Turn raw data into decisions you can act on
Spreadsheets and gut instinct only get a business so far. We build predictive models, dashboards, and applied ML systems that tell you what's actually happening — and what's likely to happen next.
What should data science ship beyond a slide deck?
Predictive Modelling
Forecasting demand, churn, and risk using models trained on your historical data.
BI Dashboards
Live dashboards that put the metrics your team checks daily in one place.
NLP & Text Analytics
Sentiment analysis, document classification, and search built on your text data.
Recommendation Systems
Product, content, or offer recommendations tuned to how your users actually behave.
Which tools power the models and dashboards?
We pick tools based on what fits your data and team, not what's trendy. Typical projects run on:
How do you get from messy data to a model you can trust?
We start by auditing what data you actually have and how clean it is — most projects live or die on this step. From there we scope a model or dashboard against a specific business question, not a vague "use our data" brief, and validate results against real outcomes before anything goes live.
Every engagement includes documentation of what the model does, its limitations, and how to retrain or monitor it, so you're never locked into us to keep it running.
A single dashboard or a well-defined prediction problem (churn, demand for one product line) typically takes 2-4 weeks once the data audit is done. A broader analytics platform pulling from multiple sources usually runs 5-10 weeks. Data quality, more than model complexity, is what actually moves that timeline — which is exactly why the audit step comes first, not last.
Who this is for
- E-commerce & D2C brands wanting demand forecasting or churn prediction.
- Service businesses wanting a single source-of-truth dashboard.
- Teams that already have data but no one to turn it into a model.
How do we scope a data science project to fit your data?
Every project gets a fixed-scope quote after a free consultation — these tiers are a starting point for the conversation.
Single Model or Dashboard
For one well-defined question or metric.
- Data audit and cleaning of one core dataset
- One prediction model or BI dashboard
- Validation against real historical outcomes
- Documentation of the model's limits
Analytics Platform
For a broader platform pulling from multiple sources.
- Multiple data sources pulled into one pipeline
- Several models or dashboards, one source of truth
- Retraining and monitoring plan included
- Team walkthrough so you're never locked in
Ongoing Data Partnership
For teams that want a standing data science function.
- New models and dashboards added over time
- Ongoing monitoring for model drift
- Priority access to the same data team
- Simple monthly retainer, no long-term lock-in
Further reading: Retrieval-Augmented Generation (RAG) Explained & Generative AI for Business