From first conversation to live system
Discovery call
We spend 45 to 60 minutes on a video call understanding your business problem. What decision are you trying to improve? What data do you already have? What does success look like in concrete, measurable terms? By the end of this call, we both know whether AI is a reasonable approach or whether a simpler solution would serve you better. There is no charge for this conversation.
Data audit and feasibility report
Our engineers review a representative sample of your data. We check volume, quality, labelling consistency and any gaps that could block model training. The output is a short written report (typically five to eight pages) that covers the proposed approach, estimated accuracy range, infrastructure requirements and a realistic timeline. This phase takes about one week and is billed as a standalone deliverable, so you can walk away with the report if you decide not to proceed.
Prototype and baseline model
We build a minimum viable model using your data and benchmark it against the simplest alternative, often a rules-based heuristic or a basic statistical method. If the AI model does not meaningfully outperform that baseline, we stop and discuss whether the project is worth continuing. This honest checkpoint has saved several clients from spending money on a model that would not have added value. The prototype phase runs two to three weeks.
Iteration and validation
With the baseline established, we iterate: feature engineering, hyperparameter tuning, architecture changes, additional data collection if needed. We run weekly demos so your team can see the model improving and flag any issues early. Each demo includes updated accuracy metrics on a held-out test set, so progress is visible and verifiable rather than abstract. This phase typically lasts two to four weeks, depending on complexity.
Deployment and integration
Once the model meets the agreed accuracy threshold, we package it as a containerised API and deploy it to your cloud environment or on-premise servers. We write integration code to connect it with your existing systems, whether that means pulling data from a warehouse, writing predictions back to a dashboard, or triggering alerts in a messaging platform. We also set up automated monitoring: latency, error rates, input-data drift. Deployment usually takes one to two weeks.
Monitoring and retraining
Models degrade over time as the real world changes. We monitor yours for twelve months, watching for accuracy drops, data-distribution shifts and edge cases the model handles poorly. When retraining is needed, we run it, validate the new model against the old one and swap it in with zero downtime. You receive a monthly performance summary by email. After twelve months you can renew the support contract or take over maintenance using the runbooks and CI/CD pipelines we hand over.
Tools and infrastructure we use
We are not tied to a single vendor. The stack depends on your constraints, but here are the tools we reach for most often.
Training and experimentation
PyTorch and scikit-learn for model development. MLflow for experiment tracking. Jupyter notebooks during exploration, then refactored into production-grade Python packages with unit tests.
Data engineering
Apache Airflow or Prefect for pipeline orchestration. dbt for transformation logic in SQL warehouses. We work with BigQuery, Snowflake, Postgres and Redshift, among others.
Deployment
Docker containers orchestrated by Kubernetes or AWS ECS. FastAPI for serving predictions. Terraform for infrastructure-as-code so environments are reproducible. GitHub Actions for CI/CD.
Monitoring
Evidently AI for data-drift detection. Prometheus and Grafana for system metrics. PagerDuty or Opsgenie for alerting when a metric crosses a threshold. Custom dashboards built in Streamlit or Retool for non-technical stakeholders.
Why a structured process matters
AI projects fail most often because of unclear scope, not because of bad algorithms. The feasibility report in phase two and the baseline comparison in phase three exist specifically to catch projects that should not proceed before significant money is spent.
Weekly demos keep your domain experts involved. They catch labelling errors, business-logic mistakes and edge cases that no data scientist would spot from the data alone. A demand-forecasting model we built for a wholesaler initially predicted negative demand for certain SKUs on bank holidays. The client's operations manager noticed this in the second demo, and we fixed the feature encoding the same week.
The twelve-month monitoring commitment means we share the risk. If the model drifts, we fix it. That alignment of incentives is deliberate.