AI MODELS & MULTIMODAL SYSTEMS
We help organisations select, adapt, train and deploy AI models for specialised applications — from language and vision models to multimodal systems, private open-source deployments and production inference infrastructure.
MODEL & DEPLOYMENT STRATEGY
The best AI architecture is not always the largest model or the most popular API. Data sensitivity, performance, cost, customisation and operational requirements all influence the right model and deployment approach.
For applications where speed of implementation, access to leading models and managed infrastructure are important.
Fast implementation · managed infrastructure · usage-based cost · externally hosted inference
Deploy open models on dedicated GPU infrastructure in a private cloud environment.
Private inference · infrastructure control · model choice · custom optimisation · production serving
Deploy models inside customer-controlled infrastructure when information or operational requirements make external inference unsuitable.
Controlled environment · tighter network boundaries · private data handling · customer-managed infrastructure
Hybrid approaches are also possible, for example combining private models for sensitive workloads with managed models elsewhere.
WHAT WE BUILD
The right model solution depends on the task, the data, the deployment requirements and how the system will be used in practice.
Evaluate open-source and commercial models against the use case, data, quality requirements, deployment constraints and operating cost.
Supervised fine-tuning, instruction tuning, LoRA/QLoRA and other adaptation strategies where they materially improve domain behaviour.
Classification, detection, visual inspection, image understanding and domain-specific vision workflows.
Applications combining text, images, documents, structured information and other inputs within one system or workflow.
Production inference APIs, private model serving, GPU infrastructure, model versioning, monitoring and integration into software products.
MODEL ADAPTATION
Depending on the use case, the right approach may be prompt and inference optimisation, fine-tuning an existing model, adapting a vision model, continued training on domain data, or training a specialised model around a defined task.
We choose the least complex approach that can reliably meet the required quality.
MULTIMODAL AI
Multimodal systems combine several forms of information to understand a situation more completely — for example, images alongside metadata, documents alongside structured records, or visual input alongside user context.
Multimodal Processing Architecture
Multimodal AI system
Decision / Workflow / Product feature
FROM MODEL TO PRODUCTION
Compare candidate models against representative data and requirements.
Fine-tune or optimise where the baseline model does not meet the use case.
Use test sets, metrics and expert review to understand real performance.
Serve the model through the appropriate API, GPU infrastructure or private environment.
Track inference behaviour, performance, cost, versions and feedback.
Human expertise remains part of the system where it matters.
For specialised or higher-risk applications, expert review, confidence thresholds and feedback workflows can remain part of the solution rather than treating every model output as automatically correct.
SELECTED WORK
Production AI Pipeline · Inference Deployment
Engineered a Python-based semantic matching service and deployed it into production with FastAPI endpoints, secure service communication, background workers, and automated triggers.
Read case studyApplied AI Document System
Designed and engineered an AI-assisted document generation application with structured prompt workflows, formatting constraints, and responsive web delivery.
Read case study: BriefyMateApplied AI Engineering & Infrastructure
Python · PyTorch · Hugging Face · LoRA · QLoRA · vLLM · Ollama · FastAPI · GPU Cloud & Dedicated Inference · Docker · CI/CD
RELATED SERVICES
Need private knowledge retrieval?
Grounded retrieval, enterprise document search and traceable answers.
Need the AI to take actions across systems?
Connected AI agents, tool use and operational automation.
MODEL TO PRODUCTION
Tell us about the use case, data, deployment constraints and quality requirements. We can help evaluate the right model, adaptation approach and production architecture.
