Dashboards and data products your teams open every morning.
Pipelines, warehouses, embedded analytics and BI applications, with natural-language querying and AI insights built in.
What is a data and analytics application?
Data and analytics applications turn scattered operational data into dashboards, reports and data products that teams actually use. Eazyware builds the pipelines and warehouse models, custom and embedded dashboards, KPI and alerting systems, and adds an AI layer for natural-language questions, anomaly detection and narrative insights.
| Service line | Digital Product Engineering |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $14,000 |
| Typical range | $14,000 – $56,000 |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Data lives in six systems and one overworked analyst. Decisions wait. Dashboards that nobody opens are not analytics.
How do we approach it?
Analytics applications start with the questions people actually ask and the decisions they make with the answers, because a dashboard nobody opens is a cost. We audit the sources, build pipelines into a warehouse or a well-modelled database, define metrics once in a semantic layer, and build dashboards and embedded analytics on top. Data quality checks run with the pipelines, and alerts fire when numbers move outside expected ranges. An AI layer lets users ask questions in plain language and get narrative summaries and anomaly explanations, grounded in the same definitions.
What do clients use it for?
- Operational KPI dashboards
- Embedded customer-facing analytics
- Data pipelines into a warehouse
- Alerting on anomalies and thresholds
Is it the right fit?
Good fit when
- Companies with data in several systems
- SaaS products adding analytics as a feature
- Leadership teams without reliable reporting
Probably not when
- Single-spreadsheet reporting needs
- Pure data-science research
What do we build?
- Data pipelines and warehouse modelling
- Custom dashboards and embedded analytics inside your product
- Operational reporting, KPI systems, alerts
- Customer-facing analytics as a product feature
- AI layer: natural-language questions, anomaly detection, narrative insights
- Data quality monitoring
What you get
- Pipelines
- Models
- Dashboards and apps
- Documentation
- Alerting
How does the engagement work?
- 01
Source audit
- 02
Pipeline and modelling
- 03
Dashboard design
- 04
Build
- 05
Rollout
What does good look like?
Dashboards that leadership and operations open every morning because the numbers are right and the questions they answer are the ones that matter. Pipelines that run reliably with quality checks that catch problems before a meeting does. Customer-facing analytics as a product feature. And a natural-language layer that reduces the data team's request queue to the genuinely hard questions.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Analytics fails when metrics are defined differently in every report, when pipelines break silently, when dashboards are built to look impressive rather than to decide something, and when real-time is promised where daily would do. We define metrics once, monitor pipelines, design for decisions and choose latency by use-case.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Dashboard usage
- Data freshness and quality checks passed
- Pipeline reliability
Which technologies do we use?
- Postgres / BigQuery / Snowflake / MongoDB
- dbt
- Airflow / Dagster
- React charts / Metabase
- LLM layer
Who does the work?
A data engineer, an analytics engineer for modelling and dashboards, and a full-stack engineer for embedded and customer-facing components.
What do you need to bring?
Access to the data sources, the people who make decisions from the numbers, existing reports and the questions they fail to answer, and a data owner to agree metric definitions. A choice of warehouse or database if one does not exist.
Frequently asked questions
Power BI or Tableau?
We integrate with them, or build custom when you need it embedded and branded.
Real-time?
Yes. Streaming pipelines where the use-case justifies it.
Where does this fit?
Data & Analytics Applications is part of our Digital Product Engineering line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.