The Data Work That Makes Your AI Actually Work
Consolidation across systems. Cleaning and labelling. Retrieval infrastructure. Governance your information officer will sign off. The unglamorous work that determines whether your AI pilot graduates to production.
Indicative scope
- function
- warehouse and pipelines
- volume
- audit-on, region-bound
- region
- POPIA scope
Indicative scope. Real engagement values confirmed at proposal.
What We Deliver
AI projects fail more often on the data side than the model side. We do the groundwork that makes everything downstream possible: identifying your authoritative sources, reconciling the ones that disagree, cleaning the records that matter, and standing up the retrieval infrastructure your models will query against. The deliverable is a data substrate you can run AI on, not a folder of exports you can only use once.
Everything Included
Authoritative Source Mapping
We identify which system is the source of truth for each data class, and where the duplicates and contradictions live.
Cleaning That Sticks
Data quality rules baked into the pipelines that produce the data, so quality holds after our engagement ends.
Retrieval Infrastructure
Vector stores, chunking strategies, and metadata indexing engineered for your query patterns, not generic defaults.
Governance Built In
Access controls, retention rules, audit logging, and POPIA lawful-basis mapping at the data layer.
Labelling Where Needed
For use cases that need supervised training, we design the labelling workflow and run it with quality controls.
Integrated with Your Stack
Works with whatever you already have: Snowflake, BigQuery, Postgres, Supabase, Microsoft Fabric, or your existing warehouse.
What Success Looks Like
Every engagement is defined by the outcomes we commit to. Work output matters only to the extent that those outcomes land.
- A reconciled, authoritative view of the data classes your AI work depends on
- Cleaning pipelines that hold quality as data flows continue
- Retrieval infrastructure engineered for your query patterns
- A POPIA-aligned governance layer your information officer can sign off
- A data health dashboard for ongoing monitoring
Our Process
Data Audit
Full inventory of what you have, where it lives, what quality it is in, and who owns it.
Source Reconciliation
For every data class, we name the authoritative source and reconcile the duplicates.
Cleaning Pipelines
Automated cleaning and validation rules built into the data flow, not one-time scripts.
Retrieval Build
Vector store, chunking strategy, metadata indexing, and query patterns designed for your use cases.
Governance Layer
Access controls, retention, audit logging, and lawful-basis tagging at the record level.
Handover
Documented architecture, runbooks, and a health dashboard your team can monitor.
Your Data Foundations questions, answered
STATUS // RESPONSE WITHIN ONE BUSINESS DAY
Tell us the function.
Share the cost line you want to address. We will come back inside one business day with a scoped proposal.
Across the practice
Other ways we replace expensive work
Core AI