AI Data & Models
AI Data Engineering
Every AI system is a data system with a model attached. We build the ingestion, transformation, quality checks and vector storage that determine whether the model has anything correct to work from.
The business problem
The failure looks like a model problem
When answers are wrong, attention goes to the model. Frequently the cause is upstream: a pipeline that silently dropped records, a schema change nobody noticed, duplicates from a partial reload, or a document set that stopped refreshing three weeks ago. Without quality checks and lineage these are invisible, so teams tune prompts against broken inputs.
What we do
Build it so failures are loud
We build pipelines with validation at every boundary, so a schema change or a volume anomaly stops the run rather than propagating quietly. Lineage is recorded end to end, which means it is always answerable where a value came from. Where retrieval is involved we handle chunking, embedding and index refresh as first-class pipeline concerns rather than as a script somebody runs manually.
AI Data Engineering
Capabilities
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AI Data Pipelines
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ETL/ELT
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Knowledge Pipelines
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Vector Databases
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AI Data Platforms
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Data Quality Engineering
Common use cases
Common use cases
- Build the ingestion and refresh pipeline behind an enterprise knowledge assistant.
- Establish quality gates on the data feeding a production model.
- Consolidate fragmented sources into something a model can reason over consistently.
- Diagnose an AI system whose accuracy degraded without any change to the model.
How we deliver
How we deliver
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Audit
Establish what data exists, who owns it, and what condition it is actually in.
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Pipeline
Build the ingestion, transformation and quality checks the models depend on.
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Benchmark
Compare approaches on your data rather than on a public leaderboard.
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Serve
Deploy behind a stable interface with versioning, monitoring and a rollback path.
Technology
Technology
- Python
- dbt
- Apache Airflow
- Snowflake
- Databricks
- PostgreSQL
- pgvector
- PyTorch
- Hugging Face
Security & governance
Security & governance
Data lineage is recorded end to end, so it is always answerable where a value came from and which model version produced a given output. Personal data is minimised, classified and retained under an explicit policy rather than by default. Training and evaluation sets are versioned alongside the code that uses them.
Engagement models
Engagement models
AI Project
We take responsibility for designing and delivering a defined AI solution.
Dedicated AI Team
Long-term dedicated engineering capacity built around your stack and delivery model.
Managed AI
We operate, monitor and continuously improve production AI systems.
Why TeamExtension.ai
We treat data as production, not preparation
Pipelines built for a proof of concept are the most common source of production AI incidents, because nobody expected them to still be running. We build them to be operated: monitored, alerted, versioned and recoverable.
Selected clients
Frequently asked questions
Frequently asked questions
Do we need a new platform?
How do you handle documents that change?
What about personal data in pipelines?
Can you work with our existing stack?
Related capabilities
Related capabilities
AI Applications & Knowledge
RAG Development
Retrieval systems that ground answers in your own content, with citations and measurable accuracy.
Learn moreAI Applications & Knowledge
AI Integration
Connect AI to the ERP, CRM and data systems where the work and the records actually live.
Learn moreAI Infrastructure & LLMOps
MLOps
Lifecycle management for machine learning: deployment, monitoring, retraining and CI/CD.
Learn moreDiscuss Your AI Initiative
The pipelines, quality controls and vector stores that AI systems depend on to be correct.