Application Development · Streamlit Development
From notebook to production application, without the rewrite.
Your data team already prototypes in Python. Kimbodo turns those prototypes — and new ideas — into production Streamlit applications with real authentication, performance, security and deployment. Data tools, LLM interfaces, internal dashboards and customer-facing analytics that behave like software, not scripts.
What Kimbodo Builds
Data Applications
Tools your whole team can use, not just the person who wrote the notebook.
- Interactive dashboards and reporting tools
- Data exploration and decision-support apps
- Document processing and review interfaces
- Notebook productionization with proper structure and tests
AI & LLM Interfaces
The fastest path from model to usable product.
- Chat and copilot interfaces over your models
- RAG front-ends with citations and feedback capture
- Model evaluation and comparison workbenches
- Human-review queues for AI-generated output
Production Hardening
Everything Streamlit doesn’t give you out of the box.
- SSO authentication and role-based access
- Caching, session management and performance tuning
- Containerized deployment: your cloud, Kubernetes or Snowflake
- Monitoring, logging and secrets management
Timeline & Engagement Model
Most focused Streamlit applications ship in 4–6 weeks as fixed-scope projects. Larger multi-workflow tools run 6–10 weeks. Existing prototypes can often be production-hardened in 2–3 weeks. Ongoing improvements are available as a monthly subscription — the same model as our AI Application Studio.
Proof, Not Promises
- Automating financial data processing with Gemini, DocAI & Python CRM integration — Python data tooling wired into business systems
- Large-scale data pipeline for real-time product recommendations — the data engineering behind production analytics
- Google Workspace control panel — internal tooling teams actually adopt
Buyer Questions
Streamlit or Shiny — which should we use?
Streamlit wins for Python-first teams and rapid LLM interfaces; Shiny (R or Python) wins for statistical depth, complex reactivity and the Posit enterprise platform. We build both — see Posit & Shiny Development — and will recommend the one that fits your team, not the one we feel like selling.
What does a Streamlit project cost?
Focused single-workflow apps typically start around $18,000; multi-workflow tools with several integrations usually land between $30,000 and $100,000. The estimator gives you a range for your scope in about a minute.
Can you take over an app our data scientist built?
Yes — production-hardening existing prototypes is one of our most common engagements: authentication, structure, tests, performance and deployment while your team keeps ownership of the analytical logic.
Have a Streamlit app in mind — or in a notebook already?
Get a preliminary range, timeline and architecture immediately.