Discovery call
A 45-minute video call where we learn about your data, your goals and the constraints you operate under. No charge, no commitment.
Artificial Intelligence services in detail
Below you will find exactly what each service includes, how long it takes and what you walk away with at the end.
Custom model training
We build supervised and semi-supervised machine-learning models tailored to your data. This is not a drag-and-drop AutoML demo: we write the feature engineering, choose the architecture and run ablation experiments until the model meets your accuracy threshold.
The engagement typically runs six to ten weeks and covers:
- Data audit: schema review, missing-value analysis, label-quality check
- Feature engineering and selection using domain knowledge you provide
- Model selection across gradient-boosted trees, neural nets and ensemble methods
- Hyperparameter tuning with cross-validation
- A written evaluation report with precision, recall, F1 and business-metric mapping
- Containerised model (Docker) with a REST inference endpoint
Conversational AI and chatbots
We fine-tune large language models on your product documentation, internal wikis and past support transcripts. The result is an assistant that gives specific, sourced answers rather than vague summaries.
Each chatbot deployment includes:
- Knowledge-base ingestion: PDFs, HTML pages, Confluence spaces, Notion databases
- Retrieval-augmented generation (RAG) pipeline with citation tracking
- Confidence scoring: the bot flags low-confidence replies and offers a human handoff
- Integration with your website, Slack workspace or Microsoft Teams tenant
- A management dashboard where your team can review conversations and update source material
Setup for a catalogue of up to 5,000 articles takes about three weeks. Larger knowledge bases may need an extra week for indexing and quality-assurance passes.
Computer vision
From quality-control cameras on a production line to document-layout analysis for legal firms, we train and deploy image-classification and object-detection models.
Typical deliverables:
- Annotated training dataset (we handle labelling if you do not have labels yet)
- Model trained on your images, not a generic pre-trained checkpoint
- Edge deployment option for on-premise cameras using NVIDIA Jetson or similar hardware
- Cloud deployment via a scalable API if latency requirements are relaxed
One food-manufacturing client uses our defect-detection model to inspect 1,200 items per minute on a conveyor belt. The false-positive rate sits below 0.3%, which means fewer good products are discarded.
Data pipeline and automation
Many AI projects fail not because the model is bad but because the data never arrives in the right format at the right time. We build the plumbing: extraction scripts, transformation jobs, scheduling and monitoring.
What this looks like in practice:
- Connectors to your databases, CRMs, ERPs and cloud storage
- Scheduled ETL jobs that clean, deduplicate and validate incoming data
- Alerting when data quality drops below a threshold you set
- Orchestration with Airflow, Prefect or a lightweight cron-based runner depending on complexity
We also automate post-model tasks: writing predictions back to your systems, triggering emails, updating dashboards. The goal is zero manual steps between raw data and business action.
How an engagement works
Every project follows the same four-phase structure, regardless of scope.
Proposal and data review
We send a written proposal within five working days. If you accept, we begin with a paid data-audit sprint lasting one to two weeks.
Build and iterate
Weekly demos so you see progress in real time. We adjust direction based on your feedback rather than disappearing for months.
Deploy and support
We deploy to your infrastructure, hand over documentation and provide 90 days of included support. Ongoing retainers are available if you want continued monitoring.