AI & Automation
Models, agents and automated workflows placed where they remove real work — measured, not assumed.
Overview
Automation starts with the process, not the model: which step repeats, which decision needs judgement, and which errors are expensive to correct. A model is added only where an evaluation set shows it removes that work. Where a rule is enough, the rule is what ships.
- Process mapping before any model is chosen
- Document and form extraction in Arabic and English
- Retrieval over internal documents and systems of record
- Agents with defined tools and a human approval step
- Classification, routing and queue triage
- Evaluation sets, regression runs and drift monitoring
- Rule-based workflow automation where a model is not needed
What you receive
- 01An automation map of the process, marking each step as rule, model or human judgement.
- 02An evaluation set built from real cases, including the ones the current process gets wrong.
- 03The inference pipeline with prompts, retrieval indexes and model versions under version control.
- 04Human review queues and an escalation path for the cases the system declines.
- 05A dashboard reporting accuracy, cost per run and the volume of work removed.
- 06A runbook for retraining, changing a prompt and rolling back to the previous version.
- Python
- PyTorch
- Hugging Face Transformers
- LangGraph
- pgvector
- Temporal
- MLflow
- Ragas
- Label Studio
- OpenTelemetry
How the engagement runs
An engagement starts with the process as it runs today, watched rather than described. Each step is then marked as a rule, a model call or a decision that stays with a person. The rule steps are automated first. The evaluation set is written before the model is chosen, so every later change is measured against cases that already exist. What ships first runs behind a human approval step, removed only where the evaluation and the logs agree. After launch the same evaluation runs against live traffic — a model that was right at release drifts when the inputs change.
- The repetitive part of the process runs without a person waiting on it.
- A model can be retrained, replaced or removed without rebuilding the workflow around it.
- Whether the automation helps is answered by the evaluation set and the production logs, not by a demo.
- Where the system is not confident, the work reaches a person instead of a wrong answer reaching a customer.
Tell us what you are building.
Send the outline and we will come back with an honest read on scope, sequence and what it takes to run it in production.