Build
AI Consulting
A paid assessment of where AI would pay off in your business. We audit your data, score use cases on effort and return, and deliver a ranked roadmap with costs.
Services / What we do
AI Consulting & Development
Services
Service portfolio
We Build AI Systems That Run On Your Business Data
We build AI systems that run on your real data, inside the software you already use. We check what your data supports before choosing a model, agree how success gets measured, and deploy with monitoring attached. MIT found 95% of generative AI pilots return nothing measurable, usually because those steps were skipped.
Build
Generative AI Development
Applications built on large language models: document processing, summarisation, drafting, knowledge assistants and customer-facing chat. We fine-tune on your data where your terminology demands it, otherwise prompt and retrieval design.
Mobilise
AI Agent Development
Systems that complete multi-step tasks rather than answer questions, calling your tools and APIs as they go. We set what it does unsupervised, where a person approves, and what gets logged.
AI
Machine Learning
Predictive models trained on your historical data: demand forecasting, churn and credit risk scoring, fraud detection, recommendations, defect detection. Cheaper to run than a language model doing the same job.
Experience
LLM Integration Services
Adding a provider such as OpenAI, Anthropic or Azure OpenAI to software you already run: function calling, streaming, provider fallbacks, cost and rate-limit controls, and testing that shows whether a prompt change helped.
Connect
RAG Development
A retrieval layer that lets a model answer from your own documents instead of its training data: ingestion, chunking, embeddings, vector search and re-ranking, with your access permissions enforced at retrieval.
Ways We Bring These Teams Together
The Engagement Models We Offer
Six ways to work with us.
Agile Pods
Data, ML and QA engineers in two- to four-week sprints. Suits a defined AI feature with a fixed end date.
Product Development
A full-cycle team taking an AI feature from use case to production, including the evaluation and monitoring that keeps it working.
Dedicated Remote Centres
Long-term AI and data engineers working only on your roadmap. Webmyne handles recruitment, payroll, legal and HR.
On-Demand Staffing
One or two specialists for a fixed period, such as an ML engineer for a modelling phase.
Fixed Price
Scope and budget agreed before work starts. Suits bounded builds such as a RAG assistant over a known document set.
Support & Optimisation
Monitoring, retraining and retrieval tuning after launch. AI systems lose accuracy as your data changes, so this is upkeep, not an add-on.
Our Process
The five stages every project runs through.
Discover
Your goal, your data and its real condition, with the gaps named.
Define
The approach, architecture, integration points, and the number success is measured against.
Build
Sprint development, with grounding, access controls and guardrails built in, not bolted on.
Test
Accuracy measured against that number, plus security, bias and failure-case testing.
Launch & support
Deployment, handover documentation, and monitoring for drift in accuracy, cost and usage.
Ready to put AI to work on a real problem?
Tell us the decision you want made faster, or the work you want taken off people's hands. We will tell you whether your data supports it, and what it would take if not.
Plan your projectFAQs
If it is scattered, inconsistently labelled or missing history, a model will produce confident wrong answers. Our AI Consulting engagement audits it, tells you what to fix first, and whether a smaller use case can start now.
A RAG assistant over an existing document set usually runs eight to twelve weeks. Machine learning depends on data preparation, normally the longest phase. Agent projects take longer, because of approval design and testing.
For a forecast, a score or a classification, machine learning is faster, cheaper to run and easier to measure. Generative AI suits unstructured work: reading documents, drafting text, answering questions. Many systems use both.
No. It stays in your environment or cloud account and trains no shared model. Where a provider API is used, we apply the settings that exclude your inputs, and record that in the contract.
You do, on full payment: model weights where we trained them, source code, pipelines, prompts and documentation. Third-party provider accounts are held in your name, so nothing stops working when the engagement ends.