Services
Eight disciplines, and an honest note on the situation each one is for. The first of them exists to tell you whether you need the other seven.
Data Readiness Assessment
Before any model, an honest audit of whether your data can carry one.
The engagement most clients skip and most later wish they had not.
What you get
- Volume, label quality, and class balance
- Leakage detection, the failure nobody catches in time
- Drift analysis across the period you actually have
- A written verdict, including 'not yet'
Predictive Modelling and Forecasting
Demand, churn, risk, and price. Models judged against a baseline you agree beforehand.
For decisions currently made by a spreadsheet and a strong opinion.
What you get
- A stated baseline the model must beat to ship
- Held out test sets, never touched during development
- Confidence intervals on every reported metric
- Backtesting across regimes, not one lucky window
Language and Document Systems
Retrieval, extraction, and assistants over your own documents. Rarely a fine tune, almost never a new model.
For teams drowning in documents that already contain the answer.
What you get
- Retrieval augmented generation with cited sources
- Extraction pipelines with human review in the loop
- Evaluation harness before deployment, not after
- Hallucination rates measured and published to you
Computer Vision
Detection, segmentation, and quality inspection on real imagery, with the lighting you actually have.
For inspection done by tired human eyes at the end of a shift.
What you get
- Annotation strategy and inter annotator agreement
- Edge deployment where latency matters
- Failure mode analysis on the long tail
- Performance under the conditions of your factory, not a benchmark
Machine Learning Engineering
Taking a notebook that works once and making it a system that works every day.
For the model that impressed everyone and then never shipped.
What you get
- Feature stores and reproducible training runs
- Model registry, versioning, and rollback
- Drift monitoring with alert thresholds
- Retraining pipelines that run without heroics
Data Engineering and Platform
Warehouses, pipelines, and the unglamorous foundation everything above it depends on.
For organisations where two dashboards disagree and nobody knows which is right.
What you get
- Ingestion that survives a schema change
- Warehouse modelling built for questions, not tables
- Data quality tests that fail loudly
- Lineage, so you can answer where a number came from
Analytics and Decision Support
Sometimes the answer is a query and a chart. We will say so.
For questions being answered with correlation and hope.
What you get
- Experiment design and causal inference
- Dashboards that answer a decision, not a curiosity
- Statistical review of claims before they reach a board
- Training so your team can do this without us
AI Governance and Assurance
Independent evaluation of models, including ones we did not build.
For anyone deploying a model that affects a person's money, health, or liberty.
What you get
- Bias and fairness evaluation across subgroups
- Documentation and model cards for audit
- Human oversight design for consequential decisions
- DPDP Act aligned data handling review
Every engagement starts with the same question
Can your data support the thing you want to build. Not whether the technique exists, which it almost always does, but whether the evidence in your possession is sufficient to learn from.
That assessment takes two to four weeks and produces a written verdict. If the answer is that you need better instrumentation, cleaner labels, or simply more time, you will get that in writing along with what to fix. Nine clients have received exactly that answer, and several came back a year later with data that worked.
Start with an assessmentWhat a proposal from us contains
Send us your data problem. We will tell you whether it is one.
A working data scientist reads every enquiry. You will get an honest read on whether your data can support the model you have in mind, before anyone quotes you a number.