Data Science for Intelligent Business Insights

Cost depends mainly on data source count, data volume, and how much modeling versus reporting is involved. A single ETL pipeline with a dashboard costs less than a multi-source data platform feeding predictive models. We scope a fixed estimate after a short discovery call rather than quoting from a generic package.

Timelines scale with source count and data quality — a single clean source moves fastest, while multiple messy sources needing reconciliation take longer. We favor shipping a working pipeline and dashboard against one priority metric first, then expanding scope once it's proving value in production.

Yes. Roughly half of our data engagements are with US-based startups and enterprises, alongside teams across India, the UAE, and the UK. We overlap working hours with US time zones and run async-friendly delivery — written specs, recorded demos, shared dashboards — so distance doesn't slow decisions down.

Data source audit and cleaning, pipeline and ETL architecture, model development where predictive work is warranted, and visualization or reporting layers. We also set up monitoring so pipelines and models keep running correctly as source data changes, rather than delivering a one-time analysis.

Yes — we integrate with common warehouses (Snowflake, BigQuery, Redshift) and BI tools (Looker, Power BI, Tableau, Metabase) rather than mandating our own stack. Where your existing setup has structural gaps, we'll flag them, but we build within your tooling wherever it's workable.

Both — reporting and dashboards for teams that need visibility, and predictive or ML models (forecasting, anomaly detection, classification) where the use case justifies it. We're direct about when a simpler statistical approach outperforms a model in practice, rather than defaulting to ML for its own sake.

You own the pipelines, models, and infrastructure from day one — nothing is licensed back to us. At handover we deliver documentation and an architecture walkthrough, with a transition period where your team pairs with ours before we step back, so you can run and extend the system independently.