M3CA / AI practice
Company AI Services
From the first useful workflow to a governed company model. Choose the level of engineering your business actually needs.
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.
Engineering capabilities
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.
Built 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.
Choose by task, not by hype
Our tools and the leading assistants.
M3CA builds applications and company workflows around suitable models. We do not claim our own rival to ChatGPT, Claude or Grok, or that one tool wins every task. Compare capabilities, evidence, access controls and review effort on your own examples.
ChatGPT
General writing, reasoning, file analysis and research, with tools depending on plan.
A flexible assistant. Company integrations, access controls and evaluation still need to be designed around the workflow; M3CA can integrate OpenAI rather than replace it.
Plus: US$20 per month, billed monthly
Official provider pricingClaude
Document work, structured writing and coding with plan-dependent tools and usage limits.
A general assistant and model provider. M3CA adds the company-specific interface, source connections, testing and handover where the task requires them.
Pro: US$20 monthly, or US$200 billed annually
Official provider pricingGrok
Research with web and X search, plus broader assistant tools depending on plan.
Useful for investigating current material. Live social content is not verified company evidence; source checking and approved company context remain essential.
SuperGrok: US$30 per month
Official provider pricingM3CA Atlas
Company document retrieval with passage citations and permission-aware answers. Product status: Beta.
A company knowledge application, not a rival foundation model. Its purpose is to connect an appropriate model to approved documents and the person asking.
Tailored quote for scope, access and deployment
Explore M3CAM3CA CNC
Opportunity listings with contextual cover-letter and tender drafting. Product status: Live.
The listing and your supplied profile become the writing context, with drafts kept for review and reuse, rather than beginning in an empty chat.
Request an access or support quote
Explore M3CAStudio AI & the M3CA Console
M3CA service discovery alongside enquiries, proposals, CRM and team workflows.
Studio AI assists with M3CA information; the Console records the operational work. HubSpot contact sync and staff replies are workflow integrations, not autonomous AI decisions.
Discuss company integration and team access
Explore M3CAVendor prices checked 5 October 2026. Listed in US dollars for individual subscriptions, not a company deployment quote. Local currency, taxes, checkout and platform pricing can differ; usage limits apply and API usage is billed separately. Consumer access does not establish suitability for confidential company data. M3CA services remain quote-only.
First-party case studies
The work, without invented results.
Live product
CNC: from opportunity to reviewed draft
The task
A role or procurement notice needs a response grounded in the applicant's own experience or capability statement.
The implementation
CNC combines the selected listing with supplied profile material to prepare a cover letter or tender draft, saved for the user to review and reuse.
Evidence boundary
First-party product example. Inspect the published CNC workflow; no client win rate or revenue uplift is claimed.
In-house workflow
M3CA: a training request becomes a CRM record
The task
A training brief needs to reach the team without losing the requested tools, group size or follow-up history.
The implementation
The training form records the brief in Enquiries, sends an owner alert and links the person in HubSpot. Staff status and reply notes follow the existing CRM workflow.
Evidence boundary
First-party operational case study. Booking, owner email and HubSpot contact linkage were tested; no conversion or time-saving percentage is claimed.
Beta product
Atlas: inspectable company knowledge
The task
A general answer is not enough when staff need to inspect the policy or passage behind it.
The implementation
Atlas is designed around source-linked document retrieval and the asker's permissions, with an evaluation harness for representative company questions.
Evidence boundary
Beta product design case study, not a verified client deployment or a measured accuracy claim. Availability and connector scope are agreed before use.
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