The six phases
Most projects take between eight and sixteen weeks from first call to production deployment. Smaller scopes (a single model, one data source) can ship in as few as four weeks. Here is what each phase involves.
1. Discovery call
We spend 45 to 60 minutes on a video call understanding your business problem. What decisions do you need to make? What data do you already have? What does success look like in numbers? We take notes and send you a written summary within 24 hours. This call is free and carries no obligation.
2. Data audit
Our data engineers connect to your systems (databases, APIs, file exports) and assess data quality. We look at volume, completeness, labelling accuracy and any privacy constraints. The output is a short report that tells you honestly whether you have enough data to build the model you want. If you do not, we recommend what to collect and how long it will take. This phase usually lasts one to two weeks.
3. Proof of concept
We build a lightweight version of the model on a representative sample of your data. The goal is to prove feasibility, not production readiness. You see accuracy metrics, confusion matrices or error distributions depending on the task type. We present results in a 30-minute walkthrough and discuss whether the performance justifies moving forward. Typical duration: two to three weeks.
4. Full model development
Once you approve the proof of concept, we train the model on the full dataset. This is where we invest time in feature engineering, hyperparameter tuning and cross-validation. We test on held-out data that the model has never seen. If accuracy falls below the threshold we agreed in phase three, we iterate. You receive weekly progress updates with charts showing how performance is changing.
5. Integration and deployment
We package the model as a REST API, a batch job or an embedded module, depending on how your systems need to consume predictions. Our engineers write integration code, set up authentication, build monitoring dashboards and run load tests. We deploy to your cloud environment (AWS, Azure or GCP) or on-premise if required. This phase includes two weeks of parallel running alongside your existing process so you can compare outputs side by side.
6. Monitoring and retraining
After go-live we monitor model drift, latency and error rates. When accuracy drops below a defined threshold, an automated pipeline retrains the model on fresh data and runs validation before promoting the new version. We provide three months of included support. After that you can extend with a monthly retainer or hand off to your internal team using the runbooks we write during deployment.
Why a phased approach matters
We have seen companies spend six figures on an AI system that never left the lab. The reason is almost always the same: someone skipped the data audit and built a complex model on data that was incomplete or mislabelled.
Our phased structure forces honest checkpoints. At the end of phase two, if the data is not ready, we say so. You lose a week of consulting fees instead of six months of engineering time.
The proof of concept in phase three is equally important. It answers the question "can this data predict this outcome at all?" before anyone writes production code. About one in five projects pivot at this stage, either to a different modelling approach or a different business question entirely. That flexibility is the point.
By the time we reach phase four, both sides know exactly what we are building, what accuracy to expect and how the output will be consumed. Surprises are rare.
Project outcomes in numbers
These figures are drawn from our last 18 completed engagements, covering the period from January 2023 to March 2025.
of proof-of-concept models met or exceeded the agreed accuracy threshold
median time from discovery call to production deployment
of projects delivered within the contractual budget (fixed-price milestones)
average retraining cycles per model in the first year of operation
Tools and technologies we use
We are not tied to a single vendor or framework. The choice depends on the problem, the scale and your existing infrastructure. That said, here are the tools that appear most often in our projects.
Machine learning
Python is our primary language. For tabular data we lean on scikit-learn, XGBoost and LightGBM. Deep learning tasks use PyTorch. We fine-tune large language models with Hugging Face Transformers when NLP scope demands it. Computer vision projects typically start with a pre-trained ResNet or YOLO backbone and get fine-tuned on your labelled images.
Data engineering
Apache Airflow or Prefect orchestrate our pipelines. Data storage depends on volume: PostgreSQL for structured data under a few hundred gigabytes, Snowflake or BigQuery for larger analytical workloads, and S3 or Azure Blob for unstructured files. We write dbt models for transformation layers so your analysts can understand and extend the logic after handoff.
Deployment
Models are containerised with Docker and served behind FastAPI or Flask. Kubernetes handles scaling for high-throughput endpoints. We use MLflow for experiment tracking and model versioning. Monitoring runs through Prometheus and Grafana, with custom alerts for prediction drift and latency spikes.
Security
All data in transit is encrypted with TLS 1.3. Data at rest uses AES-256. We follow the principle of least privilege for cloud IAM roles and rotate credentials every 90 days. If your industry requires specific compliance (ISO 27001, NHS DSPT), we work within those frameworks from day one.
Ready to start?
Book a free discovery call and we will tell you within a week whether your data can support the AI project you have in mind.
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