Data Science Analytics Company

Because it is measured on data it has never seen, against a baseline agreed before the work started. Miracuves fixes and versions the train and test split up front, ships a simple baseline first so the value of anything more sophisticated is visible rather than assumed, and reports on held-out data. We also agree what failure looks like, and the point at which a model should be retrained or switched off, before it informs a customer-facing decision.

A ready-made platform from our catalogue starts from $2,199 and ships in 6 days; its price stays fixed when your scope matches the base product. A custom data science MVP typically runs $8,000-$25,000 and takes 2-8 weeks depending on scope - how many systems your data sits in and how clean it is, whether past outcomes exist to learn from, the number of models, how results are delivered and how often models are retrained decide where it lands. Ongoing work is available from $2,299/month for feature work and maintenance. Every quote is written before payment, with no surprise invoices after kickoff.

Yes - completely. Miracuves delivers the full data science codebase: notebooks, feature and training code, the trained model files, the repository with complete commit history, environment configuration, API documentation, and all deployment credentials. Zero lock-in. Your data team or any other company can retrain and extend the models immediately after handoff.

A ready-made platform from our catalogue - web app, admin panel and mobile apps, white-labelled - ships in 6 working days. A custom data science build takes 2-8 weeks depending on how ready your data is and how many models are in scope. All timelines are stated in writing before any payment is requested.

It depends on how many models you will run and for how long. If data science is core to the product and you expect many models retrained for years, build an in-house team - Miracuves can build the first models, pipelines and conventions they inherit. If you need one to three questions answered well - a forecast, a churn score, a pricing test - an engagement with Miracuves is faster than hiring, and you still own every line of code. For reporting only, we recommend data analytics instead.

For a custom data science build, the dashboard shows model outputs next to the numbers they drive - forecasts against actuals, segment sizes and movement, experiment results with their confidence intervals - built in Streamlit, Power BI, QuickSight or your existing BI tool, with access by role. A ready-made platform also ships its own web admin panel with user, order and revenue reports.

Yes, as part of every delivery engagement. Miracuves deploys pipelines, models and dashboards to your AWS, GCP or Azure account or your own servers: scheduled jobs, the model as a batch job or REST API, secrets in environment configuration, and alerts for failed runs and drift. The cloud accounts stay in your name, so you keep control of the data and the bill.

Every Miracuves data science delivery includes 60 days of post-launch technical support. Pipeline failures and bugs within the delivered scope are fixed at no additional cost. New models or questions beyond scope are quoted separately. Monthly retainers for retraining and monitoring are available at published rates.

Miracuves signs a bilateral NDA before any project details are shared. The NDA covers all technical details, business logic, and IP. An IP assignment agreement confirming 100% ownership transfers to the client is signed at project start - not at the end.

Yes, with controls agreed before any data moves. We work inside your cloud account where possible, remove or pseudonymize direct identifiers before modelling, restrict access by role and keep a log of who ran what. We build to the controls GDPR, HIPAA and similar frameworks require; certification and legal sign-off remain your organization's audit. An NDA is signed before any data is shared.

Sooner or later it will, because customer behavior, prices and product mix change. Every model we deploy logs its predictions, compares them with actual outcomes and alerts when error rises past a threshold agreed with you. Within the 60-day support window we investigate and fix issues in the delivered scope; after that, retraining and monitoring are covered by the retainer from $2,299/month, or by your own team using the retraining code we hand over.

No. Many first models are built from exports of your production database, CRM or spreadsheets. A warehouse becomes necessary when the model has to refresh on a schedule from several systems; in that case we scope the pipeline as part of the build, or recommend our data engineering service first.