Own the AI operating layer of your business.
MLX turns your systems, data, processes and institutional knowledge into an AI-ready operating layer you control. Use Claude, OpenAI, open models or your own infrastructure without locking your business logic inside one provider.
Product
AI operating layer
Deploy
Cloud · VPC · On-prem
From
Mercury Labs · London
Do not rent your business back from an AI lab.
Your company already has the raw material: accounting data, CRM records, emails, documents, dashboards, spreadsheets, approvals, exceptions and the judgement of experienced people.
MLX turns that material into a governed operating layer for AI: connected systems, structured views, versioned skills, business rules, model routing, audit trails and review loops.
Why now
The result is not another chatbot. It is a business-owned layer that any AI model or interface can use.
- Own
- Connect accounting, CRM, documents, email and operational systems into an AI-ready layer that stays under your control.
- Any
- Claude, OpenAI, open-weights or self-hosted models. Your workflows and business logic do not belong to one vendor.
- VPC
- Run managed, in your VPC or on-prem when sensitivity, residency or continuity demands customer-controlled execution.
- Loop
- Every reviewed workflow can improve your owned skills, rules and operating knowledge instead of a vendor's opaque history.
The business record
Model or provider
or on-prem
Review becomes knowledge
Own the record, the playbook and the learning loop.
Data structuring is part of the job, but it is not the whole offer. MLX gives AI a governed map of how your business works, then keeps that map under your control.
- 01
Own the business record
Connect accounting, CRM, documents, email, warehouse and operational systems into an AI-ready layer that stays under your control.
- 02
Own the playbook
Capture how work actually happens: rules, exceptions, review steps, reports, approvals, caveats and judgement calls.
- 03
Use any model
Route work across Claude, OpenAI, open-weight or self-hosted models without rebuilding the workflow layer underneath.
- 04
Run where the data lives
Deploy managed, in your VPC or on-prem depending on sensitivity, residency, security and continuity requirements.
- 05
Control the cost curve
Use cheaper models for high-volume work and frontier models only where they earn it, without rewriting the business process.
- 06
Improve through review
Every run leaves evidence. Reviewed misses become better skills, better rules and better company-owned operating knowledge.
From scattered systems to an AI-ready business layer.
MLX sits between your private operating systems and the models. It builds the views, skills and rules that let AI work with the business instead of guessing from a chat prompt.
- 01
Connect the systems
Xero, SUN, Overture, Gmail, Notion, SharePoint, CRM, warehouse and internal tools connect into one governed operating layer.
- 02
Build the operating view
MLX turns raw records into channels, tables, dashboards and curated views that match how the business asks questions.
- 03
Encode the playbook
Skills capture recurring workflows. Overlays capture source-of-truth rules, caveats, terminology and business-specific judgement.
- 04
Run through any model
The same business layer can use different models for different jobs: fast, cheap, private or frontier-grade.
- 05
Expose useful surfaces
Answers, docs, tables, dashboards, channels, apps and external assistants can all use the same company-owned operating layer.
- 06
Keep learning inside the company
Reviewed answers improve your owned skills and rules, not a model provider's private memory or an ungoverned chat history.
AI adoption is not one step.
Some companies are still introducing safe assistant use. Others need board-level visibility across systems, skills and cost. MLX meets the business at the stage it is actually in.
- 01
Start using AI safely
Most teams are still experimenting with generic assistants, unclear policies and little connection to live operating systems.
- 02
Map how the business works
Capture the recurring workflows, judgement calls, reports, approvals and exceptions that make the company run.
- 03
Own the data layer
Connect legacy systems, documents, SaaS tools and databases without handing the operating record to one model provider.
- 04
Encode skills and processes
Turn repeated work into versioned, reviewable skills that can be run, tested and improved.
- 05
Optimise models and cost
Route each workflow to the right model, provider and deployment target as the market changes.
Start with one workflow. Build the operating layer from there.
The first deployment should prove the pattern: connect real systems, create useful operating views, encode the workflow, and keep the review loop inside the company.
- 01
Choose the first workflow
A forward-deployed engineer maps the source systems, repeated decisions, reports, exceptions and controls around one valuable workflow.
Week 1
- 02
Connect read-only systems
Bring the required data sources into MLX with scoped permissions. Read-only analysis lands first so evidence and access are clear.
Week 2
- 03
Author views and skills
Build the channels, tables, dashboards, skills and overlays that make the workflow repeatable and reviewable.
Week 3
- 04
Run, review and expand
Put the first workflow into use, review the evidence trail, then expand to adjacent workflows, models and deployment requirements.
Week 4
The questions a buyer will ask.
Control, cost, regulated use, model choice and where the business record lives — the objections we hear most when leaders evaluate serious AI adoption.
Your data stays under your control. MLX connects to approved systems, builds governed views and starts read-only by default. It does not turn your operating record into a model provider's private data estate.
Yes. The agent worker is designed for customer-controlled execution. Run it in our managed cloud, in your VPC, or on-prem when residency, sensitivity or compliance requires it. The same control plane works across deployment models.
No. Your workflows, skills, rules and reviewed corrections live in MLX, not inside one model provider. Claude, OpenAI, open-weight and self-hosted models become routing choices.
You set the marginal cost. Each skill specifies which model it runs against, so you compose cheap models for high-volume work and frontier models only where they earn it. Open-weights and self-hosted models are first-class — the cost of running a workflow is a routing decision you control, not a per-seat SaaS curve we control.
Those are broad assistants. MLX is the company-owned operating layer underneath them: source-system access, governed views, skills, rules, audit trails and model routing. The business logic stays portable.
The current model keeps execution close to the data and starts with restricted, read-only capabilities. The agent worker can run inside your environment, scoped to specific data sources. That's a much more credible path for regulated environments than a generic cloud AI assistant.
No. The strongest initial use case is analysis and answer generation against approved data — not autonomous write actions. Broader capabilities are opt-in per profile, with skills, profiles and a full audit trail governing what runs.
No. MLX connects to the systems you already run and creates AI-ready operating views around them. The point is not another warehouse project; it is a controlled layer that knows how the business works.
No. Mercury Labs embeds senior engineers with your team to wire up sources, map the workflow, author the first skills and keep the implementation grounded in how the business actually runs.
Map your first owned AI workflow.
Start with a real workflow, the systems behind it and the operating knowledge around it. MLX turns that into the first piece of your company-owned AI layer.
Get in touch
team@mercurylabs.io
Deploy
Cloud · VPC · On-prem
From
Mercury Labs · London