From raw events
to operational decisions.
Modern data warehousing, BI dashboards, ML pipelines and applied AI engineering — including production AI deployments running live across education, finance, and logistics in Kenya. Including MwalimuPLUS, currently serving 200,000+ students.
- Data audit & warehouse design
- ETL / ELT pipelines
- BI dashboards & self-serve analytics
- ML model development
- Applied AI / LLM integrations
- MLOps & monitoring
- Data governance & DPA compliance
Data work that moves the P&L.
A lot of "data projects" build a beautiful warehouse and a few dashboards nobody opens. Ours start from the operational decision the data needs to inform — and only build what makes that decision better.
Every Augusta data engagement is anchored to a measurable business outcome. If we can't articulate what changes when the dashboard goes live, we don't build it.
Decision-first
We start from the question — not the data. Every model, dashboard, and pipeline we build connects to a specific operational decision a real human will make differently.
Production AI experience
We've shipped applied AI to production at scale — including MwalimuPLUS, used by 200,000+ students with adaptive curriculum-aligned learning paths.
Lean, modern stack
Snowflake / BigQuery, dbt, Looker / Metabase, Airflow, MLflow, Anthropic / OpenAI. We pick what's right; we don't push enterprise products that overshoot.
Honest about LLMs
We deploy LLMs where they pay off — and we'll tell you when they don't. Many "AI" problems are better solved with rules, classical ML, or just a SQL query.
What we build.
Six data and AI practices, all shipping in production today.
Data warehousing
Modern lakehouse and warehouse architectures on Snowflake, BigQuery, Databricks. dbt-based transformation layers, version-controlled.
ETL / ELT pipelines
Airflow, Dagster, Fivetran, custom Python — pipelines that handle the messy reality of African business data sources.
BI & dashboards
Looker, Metabase, Superset — boardroom-ready dashboards, plus self-serve exploration for analyst teams.
Applied AI & LLMs
RAG systems, agentic workflows, OpenAI / Anthropic integrations, embeddings, semantic search — production-grade, not demo-grade.
ML engineering
Recommendation engines, classification, forecasting, computer vision, MLOps. PyTorch, scikit-learn, MLflow, Weights & Biases.
Data governance
Catalog, lineage, access controls, PII handling, DPA-compliant retention policies. The boring work that makes data trustworthy.
From question to dashboard.
Three phases. We deliver value in the first six weeks, not the eighteenth month.
Question & map
What decision needs to change? What data exists today? Where does it live? We end with a prioritised set of decisions and a phased plan.
Build & ship
First-cut warehouse, first dashboards, first ML model — shipped in weeks, not quarters. Iterative, measurable, with the customer in the loop.
Operate & evolve
Pipeline reliability, dashboard maintenance, model retraining, governance review. Optional managed data ops if you don't have a data team yet.
Buyer questions.
The questions we hear before signing.
Have data?
Let's make it useful.
Tell us about the decisions you're trying to make and the data you have. A senior data engineer will respond within one business day with a clear point of view.