Can you extend an existing Streamlit app?

Yes—we start with a codebase review, agree a remediation and extension scope, and hand back a documented, tested version.

Can Streamlit replace our BI tool?

For custom, code-driven analytics—yes. For self-service drag-and-drop exploration by non-developers, a BI tool is usually the better fit.

Possible for tightly scoped pilots. Open-ended or evolving dashboards suit a retainer or phased model.

Streamlit data app and dashboard development — agency delivery

When Streamlit moves from prototype to internal product

Streamlit is the fastest route from a Python script to a shareable interface—but internal dashboards and data tools need more: scheduled data refreshes, warehouse connectors, caching that doesn't stall users, and role-based access. We take the working prototype and turn it into a tool your team actually relies on.

Trademark notice

Named products and brands are used for technical orientation and remain property of their respective owners. Mention does not imply endorsement, partnership, or availability guarantees for experimental software.

What we deliver

Data pipeline and warehouse integration

Direct connectors to Postgres, BigQuery, Snowflake, Redshift, or dbt models—with caching layers that keep dashboards fast without hammering your warehouse.

Multi-user auth and access control

SSO via OIDC/SAML, role-based page routing, and session-safe state management so different teams see the right data.

Deployment, scheduling, and monitoring

Containerised builds, Kubernetes or managed cloud runtimes, scheduled refresh jobs, alerting, and runbooks your ops team can follow.

Quality and delivery logic

Grounded in the service matrix—applied in your context

Caching strategy

st.cache_data and st.cache_resource scoped correctly so queries are fast and memory stays bounded—not a global cache that silently serves stale numbers.

Reproducible builds

Pinned requirements, Docker images tested in CI, and a clear separation between dev fixtures and production data sources.

State and session hygiene

No cross-user state leaks. Session isolation tested explicitly—especially for apps that display sensitive figures.

When engagement makes sense

Internal BI tool becoming a real product

When the dashboard built for one analyst is now expected to serve a whole department with consistent, trusted numbers.

Warehouse or dbt model integration

When the app needs to query production data, join models, and write results back—without a manual CSV export step.

Data team without ops bandwidth

When the Python expertise is there but deployment, auth, and incident response are not the team's core skill.

FAQ

Can you extend an existing Streamlit app?

Yes—we start with a codebase review, agree a remediation and extension scope, and hand back a documented, tested version.
Can Streamlit replace our BI tool?

For custom, code-driven analytics—yes. For self-service drag-and-drop exploration by non-developers, a BI tool is usually the better fit.
Fixed price?

Possible for tightly scoped pilots. Open-ended or evolving dashboards suit a retainer or phased model.

Discuss your Streamlit project

We give an honest view of scope, complexity, and risk before any commitment.

Streamlit data apps that run in production

Internal dashboards, analytics tools, and data science UIs—hardened for real teams, not just demos.