M3CA / AI practice
Company AI & Model Engineering
M3CA helps organisations choose, integrate and govern the right AI, then trains their people to use it well. We connect company knowledge and workflows to Lovable, OpenAI and ChatGPT, Anthropic and Claude, Google Gemini, xAI and Grok, plus suitable open and self-hosted models.
An accountable AI partner
Useful intelligence. Real business work.
Integration, adoption and model engineering belong together. We start with a business task, test the quality of the result and leave your people able to run it.
See the full breakdown
Integrate a leading model
The fastest route to value. We connect an established model to the right company data, interface and workflow, then train the people responsible for it.
Adapt or fine-tune
When retrieval and prompting are not enough, we prepare a governed dataset and tune a model for a narrower language, behaviour or specialist task.
Where company AI earns its place
01
Company knowledge, ready to answer
Connect approved policies, procedures and project documents to a knowledge assistant. Answers point back to source material, respect each person's access and flag gaps rather than inventing a company policy.
02
Enquiries into working briefs
Help your team organise an incoming enquiry, identify missing information and prepare a draft response. Staff approve what goes to the customer; CRM records and follow-up actions stay traceable.
03
Documents into decisions
Extract structured information from reports, applications or tender packs, compare it against agreed criteria and present an evidence-backed summary for a human decision-maker.
04
Operations with fewer handovers
Connect repeatable tasks across the systems your team already uses. Agents operate with limited permissions, approval checkpoints and a clear stop condition when the task falls outside their remit.
05
Content with a review trail
Draft proposals, tender responses and business writing from approved source records. Review factual claims, tone and required sections before publication or submission.
06
A specialist company model
Evaluate whether your task needs retrieval, fine-tuning or a private deployment. Specialist models are tested against a held-out dataset, with rights to the training data and ongoing ownership agreed before work begins.
What we build and integrate
01
AI strategy and opportunity mapping
We map decisions, delays and repetitive work, rank the opportunities by value and risk, then choose where AI should and should not be used.
02
Company knowledge and agents
Permission-aware assistants, customer agents and employee tools answer from your documents, systems and live business context, with citations and human escalation.
03
AI inside your services
We embed intelligence into websites, applications, CRM, ERP and internal workflows, connecting useful actions instead of adding another isolated chatbot.
04
Team training and adoption
Role-specific workshops teach leaders and teams prompting, workflow design, responsible use, verification and the practical limits of each model.
05
Fine-tuning and custom AI models
We prepare specialist datasets, adapt suitable open or commercial models, evaluate them against real cases and deploy them privately where required.
06
Foundation-model programmes
For organisations with the data, compute and investment, we scope dataset design, training infrastructure, research, safety evaluation and production operation for a new foundation model.
07
Model and provider selection
Lovable, ChatGPT and OpenAI, Claude, Gemini, Grok and open models are assessed by task, quality, privacy, latency and cost. We combine them when one model is not the best answer for everything.
08
Governance and evaluation
Permissions, audit trails, test sets, monitoring, cost ceilings, security controls and human review are designed before rollout and measured afterwards.
The delivery path
01
Integrate a leading model
The fastest route to value. We connect an established model to the right company data, interface and workflow, then train the people responsible for it.
02
Adapt or fine-tune
When retrieval and prompting are not enough, we prepare a governed dataset and tune a model for a narrower language, behaviour or specialist task.
03
Train a foundation model
A research and infrastructure programme for organisations with a defensible dataset, substantial compute and a reason existing models cannot meet the requirement.
04
Operate and improve
We monitor quality, adoption, safety and cost, retrain staff when workflows change, and keep the model stack current without locking the company to one provider.
Need it fully private?
Train and run on M3CA Sovereign AI.
Company AI covers the people and the models. Sovereign AI is where they can run: a private UK data centre, isolated from the public internet, where your model learns only your company's language.
Explore Sovereign AIBuilt inside M3CA
01
Studio AI and M3CA Atlas
Cited answers across the M3CA ecosystem and permission-aware retrieval over company knowledge show how useful AI stays grounded and inspectable.
02
M3CA Console and Marketing
Enquiries, CRM activity, campaigns, operations and reporting show how AI belongs inside working business systems, with people retaining control.
03
M3CA CNC and Skills Hub
AI-assisted tender writing, CVs, cover letters and learning workflows turn structured records into practical outputs that users can review and improve.
What an engagement delivers
01
A scoped opportunity and baseline
A prioritised workflow map, starting quality measures, data and access requirements, and acceptance criteria. You can see what the pilot must prove before deciding to expand it.
02
A working, evaluated pilot
An integrated workflow, representative test cases, source and permission checks, and a record of failure modes. We compare the result with your existing process rather than relying on a polished demo.
03
A handover your team can use
An operating guide, named responsibilities, staff training and an agreed monitoring plan. Model usage, review checkpoints and maintenance requirements are made explicit.
Before we begin
Good questions. Clear answers.
Do we need to train a model from scratch?
Usually not. We begin by testing established models with good prompting and controlled retrieval. Fine-tuning is appropriate when you have a repeatable specialist task and suitable examples. A foundation model requires a separate research case, substantial compute and a defensible dataset.
Can AI use our private company information?
Only through an agreed design. We review data classification, provider terms, retention, hosting and user permissions before connecting a source. Private deployment is an option where the requirement and model support it, not a blanket promise that every tool is private.
How do you judge whether it works?
We agree representative tasks and measure answer quality, source accuracy, human review effort, workflow completion and model usage. A pilot must meet its acceptance criteria before wider rollout. No universal saving or accuracy percentage is promised.
Start with a conversation
Put your company AI in motion.
Tell us about your team and the work you want to improve. We will agree a tailored scope and suitable dates with you.
Book a 20-minute fit call