AI and Machine Learning Development London

Softomate builds custom machine learning models, NLP pipelines, computer vision systems and predictive analytics platforms for UK businesses across finance, healthcare, property and professional services. Python, TensorFlow, PyTorch and scikit-learn are the primary development tools. Azure ML and AWS SageMaker host production models with automated retraining pipelines. Retrieval-augmented generation and LLM fine-tuning extend language model capabilities for document-heavy workflows. UK GDPR data minimisation, purpose-limited training sets and audit logging are built into every project plan. Softomate clients commonly reduce manual classification time from hours per day to under five minutes after ML model launch. Model monitoring, drift detection and retraining schedules are scoped at delivery planning stage. A discovery workshop maps your data, business problem and compliance requirements before scope and pricing are confirmed.

Yes. Softomate builds predictive models in Python using TensorFlow, PyTorch or scikit-learn and connects them to Salesforce, HubSpot or bespoke CRMs through REST API and webhook patterns. Prediction outputs, confidence scores and recommended actions write directly into CRM objects in near real time. OAuth 2.0 and JWT authentication control which fields the model can read or update. Azure ML or AWS SageMaker host the model endpoint, handling inference requests at scale. UK GDPR controls limit which personal data fields the model can process during training and inference. Softomate clients typically see CRM-integrated ML predictions operational within six to ten weeks of discovery sign-off. Explore our API development and system integration services to understand how REST API patterns connect ML models to your existing platforms.

Retrieval-augmented generation retrieves approved source content at query time and uses a large language model to compose a grounded answer without altering the model weights. Fine-tuning updates the model parameters on a curated training dataset, changing the model itself to reflect domain vocabulary, tone or task format. RAG is faster to deploy and easier to update because re-indexing approved documents is quicker than retraining. Fine-tuning gives tighter control over output style and domain accuracy but requires more training data and longer delivery timelines. Softomate implements RAG using vector embeddings, semantic search and prompt guardrails for document-heavy workflows. Fine-tuning using Hugging Face and ONNX optimisation suits classification, summarisation and named entity extraction tasks. UK GDPR controls apply to training data selection and inference-time personal data access. A discovery session identifies which approach suits your use case, data volume and compliance needs.

Yes. Softomate ML projects include UK GDPR controls covering training data minimisation, purpose limitation, data subject rights and audit logging. Personal data used for model training is identified, mapped to a lawful basis and anonymised or pseudonymised where possible. UK GDPR-compliant data pipelines on Azure ML or AWS SageMaker restrict which fields enter training datasets. Model inference endpoints serving personal data include UK GDPR access controls aligned to OAuth 2.0 permission scopes. Subject access request and deletion workflows are designed into data pipeline architecture before training begins. ICO guidance on automated decision-making and explainability is considered during model design for high-stakes predictions. Softomate recommends a data protection impact assessment for models that process sensitive or regulated personal data. A compliance review session confirms data categories, retention rules and risk controls before project approval.

Most custom machine learning projects for UK businesses take six to fourteen weeks from discovery to production deployment. Data assessment and UK GDPR compliance mapping usually need one to two weeks. Feature engineering, model selection and initial training on Python, TensorFlow or PyTorch take two to four weeks depending on data quality and volume. Model evaluation, hyperparameter tuning and bias testing add another one to two weeks. REST API or Azure ML endpoint build, integration testing and UAT normally take five to ten working days. RAG pipelines with vector indexing or LLM fine-tuning on Hugging Face can extend timelines by two to four weeks. Softomate clients typically see production ML model predictions active within ten weeks of discovery. A scoped workshop gives a more precise schedule once data, infrastructure and compliance requirements are confirmed.

RAG company knowledge assistant starts from £6,000 for implementation. Agreed document collection, grounded answers with sources, access rules and evaluation. Monthly care starts from £399/month. Prices exclude VAT. Required licences, hosting and usage are confirmed separately before you commit. Care is optional after client-managed handover; additional development is quoted separately. Fixed-price projects start from £8,000.

Yes. Softomate deploys machine learning models on AWS SageMaker and Azure ML using managed endpoint hosting, auto-scaling inference and automated retraining pipelines. SageMaker endpoints serve real-time predictions through REST API calls with JSON payloads, supporting Python scikit-learn, TensorFlow and PyTorch model artefacts. Azure ML managed endpoints offer similar REST API inference with ONNX model optimisation for lower-latency production serving. MLOps pipelines on both platforms handle model versioning, drift detection alerts and scheduled retraining from refreshed datasets. UK GDPR data residency controls are configured to keep personal data within approved Azure or AWS UK regions. A technical discovery session confirms cloud platform preference, data volume and compliance requirements before infrastructure design begins.