Hire Data Analysts and Engineers,
or an AI Agent That Crunches Your Data.
Dashboards, pipelines, and AI agents that turn your warehouse into decisions.
Agent or human?
AI data agent vs. human analyst.
AI DATA AGENT
Best for ad-hoc.
Text-to-SQL, analyst copilots, anomaly detection on a clean warehouse. Answers questions on demand.
HUMAN ANALYST
Best for strategy.
Stakeholder communication, root-cause investigation, dashboard design that gets used.
BLEND
Best for most teams.
AI agent for the queries, human analyst for the strategic work. The agent makes the analyst 3x faster.
What we ship
What does an outsourced data team actually do?
DASHBOARDS
Dashboards your team uses
Built in Looker, Mode, Hex, or Metabase. Designed for one job each, not a sea of charts.
PIPELINES
Data pipelines
Ingestion, modeling, and orchestration on Snowflake, BigQuery, or Databricks. Versioned and tested.
AI ON WAREHOUSE
AI agent on top of data
An AI agent that answers natural-language questions against your warehouse. With guardrails.
TRANSFORM
Analytics engineering
dbt models, tests, documentation. The kind your future analyst will thank you for.
REVENUE OPS
Revenue analytics
ARR, MRR, churn, cohorts, attribution. Decisions you can defend in a board meeting.
ANOMALIES
Anomaly detection
AI watches your KPIs. Flags what changed, what caused it, and what to do.
Stack
What data stacks do you work with?
WAREHOUSE
Snowflake, BigQuery, Redshift, Databricks
TRANSFORM
dbt
ORCH
Airflow, Dagster, Prefect
BI
Looker, Mode, Hex, Metabase
INGEST
Fivetran, Airbyte, Stitch
AI
OpenAI, Claude, text-to-SQL agents
How it works
Four steps from messy data to decisions.
Audit the data
Where it lives, how dirty it is, what decisions it should drive.
Pick the team shape
Analyst, engineer, AI agent, or blend. We propose what fits.
Ship the foundation
Pipelines, models, dashboards, AI agent on top. Weekly demo cadence.
Handoff or stay
Documented stack, runbooks, ongoing support. You choose the duration.
Who it is for
Best-fit teams.
SAAS FOUNDERS
Need answers from data without hiring a full data team.
REVOPS LEADERS
Want revenue analytics that match the way you actually run the business.
SMB BUYERS
Have data in 5 tools and no source of truth. Yet.
FUNDED STARTUPS
Ready to build the data foundation before scaling.
Related offerings
What we pair with data work.
Pair the data team with AI developers who build AI models when the pipelines feed a custom model. Most outbound failures are data failures. See how our AI SDR setup service plugs into your CRM cleanly. And if you are hiring data engineers, our AI agent for recruiting outreach can run the candidate sourcing for you.
FAQ
Quick answers.
Do I hire a data analyst, a data engineer, or both?
+
A data analyst answers questions: dashboards, ad-hoc analyses, weekly reporting. A data engineer builds the plumbing: ingestion, modeling, the warehouse, and reliable pipelines. Early-stage SaaS usually starts with one analytics engineer who does both. Once your data volume crosses a threshold, you split into specialized roles. We help you scope which one to hire first.
Can an AI agent replace a data analyst?
+
For ad-hoc questions against a clean warehouse, yes. AI agents like text-to-SQL tools and analyst copilots are good enough today. For pipeline reliability, anomaly investigation, and stakeholder communication, you still want a human in the loop. We offer both: an AI data agent layered on top of your warehouse, plus human analysts for the strategic work.
What data stacks do you work with?
+
Warehouses: Snowflake, BigQuery, Redshift, Databricks. Transformation: dbt. Orchestration: Airflow, Dagster, Prefect. BI: Looker, Mode, Hex, Metabase. Ingestion: Fivetran, Airbyte, Stitch, custom connectors. We build on the stack you already pay for instead of forcing you onto ours.
Turn your data into decisions.
Tell us where your data lives. Scoped proposal in 48 hours.
Book a discovery call