Data & AI Intelligence
Predictive analytics, BI dashboards, and ML models that turn your scattered data into business intelligence you can act on.
What a properly built data platform unlocks
Where teams lose months without the right foundation
Time spent on common data problems before our platform-first approach. Each line is hours per month a typical analyst loses to issues a clean stack solves.
CapabilitiesWhat we build with your data
Predictive Analytics
Forecasting models for revenue, demand, churn, lifetime value, and risk - built on your historical data and deployed as services.
BI Dashboards
Real-time dashboards in Metabase, Looker, or Power BI that connect every data source and give leaders a single source of truth.
Data Pipelines
ETL/ELT pipelines that move and transform data from operational systems into a clean, queryable warehouse - daily, hourly, or real-time.
ML Model Training
Custom machine-learning models fine-tuned on your data for classification, scoring, recommendation, and anomaly detection.
NLP Solutions
Document understanding, sentiment analysis, intent classification, and information extraction at scale.
Real-time Analytics
Event-streaming architectures that surface insights as they happen - fraud signals, support spikes, conversion drops - not days later.
Before and after a real data platform
What a typical mid-market analytics function looks like before our engagement, and what it looks like 90 days after launch.
- Time to answer a new business question3-10 days
- Source of truth for revenue3+ conflicting reports
- ML / predictive models in production0
- Data freshnessWeekly export
- Cost to add a new data sourceCustom build, weeks
- Confidence in the numbersCaveated every meeting
- Time to answer a new business questionUnder 1 hour
- Source of truth for revenueSingle warehouse
- ML / predictive models in production3-6 use cases
- Data freshnessHourly or real-time
- Cost to add a new data sourceConfigured, hours
- Confidence in the numbersTrusted by default
HowFoundations first, then intelligence layers
Map
Inventory data sources, quality issues, and the business questions leadership needs answered.
Build
Stand up a clean data warehouse and pipelines so every downstream model and dashboard works from trustworthy data.
Model
Train, validate, and deploy the analytics and ML models that answer the priority questions.
Iterate
Monitor model performance, retrain as data drifts, and expand the platform to new use cases.
Where engineering effort goes on a data platform build
Most teams underinvest in the unglamorous parts - quality, pipelines, governance - and pay for it forever. Our split is deliberately weighted to the foundations.
- Warehouse, modelling & pipelines38%
- Data quality & observability22%
- ML / AI models18%
- Dashboards & embedded analytics14%
- Governance & access control8%
What ‘good’ looks like for a data platform
Targets we hit before we hand the platform to your team. Anything less and we keep iterating.
WhyModern data platforms, no vendor lock-in
We use battle-tested, open-source-friendly tools and assemble the right stack for your scale and budget - from startup-friendly setups to enterprise data platforms.
Warehouses
BigQuery, Snowflake, PostgreSQL, or DuckDB depending on scale, cost, and existing infrastructure.
Pipelines
Airbyte, Fivetran, dbt, Airflow, Prefect - chosen to match your team's skills and reliability needs.
ML Frameworks
scikit-learn, XGBoost, PyTorch, and Hugging Face Transformers for everything from classical ML to modern deep learning.
Visualization
Metabase, Looker, Tableau, Power BI, and embedded analytics components inside your apps.
MLOps
MLflow, Weights & Biases, BentoML, and Modal for experiment tracking, model registry, and deployment.
Real-time Streaming
Kafka, Redpanda, and managed streaming services for event-driven analytics and AI.
QuestionsAnswers to common questions about this service.
We don't have a data team. Can you still help?+
Yes. Many of our clients don't have a dedicated data function. We build the platform, set up dashboards, and train your operating team to read and act on them. We also offer ongoing managed analytics if you'd rather not hire in-house.
How clean does our data need to be before we start?+
Not as clean as you think. Part of the engagement is cleaning, deduplicating, and standardizing what you have. We'll surface data quality issues early and recommend the minimum cleanup needed before each downstream use case.
Do you build internal dashboards or customer-facing analytics?+
Both. We build internal BI dashboards for leadership and operations, and we build embedded analytics features inside your customer-facing applications when that's part of the product story.
How long until we see results?+
Quick wins in 2-4 weeks - usually a key dashboard or first predictive model. Foundational data platform work takes 6-12 weeks depending on the number of sources. ML projects vary by complexity, but most ship a first version within a quarter.
Let's buildReady to put AI to work in your business?
Book a free 30-minute strategy call. We'll map your highest-impact automation opportunities and give you a clear roadmap - no obligation.