AI agents · automation · software
MLChronix builds AI agents that run your operations.
We design, build and run agents that read your data, use your tools and prepare the work, so your team approves decisions instead of doing the busywork. For startups and enterprises in the UK, US & UAE.
- agents online
- 13/13
- tokens/sec
- 4,210
- p50 latency
- 182ms
- guardrail checks
- 128,406
- founded
- 2020
- senior engineers
- 5
- products we run ourselves
- 3
- agents on shift
- 24/7
- Agent orchestration
- Retrieval over your docs
- Tool calling
- Evals & guardrails
- Human-in-the-loop approvals
- Workflow automation
- Data pipelines
- Voice agents
- Next.js platforms
- React Native apps
01 / Live demo
Watch it think, then answer.
Pick a question a manager might ask on a Monday. The agent plans, calls tools, checks its guardrails and only then answers.
pick a question
A scripted simulation of how our agents work: plan, call tools, check guardrails, then answer. Companies and numbers are fictional.
02 / Agent swarm
Many small agents beat one big chatbot.
Each agent does one job well. When work arrives, the nearest free agents pick it up, work together and hand it back resolved. Click the field to add work.
03 / Services
Agents first. The whole stack behind them.
AI is the headline, but it needs a platform, clean data and good apps around it. One team builds all of it.
AI agents & LLM systems
We build AI agents that take real work off your team: triaging requests, drafting replies, reconciling data, preparing reports. They plug into the tools you already use, follow your rules, and hand decisions to a person when it matters.
- Multi-step agents with tool use and memory
- Retrieval over your documents and databases
- Guardrails, audit logs and human approval steps
- Model-agnostic: Claude, GPT, open-source
Full-stack & SaaS platforms
Web apps and SaaS products in TypeScript, built to scale and easy to maintain.
ExploreAutomation & data pipelines
Connect your systems, move data reliably and remove the copy-paste work.
ExploreMobile apps
iOS and Android from one React Native codebase, through to store release.
ExploreMVP development
From idea to a launchable product in weeks, scoped to test what matters.
ExploreBlockchain & Web3
Smart contracts and Web3 front ends, with security reviewed before launch.
Explore04 / Built in-house
We run our own products, so we know what production means.
MLCarex is a healthcare platform we designed, built and still operate. No borrowed logos: this is our own work.
HealthTech · SaaS
MLCarex: healthcare booking, built and run in-house
MLCarex is our own healthcare platform. Patients search, compare and book verified doctors, hospitals, labs and physiotherapy centres, online or in person.
Read the case study- Search and book verified providers
- Online consultations and in-person visits
- Digital prescriptions and lab reports
- Multi-country, multi-currency support
- Patient ratings and provider reviews
05 / Channels
Ask in the chat you already use.
Your team tags the assistant like a colleague. It pulls the numbers, flags what looks wrong and offers the next step.
Lives where your team works
Tag the assistant in Slack, Teams, email or WhatsApp. No new tool to learn.
Answers with sources
Every number links back to the system or document it came from.
Acts, with approval
It can open tickets, draft emails and update records once a person says yes.
Follows your rules
Permissions, policies and tone of voice are set by you and enforced on every reply.
06 / Voice
A briefing you can just ask for.
Voice agents answer the phone, brief executives and take actions, with the same guardrails and approvals as text.
07 / Mission control
See every agent, every action.
A live view of what your agents are doing, what they have finished and what is waiting for a person to approve.
08 / Why not a chatbot?
Why not just a chatbot or a dashboard?
Chatbots talk. Dashboards show. Agents do the work, explain it and wait for your approval.
| capability | chatbot | dashboard | mlchronix agents |
|---|---|---|---|
| Takes action in your tools | No | No | Yes, with approval |
| Joins data across systems | Rarely | Yes | Yes |
| Explains why, in plain English | Sometimes | No | Yes, with sources |
| Spots problems before you ask | No | Only if you look | Yes, and alerts you |
| Human approval built in | No | n/a | Yes |
| Quality measured continuously | Rarely | n/a | Yes, with evals |
09 / ROI calculator
What is the busywork costing you?
Move the sliders to match your team. It is rough on purpose: the real number comes from looking at the work together.
hours / year
2,208
value / year
£77,280
working days
294
Assumptions: 46 working weeks a year, 7.5-hour days, and time handed back valued at the loaded hourly cost. This is an estimate to frame the conversation, not a quote or a promise.
Validate this with us10 / Architecture
Channels in, checked actions out.
Requests arrive from wherever your team works. An orchestrator plans, a team of agents does the work, and guardrails check every step against your tools and data.
channels
- Slack & Teams
- Web chat
- Voice
orchestrator
liveRoutes each request to the right agent, plans the steps, and checks every action before it runs.
guardrails
- PII redaction
- Policy rules
- Human approval
- Audit log
- Rate limits
agent team
- Support
- Finance
- Ops
- Sales
- Data
- Compliance
tools & data
- CRM
- Helpdesk
- ERP & accounting
- Data warehouse
- Docs & drives
- Internal APIs
11 / Process
Small steps, real data, early proof.
- step 01
Discover
We find the workflow with the clearest payback and agree one success measure.
- step 02
Design
Agent roles, tools, data access and approval points, written down before code.
- step 03
Prototype
A working agent on a sample of your real data, early, so you can judge it.
- step 04
Harden
Guardrails, evaluations, logging, permissions and failure handling.
- step 05
Launch
A gradual rollout with people reviewing outputs until the numbers hold.
- step 06
Run & improve
Monitoring, monthly reviews and new skills as your team asks for them.
12 / Engagement models
Start small. Scale what works.
Every engagement is scoped and priced after a strategy call, once we have seen the work.
Pilot
Prove it on one workflow
- One workflow, one success measure
- Working prototype on your data
- Fixed scope and fixed price
- Go / no-go review at the end
Production
Most chosenRoll out and run it
- Hardened agents with guardrails
- Integrations with your core tools
- Evaluations and audit logging
- Monitoring and monthly improvements
Enterprise
Many teams, one platform
- Shared orchestration platform
- Deploy in your own cloud
- Security review support
- Dedicated engineering team
13 / Security & trust
Built to be audited, not just admired.
These are the practices we commit to on every engagement.
NDA first
We sign an NDA before you share anything sensitive.
Your cloud, your keys
We can deploy inside your own cloud account so data stays with you.
Least-privilege access
Agents get scoped credentials for exactly the tools they need, nothing more.
Every action logged
Each tool call and decision is recorded so you can review what happened and why.
Humans approve
Anything touching money, customers or records waits for a person by default.
You own the IP
Code, prompts and evaluation data belong to you when the work is done.
14 / Industries
Wherever there is repeatable work.
- Retail & e-commerce
- Logistics
- Financial services
- Healthcare
- B2B SaaS
- Manufacturing
- Professional services
- Property
where we work
- United Kingdom
- Germany
- Netherlands
- Ireland
- France
- United Arab Emirates
- Saudi Arabia
- Qatar
- Kuwait
- Oman
- Australia
- New Zealand
- United States
- Spain
- Italy
- Sweden
- Switzerland
- Belgium
- Denmark
- Norway
- Finland
- Austria
- Poland
- Portugal
- Bahrain
- Jordan
- Egypt
- Canada
- Singapore
- All locations
works with the tools you already use
- Slack
- Microsoft Teams
- Gmail
- Outlook
- HubSpot
- Salesforce
- Zendesk
- Intercom
- Xero
- QuickBooks
- Shopify
- Stripe
- PostgreSQL
- Snowflake
- Google Drive
- Notion
- Jira
15 / FAQ
Straight answers.
Anything else, press ⌘K or book a call.
What is an AI agent, in plain terms?
Software that can read a request, decide the steps, use your tools (CRM, helpdesk, spreadsheets) and come back with a result. Ours prepare the work and ask a person before anything sensitive happens.
Will it work with the systems we already have?
Usually, yes. Agents connect through the APIs and exports your tools already offer. Where a system has no API, we look at safe alternatives during discovery.
How do you stop it making things up?
Answers are grounded in your data and cite their sources, outputs are checked by automated evaluations, and anything uncertain is routed to a person instead of guessed.
Where does our data go?
Wherever you decide. We can run inside your own cloud, use model providers that do not train on your data, and keep access scoped and logged.
How long does a pilot take and what does it cost?
A pilot covers one workflow with a fixed scope and price. We agree both after a short scoping call, once we have seen the work and the data.
Do you only build AI?
No. We also build the web platforms, mobile apps, data pipelines and MVPs that AI features live in, so one team can own the whole result.
Next step
Hand the busywork to agents. Keep the decisions.
Thirty minutes, one workflow, an honest view of whether an agent will pay for itself.