AI & Machine Learning
Most organisations do not have an AI problem. They have a use-case selection problem, a data readiness problem, and an evaluation problem — and those are the three things we fix first.
Arush Soft builds machine-learning and applied-AI systems that survive contact with production: monitored, versioned, evaluated against a held-out set that the business agreed on before the model was built.
What we deliver
- Use-case triage. A scored portfolio across value, data readiness, and the cost of being wrong — so the first project is winnable.
- Data readiness. Labelling strategy, leakage checks, feature engineering and the pipelines that make training reproducible.
- Classical ML. Forecasting, classification, ranking, anomaly detection and optimisation, where they still beat anything larger.
- LLM applications. Retrieval-augmented generation, structured extraction, summarisation and agent workflows with human review built in.
- MLOps. Model registry, CI/CD for models, shadow deployment, canary rollout and automated rollback.
- Monitoring. Drift detection, calibration tracking, cost-per-inference and a feedback loop back into training data.
Evaluation before enthusiasm
Every engagement defines the evaluation set and the acceptance threshold before a single model is trained. If the model cannot beat the current process on that set, we say so — and we would rather say so in week three than in month nine.
Responsible deployment
We document what the system does, what it cannot do, and where a human must stay in the loop. For anything touching hiring, credit, health or safety, human review is a design requirement rather than a policy footnote.
- 3Gates before a model ships
- Week 3Go / no-go decision point
- 100%Models with drift monitoring
Talk to someone who does this work
Not a sales team. The practice lead for AI & Machine Learning will be on the first call.