing User Stories per audience and a metric dictionary. Design delivers wireframes of the actual screens with real sample data, because layout arguments are easier to settle before development.
Development builds the pipeline, the semantic layer and the React dashboards, with alerts through email, SMS via Twilio or push where thresholds matter. Testing reconciles dashboard numbers against source systems line by line before anyone is asked to trust them. Training and Support follow Tepia’s standard process, with a US based project manager throughout.
The first trusted dashboard typically lands in 6 to 12 weeks: two to three weeks of Discovery and definitions, then build in stages, one audience at a time. Starting with operations usually wins, because daily use flushes out data problems fast.
The work pairs with database design when the underlying model needs help, and with real time applications when screens must update themselves.
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