Data
Pipelines before models
Ingestion, transformation and lineage - where each number came from, and whether it still means what it meant at source.
The Stack Behind Every Engagement
Four disciplines, one accountable team - depth where it actually changes the outcome.
MLOps: Models That Survive Contact With Production
Versioning, monitoring, automated retraining - the work that starts the day a model ships.
A model that works in a notebook has been tested against a frozen copy of the world. Production moves: the data drifts, the upstream schema changes, the business asks a question nobody trained for. Model operations is what keeps the answer trustworthy after launch, and it is the half of an AI project that is most often left unstaffed.
From notebook to production, and back again
The loop
Not a launch checklist - a loop that keeps running. Each stage feeds the next, and the last one feeds the first.
- 01
Package and version
The model, the data it was trained on, and the parameters that produced it are versioned together, so any result can be reproduced later rather than argued about.
- 02
Deploy
Into the systems you already run, behind the access controls you already have - not onto a parallel stack that becomes someone's side responsibility.
- 03
Monitor
Accuracy, latency and drift watched as running metrics, with alerting when the numbers move rather than when a user complains.
- 04
Retrain
Automated retraining on a schedule or on a drift signal, with the new version evaluated against the old one before anything is swapped.
- 05
Roll back
A defined path back to the previous version, tested before it is needed. A model you cannot withdraw quickly is a model you cannot deploy confidently.
- 06
Account for it
An audit trail of what was deployed, when, by whom, and on what evidence - the record a regulated client will eventually ask for.
A model is only as good as the pipeline feeding it
Underneath
- Structure
Structured data kept structured
Our records engagement pairs a structured database alongside the vector store rather than embedding figures and hoping: numbers keep their meaning, and trends are answered from real values.
- Compliance
Accuracy and compliance built in
Checked in from the start rather than audited afterwards - which is the difference between a control and a remediation project.
Where this is already running
In production
Four engagements with the operational layer underneath them, each written up in full.
Records Intelligence
Agentic RAG over decades of records, with every figure traceable to the report it came from.
Read the caseDocument OCR
Scanned, handwritten and printed documents turned into a searchable archive, deployed on-premise.
Read the caseThreat Intelligence
Continuous monitoring of public channels for threats and risky behavior, with real-time alerting.
Read the caseLanguage AI
Real-time speech-to-speech translation, and an AI coach that assesses and tutors in one loop.
Read the case
Common Questions About MLOps
FAQ
MLOps (machine learning operations) is the practice of taking models from a notebook to production and keeping them accurate there - through versioning, deployment, monitoring, retraining and rollback.
Accuracy, latency and drift are monitored as running metrics, with alerts when the numbers move rather than when a user complains.
Retraining runs on a schedule or on a drift signal, and each new version is evaluated against the current one before anything is swapped.
There is a defined rollback path to the previous version, tested before it is needed.
Yes. We keep an audit trail of what was deployed, when, by whom and on what evidence, and version models, data and parameters together so any result can be reproduced.
Stuck between the demo and production
If you have a model that works and nowhere safe to put it, tell us where it stalled. We will tell you what the operational layer underneath it would have to do.