AI Development and Integration Services
AI capabilities built into the systems your business already runs
What is AI Development and Integration?
Most AI value comes from adding intelligence to systems people already use, not from building a separate AI product. A support platform that drafts replies, an ERP that flags anomalous purchase orders, a claims system that extracts data from documents - the AI is a feature inside an existing workflow, which is exactly why it gets adopted.
That shapes how we build. The model matters, but so do latency inside a user interaction, graceful behaviour when the model is uncertain, cost per call at production volume, and a path for the system to improve as real usage reveals where it falls short. We build AI features that hold up under daily use rather than demonstrations that impress once.
What We Deliver
Custom Machine Learning Models
Prediction, classification, recommendation and pattern recognition models built with TensorFlow, PyTorch and scikit-learn, trained on your data and deployed with versioning, monitoring and a retraining path built in from the start.
Natural Language Processing
Chatbots, sentiment analysis, document understanding and semantic search using GPT, Claude, BERT and fine-tuned open models. Applied to support conversations, contracts, reviews and internal knowledge bases.
Computer Vision
Image recognition, object detection, visual inspection and OCR for quality control, security, medical imaging and document digitisation. Deployed to cloud or edge depending on latency and privacy requirements.
LLM Application Development
Retrieval-augmented generation over your own documents, structured output, tool use and multi-step reasoning. Includes the engineering that makes LLM features reliable - grounding, validation, fallbacks and cost control.
API Integration
Connect OpenAI, Anthropic Claude, Azure OpenAI and custom models into your CRM, ERP and internal applications through documented APIs, so intelligent features arrive without disrupting existing workflows.
Model Optimisation and Deployment
Compression, quantisation, caching and inference optimisation to bring latency and cost into a range that works at production volume. Frequently the difference between a viable feature and one too expensive to keep running.
Problems This Solves
“Per-call AI costs are unsustainable”
Caching, prompt optimisation, smaller models for simple cases and selective fine-tuning typically reduce cost substantially.
“The model produces confident wrong answers”
Grounding in your own data, output validation and explicit uncertainty handling keep the system honest about what it does not know.
“It is too slow for interactive use”
Streaming responses, model selection by task complexity and inference optimisation bring latency within interactive range.
“It does not fit our existing systems”
We build integration-first, adding AI as a service your current applications call rather than a platform they must migrate to.
How We Work
Define
Agree the use case, success criteria, latency and cost constraints.
Prototype
Build a working version against real data to validate the approach.
Engineer
Harden it - integration, error handling, evaluation, monitoring.
Deploy and Iterate
Release, measure real usage, and improve against what it reveals.
Talk to us and
get your project moving!
We will respond to you within 24 hours.
You'll be talking to product and tech experts.
NDA available upon request.