Forecasting, classification, and extraction applied to the work you already run — connected to ERP and process automation, with data privacy handled in the design rather than added after a compliance request.
Most production value sits in forecasting, classification, extraction, and triage — work that changes cycle times, error rates, and throughput you can measure. That is where we start before larger research-style efforts.
Generative models have a place when retrieval and summarisation fit the job. We do not open with an LLM by default, and if one is the wrong tool we say so before budget is spent proving it.
Build a chatbot for a problem a simple form already solves.
Train a custom model when a fine-tuned open model costs less and performs the same.
Ship a model without documenting how it fails and what fallback applies.
Use your data to train products we sell to other clients.
Work that can return value inside a quarter. If it needs a research programme first, it is usually the wrong opening project.
Still one of the highest-value ML applications inside ERP. Models capture seasonality, leading indicators, and the drivers behind each number — auditable, explainable, and tuned per business line.
Invoices, contracts, KYC documents, and COAs: OCR, extraction, and validation connected directly to ERP and approval workflows.
Support tickets, leads, anomalies, and exceptions routed by category and urgency, with a confidence threshold that decides when a person must review.
Fraud, asset misuse, system drift, and sensor faults — tuned to what is normal for your operation, not a generic baseline that floods the queue with false positives.
Internal knowledge search, document summarisation, and policy lookup — retrieval-augmented, grounded in your data, with citations. Built for staff use, not as a public chatbot.
Next-best-action for sales, dispatch routing, asset-to-task matching, and role-based recommendations inside ERP — built from your business rules.
Discovery, prototype, then production. No step skipped, and nothing left as a permanent side project.
Which decision does the model improve, and what does success look like in operational metrics? If we cannot answer that clearly, we do not proceed.
What you have, what is missing, and what is reliable enough to build on. We check before training starts, not after something fails in production.
We start with a simple baseline. If a basic model cannot beat a sensible heuristic, the problem may not suit ML — and we learn that within three weeks.
Feature engineering, model selection, and threshold tuning decided by holdout performance, not by preference in the review meeting.
Systems integration with monitoring, retraining schedules, documented failure modes, and fallback behaviour. The model leaves the notebook and runs in production.
Drift detection, scheduled retraining, and evaluation against live data. Models degrade if left unmonitored; we keep them under observation.
Most of the value is in the last mile. A forecast that never reaches the procurement plan is only a report. We build the model and the path into the operational system that acts on its output as one engagement.
Commitments written into the statement of work, where they are enforceable.
Nothing leaves your environment for training without your explicit written approval.
Your cloud and your region. UAE data residency is the default, not an optional add-on.
Every production decision can be traced, with feature attribution and confidence scores logged alongside the output.
Inference costs, retraining cycles, and ongoing compute are modelled and budgeted before you sign, so month-four bills are expected.
The model recommends; a person decides on high-stakes actions. Thresholds are tuned per use case.
Model versions, training data signatures, and deployment events are captured because compliance reviews will need them.
A 30-minute call with an ML lead. Bring the operational problem; we will say whether it suits machine learning and roughly what a solution would require.