AI Consulting, Strategy & Model Selection
Most AI projects that fail do not fail because of bad engineering - they fail because the wrong problem was selected, the wrong model approach was chosen, or the deployment environment was not considered during design. We run structured AI consulting engagements covering use case prioritisation based on data availability and business impact, model selection between open-source, commercial API, and custom-trained options with honest trade-off analysis, data readiness assessment identifying gaps that need to be addressed before training can begin, architecture design for the full AI system including data pipeline, model serving, and monitoring, and PoC development to validate technical feasibility before a full engagement begins.
Machine Learning Model Development
Most ML projects fail not because of model quality but because of the gap between a notebook experiment and a production system that receives real data, updates over time, and feeds decisions that matter. We run the full ML lifecycle - data assessment and feature engineering, model selection and training across supervised, unsupervised, and reinforcement learning approaches, evaluation against business metrics not just accuracy scores, deployment through REST APIs and batch pipelines, and ongoing monitoring with automatic retraining triggers when model performance drifts. We have built ML models for demand forecasting, churn prediction, fraud detection, recommendation engines, risk scoring, and predictive maintenance across a range of industries.
Computer Vision Development
Computer vision models that work on curated benchmark datasets frequently underperform on real deployment data - different lighting conditions, camera angles, object occlusion, and image quality variations that were not in the training set. We build computer vision systems with production deployment in mind: data collection and augmentation strategies that anticipate real-world variation, model architectures selected for the accuracy-latency trade-off your deployment target requires, and post-training optimisation using quantisation and pruning for on-device and edge deployment. We have built object detection, image classification, instance segmentation, OCR, facial recognition, defect detection, and video analytics systems for industrial, retail, medical imaging, and security applications.
Natural Language Processing & Conversational AI Development
NLP applications - document processing, sentiment analysis, entity extraction, classification, and conversational interfaces - are where language models deliver the most direct and measurable business value. We build NLP systems covering document intelligence pipelines that extract structured data from unstructured text at scale, sentiment and intent classification models trained on domain-specific data, named entity recognition systems for legal, medical, and financial document processing, and conversational AI systems that go beyond FAQ matching to handle multi-turn dialogue, context tracking, and business process integration. We deploy NLP models as APIs, embedded modules, and batch processing pipelines depending on your throughput and latency requirements.
AI Integration & Deployment Services
Building a model is half the work. Getting it into a product, a device, or an operational system where it actually changes a decision or automates a task is the other half and it is where most AI projects stall. We deploy models as REST and GraphQL APIs, streaming inference endpoints, batch scoring pipelines, and on-device embedded modules depending on your latency, throughput, and connectivity requirements. We integrate AI into web and mobile applications, IoT platforms, embedded hardware, and enterprise systems - handling versioning, graceful failure, and latency management so the AI layer works as part of the product rather than as a fragile add-on. We have deployed AI across cloud, edge, and MCU-level hardware and understand the architectural constraints at each layer.
Generative AI & LLM Development
Generative AI delivers value when the model is built around the specific knowledge, tone, and task requirements of the business using it — not when it is an off-the-shelf API with a thin prompt wrapper. We build generative AI systems covering LLM fine-tuning on domain-specific datasets using LoRA and QLoRA techniques, Retrieval-Augmented Generation pipelines that ground model outputs in your actual knowledge base, AI agent systems that take multi-step actions using tool calling and function execution, and evaluation frameworks that measure whether the model does what your business actually needs. We work with open-source models including LLaMA, Mistral, and Phi alongside OpenAI, Anthropic, and Google APIs — selecting based on your accuracy, cost, latency, and data privacy requirements.
