A data-science team embedded with your operators - turning messy CRM, billing, product and ops data into the dashboards leadership opens on Monday. We do the heavy lifting end-to-end: from the first uncomfortable question through to a deployed model and a dashboard that means something.
The same exploratory-data-analysis process our team runs for every engagement - so you always know where the work stands, and what we'll deliver next.
Frame the business question. Audit the dataset - shape, types, sample rows - and confirm what's actually in there versus what's assumed.
Drop duplicates. Decide what to do with missing values - impute, leave, or remove. Surface outliers with IQR or box plots and treat them on context.
One variable at a time. Distributions, central tendency, frequency counts - histograms for numeric, bar charts for categorical, so the data tells its own story first.
How do variables interact? Scatter plots for continuous pairs, correlation heatmaps for the full grid. This is where the actual signal lives.
Combine columns into the features that actually matter - "Total Price" from "Qty × Unit Price", recency, lifetime, ratios. Normalize, scale, encode for the next stage.
The non-technical layer. A dashboard leadership opens, a written summary, hypotheses to test next quarter. The work isn't done until somebody acts on it.
If you've heard yourself or your CFO say one of these, this is where we start.
We don't drop a model and disappear. The team is on Slack, in your stand-up, and shipping into the tools your operators already use.
Salesforce CRM Analytics, HubSpot reports, Looker, Power BI - whatever the C-suite opens.
Churn, propensity, lead scoring, forecasting - shipped into CRM as actionable fields.
Ask the data a question in natural language. Behind the scenes: a model grounded in your schema.
ELT pipelines into the warehouse, dbt models, governance, lineage. The plumbing that makes the rest possible.
AI agents in production with evals, guardrails, and proper human escalation - on Salesforce and HubSpot.
Retention curves, LTV by tier, time-to-value by persona - the views nobody had time to build.
When something looks wrong, we flag it before the next board meeting does.
Plain-English summary docs that translate analysis into decisions - so the work travels past one stakeholder.
Not analysts hiding behind a SQL editor. The team has academic depth and shipping experience.
We ship to Salesforce and HubSpot fields, not to a notebook nobody opens.
We sit in your channel, your retros, and your QBRs. The work compounds because the context compounds.
From kickoff to a working dashboard inside a month. Bigger questions, bigger answers - but never PoC theatre.
We run it. The team will read the data, write the queries, build the model, deploy it, and document it. Advisory engagements exist but are the exception - most of our work is implementation.
That's the first three steps of EDA. Cleaning, deduping, missing-value strategy, outlier triage - we expect it and budget for it. The audit at week one will tell you exactly how messy.
A dashboard answers a question once a week. A model answers it every time a record changes. We start with the dashboard - and only move to a model when the answer needs to happen at the speed of the business.
Both. Native integrations with Salesforce CRM Analytics, Einstein, Data Cloud, and Agentforce - and with HubSpot's reporting + Breeze AI on the other side. Plus Snowflake, BigQuery, dbt, Looker, Power BI, Tableau.
Yes - one of our specialties. JumpStart partner with Salesforce on Agentforce, and early access on Breeze AI. We've put agents into production with proper evals and guardrails.
Smallest: a fixed-scope analysis with deliverable, 3-4 weeks. Most common: an embedded squad on retainer, 2-3 senior people, ongoing. Largest: a full data platform build with engineering and science combined.
Tell us the question and the data you have. We'll come back inside two working days with how we'd approach it and what we'd deliver in the first month.