AI, Data & Intelligence
Custom AI Development
Bespoke machine learning, computer vision and language models trained on your proprietary data for problems that off-the-shelf AI products and general-purpose models cannot solve well enough.
Capability overview
What custom ai development involves
General-purpose AI models are remarkably capable, but some problems need more: detecting defects specific to your production line, predicting failures in equipment only you operate, or classifying documents in a vocabulary unique to your industry. In these cases a model trained on your own data is often more accurate, cheaper to run and easier to control.
Custom development starts by proving that a simpler approach will not work, because custom models carry ongoing costs for data labelling, retraining and monitoring. When they are justified, we build with PyTorch, TensorFlow, scikit-learn or gradient boosting libraries, and hand over models with full training code and documentation.
What is included
What custom development can include
Problem framing
The business decision translated into a precise prediction task, with the accuracy required to be useful and the cost of each kind of error defined.
Data preparation and labelling
Historical data cleaned and joined, and labelling guidelines written so annotators or domain experts produce consistent training labels.
Model development
Baseline models followed by more advanced architectures only when they show measurable gains on held-out test data.
Small model fine-tuning
Compact open models adapted to narrow tasks where they can match large model accuracy at a fraction of the inference cost.
Ownership and handover
Training pipelines, model cards and evaluation reports delivered, and intellectual property in models built on your data assigned to you.
How we work
How we deliver custom ai development
Feasibility study
Data sampled and a quick baseline built to estimate achievable accuracy before larger investment is committed.
Data pipeline
Repeatable extraction and feature pipelines built so training can be rerun as new data arrives.
Experimentation
Experiments tracked with tools such as MLflow so every result can be reproduced and compared.
Validation with experts
Model errors reviewed with domain specialists to find patterns the metrics alone do not show.
Productionisation
The chosen model packaged, deployed behind an API or batch job and connected to monitoring.
Related capabilities
Related capabilities in Artificial Intelligence
AI Application Development
Full-stack development of web and mobile applications with AI at their core, covering the interface, backend, model calls, streaming, error handling, access control and the cost of every request.
AI Solution Architecture
Architecture decisions for AI systems: which models to use and where they run, retrieval versus fine-tuning, data flows and permissions, latency and cost budgets, and how the whole system is evaluated.
AI Assistants
Rollout and governance of ready-made workplace AI assistants such as Microsoft 365 Copilot, Gemini for Google Workspace and ChatGPT Enterprise, so licences turn into real productivity rather than idle seats.
AI Agents
AI agents that complete defined tasks by calling your systems, such as updating a CRM record, triaging a ticket or reconciling a payment, with strict permissions, audit logs and human approval where it matters.
Explore further
Explore connected pages
Related services
Related solutions
Digital Transformation Solutions
Business and application solutions that modernise how work gets done. Acmez shapes digital…
Custom Business Solutions
Business and application solutions that modernise how work gets done. Acmez shapes custom…
Enterprise Application Solutions
Business and application solutions that modernise how work gets done. Acmez shapes enterprise…
Enterprise Integration Solutions
Cloud, security, integration, modernization and platform engineering solutions. Acmez shapes…
Where this applies
Healthcare & Life Sciences
Technology systems for regulated environments where privacy, auditability and continuity…
Manufacturing & Industrial
Connected operations, asset, field, supply chain and industrial platforms for complex operating…
Banking, Financial Services & Insurance
Technology systems for regulated environments where privacy, auditability and continuity…
E-Commerce
Digital platforms for customer experience, operations, commerce, content, marketing and service…
Questions & answers
Questions about Custom AI Development
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionIt depends on the task. Tabular prediction problems often work with a few thousand labelled historical examples, while image models may need hundreds of examples per defect type. The feasibility study gives a realistic answer for your case.
Models trained on your data for your project are owned by you, including training code and weights. Any reusable internal libraries we bring are licensed to you for use with the delivered system.
A feasibility study takes three to four weeks. A production-ready model with pipelines and deployment typically takes three to six months, depending largely on data preparation.
Feasibility studies are fixed price. Development is quoted in phases with clear exit points, so you can stop if results do not meet the agreed accuracy threshold.
Next step
Discuss custom ai development with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.