Data Science Services Company
Data science services company turning raw data into business decisions.
Aiinfox is a data science services company — predictive models, BI, ELT pipelines, causal inference & experimentation. Senior team, fixed-price scope.
Churn model · weekly run
- Data freshness12 min ago
- Eval AUC0.847
- Drift (KL)0.021 ok
- Rows scored184,221
- CalibrationBrier 0.092
Forecast lift vs. baseline+12.4%
Building with
- Python
- SQL
- dbt
- Airflow
- Prefect
- BigQuery
- Snowflake
Overview
Most data science engagements stall because the dashboard never makes it into the daily standup, and the model never makes it into a product decision. We start at the other end: what business decision is the data supposed to drive, what's the cost of being wrong, and what's the cadence at which the decision actually gets made? Then we work back to the model, the pipeline, the source data, and the dashboard layer your team will actually open on a Monday morning.
Our practice covers the full stack — predictive modelling (churn, propensity, fraud, uplift), business intelligence (Metabase, Looker, dbt), data engineering (ELT, dbt, Airflow, Temporal), causal inference (difference-in-differences, propensity matching, synthetic controls), and experimentation platforms (proper power analysis, peeking guards, segmented readouts). Across 12 industries we've delivered a 10× median speed-up on previously manual reporting and 99%+ uptime on managed pipelines. Senior data engineers and scientists, fixed-price scopes, no agency layer between you and the people doing the work.
Quick definition
What are data science services?
Data science services are end-to-end engagements that turn raw business data into models, dashboards, and experiments that drive decisions — covering data engineering (ELT, dbt, orchestration), predictive modelling (forecasting, churn, fraud, uplift), business intelligence, causal inference, and experimentation platforms. The deliverable is decision support that survives past the consultancy.
What we deliver
What you actually get.
Predictive modelling
Forecasting, churn, propensity, fraud, and uplift. Calibrated, monitored, and retrained on the cadence your business actually moves at.
Business intelligence
Production dashboards in Metabase, Looker, or your tool of choice. Real-time where it matters, cached where it doesn't.
Data pipelines & ELT
Reliable ingestion across SaaS, databases, files, and streams. dbt models with tests; orchestration via Airflow, Prefect, or Temporal.
Causal inference
Difference-in-differences, propensity matching, and synthetic controls. We answer the why, not just the what.
Experimentation platform
Internal A/B framework with proper power analysis, peeking guards, and segmented readouts.
Data quality & lineage
Tests, freshness SLAs, and lineage tracking so the next bad-data incident is caught at the source.
How it fits together
A picture of the whole system.
The shape of every engagement — three lanes from data to delivery, with the parts most teams skip already wired in.
Sources
- WarehouseSnowflake · BigQuery
- Event streamKafka / Segment
- SaaS extractsStripe · HubSpot
Model
- dbt modelstests + lineage
- Train + tuneXGBoost · DL
- Calibrationisotonic · Platt
Delivery
- BI dashboardsMetabase · Looker
- Reverse ETLscores → CRM
- Experimentationpowered A/B
Proof
A real build we shipped.
Featured case · SaaS / SEO
AI agents that scaled organic traffic 300% in 6 months.
Proprietary AI SEO agents handling keyword clustering, content briefs, technical audits, and on-page optimisation — at a scale a human team couldn't reach.
Read the full caseAiinfox Internal Growth
+300%organic traffic in 6 months
We finally have a forecasting layer the CFO trusts. The pipeline catches issues before we do.
Tools
The stack we wield.
- Python
- SQL
- dbt
- Airflow
- Prefect
- BigQuery
- Snowflake
- ClickHouse
- Metabase
- Looker
- Pandas
- Polars
FAQ
Questions teams actually ask.
Do you do pure analytics or full data engineering?
Both. Most teams need a partner who covers both because the model is only as good as the pipeline feeding it. We can do analytics on top of your existing warehouse, build the warehouse, or take over a stalled data engineering project.
Which data warehouses do you work with?
Snowflake, BigQuery, Redshift, Databricks, Postgres, ClickHouse. We slot into your existing stack rather than forcing a migration. If you don't have a warehouse, we'll recommend one based on data volume, query patterns, and budget — not vendor incentives.
How do you price data science projects?
Fixed-price one-pager per scope. Typical analytics engagements land between $25,000 and $80,000 for a defined deliverable (a forecasting model, a BI rollout, an experimentation platform). Ongoing data-team augmentation is a monthly retainer.
Can you take over an existing data stack?
Yes — takeover audits are routine. Step one is reading the dbt project, the orchestration code, and the dashboards. Step two is shipping the smallest valuable change to prove we understand it. Step three is the longer-term rebuild plan if one is needed.
How do you handle data privacy and PII?
PII is masked in non-production environments and pseudonymised in analytics tables. Access is role-scoped via your identity provider. Audit logs on every query touching restricted data. GDPR / HIPAA-aligned controls where the engagement requires.
Do you support real-time analytics?
Yes — streaming pipelines via Kafka, Kinesis, or Pub/Sub into ClickHouse, Materialize, or RisingWave. We deploy real-time only where the decision actually moves at real-time speed; otherwise batch is cheaper and more reliable.
Let's build it
Ready to ship real data science services company?
30-minute discovery call. No pitch deck. We'll tell you straight whether we're a fit.