AI Automation Services
AI automation built by engineers, not demoed by consultants. Workflow automation, document processing, AI agents with bounded authority, and self-hosted options integrated with the ERP, CRM and line-of-business systems you already run.
Most AI Automation Never Leaves the Demo
AI automation fails for the same reason almost every time: it is built as a demo and never connected to the systems where work actually happens. A workflow that reads an email and drafts a reply is impressive once. A workflow that reads an order, validates it against live inventory, creates the record in your ERP, and escalates only the exceptions is a business change.
MetaDesk Global builds the second kind. We are an engineering company — our background is embedded systems, industrial IoT and production AI — and we apply the same discipline to automation: defined scope, real integration, monitoring, audit trails, and a way to roll back when something goes wrong.
Five Kinds of Automation
Each of these is a project we deliver end to end, from the process audit through to the automation running in production on your own data.
AI workflow automation
Processes that involve judgement, unstructured input or messy documents — the work traditional rules-based automation could never reach. Reading and extracting data from PDFs, emails, invoices and forms; classifying and routing incoming requests; drafting responses for human approval; reconciling data between systems that were never designed to talk.
AI agents with bounded authority
Agents that complete multi-step tasks using your tools — look up a record, check a rule, take an action, escalate when uncertain. Every agent we build has explicit limits on what it may do, logged reasoning, and a human approval step wherever the consequence is real.
Operations and production automation
This is where our engineering background matters. Automation that touches machines, sensors, maintenance systems and production data, not just office software — work orders raised from equipment condition, quality exceptions routed automatically, operational data reconciled across OT and IT.
Self-hosted and data-sovereign automation
If your data cannot pass through third-party SaaS — customer records, patient data, commercially sensitive production figures — we build on self-hostable platforms running in your own infrastructure, so the data never leaves it.
Integration with the systems you already run
ERP, CRM, CMMS, accounting, ticketing, databases, internal APIs, and legacy software with no API at all. Automation only creates value where it reaches the system of record, and that integration work is usually the real project.
A Process Built Around Evidence, Not Enthusiasm
Industry research in 2026 has been unusually blunt. Gartner has predicted that more than 40 percent of agentic AI projects will be cancelled by 2027, and MIT’s Project NANDA study found that 95 percent of enterprise generative AI pilots produced no measurable revenue or cost impact within six months. The most cited blocker is not the technology — it is that nobody defined what “working” meant. Our breakdown of why AI automation projects fail covers the rest. So we start there.
Process audit and automation scoring
We map the candidate processes and score them on volume, time spent, error rate, how stable the rules are, and how clean the input is. The output is a ranked shortlist with an estimated hours-saved figure for each — including the ones we recommend not automating.
A definition of done, agreed in writing
What the automation must achieve, measured how, and what accuracy is acceptable. Without this an automation can never be declared finished, which is how pilots drift for a year.
First workflow in production, not in a sandbox
We put one real process live early, with a human approval step, so the organisation sees actual output on actual data. Scope grows from something that works rather than from a slide.
Monitoring, audit trail and rollback
Every run is logged: what came in, what the system decided, what it did, and what a human changed. You can prove what happened, measure accuracy, and switch it off without drama.
Handover or ongoing support — your choice
You own the code, the workflows and the accounts. We can maintain them, or document and hand them to your team. We will not build something only we can operate.
Processes Worth Automating First
| Process | Why it suits AI automation |
|---|---|
| Order and invoice intake from PDFs and email | High volume, unstructured input, clear validation rules |
| Quote and RFQ preparation | Repetitive assembly of data that already exists in your systems |
| Support ticket triage and routing | Language understanding with an unambiguous outcome |
| Data reconciliation between systems | Tedious, error-prone, and entirely rule-checkable |
| Report and compliance pack generation | Same structure every period, data pulled from several sources |
| Maintenance work orders from equipment data | Condition signals already exist; the gap is turning them into scheduled work |
| Supplier and inventory exception handling | Most cases are routine; only exceptions need a person |
The best first automation is boring, frequent, and currently done by someone who would rather be doing something else.
What AI Automation Typically Costs
We scope and quote per project, but it helps to know the shape of the market before you talk to anyone. Across the industry in 2026, typical build costs look like this.
| Scope | Typical build cost | What that covers |
|---|---|---|
| A focused first workflow | $1,500 – $4,000 | Two or three steps, light integration |
| Multi-step workflow, several systems | $7,000 – $12,000 | Real integration, branching logic, error handling |
| Custom AI agent with governance | $15,000 – $100,000 | Discovery, development, integration, deployment |
| Ongoing running costs | Monthly | Model usage, hosting, monitoring, maintenance |
Market ranges for 2026, not MetaDesk pricing. One warning worth repeating: surveys consistently find organisations overrun their AI budgets, often badly, because the integration and data work is underestimated. We would rather scope that properly up front than discover it in month four. Our breakdown of what drives AI automation cost explains where the money actually goes.
An Engineering Company, Not an AI Reseller
Plenty of agencies can demo a chatbot. Far fewer have shipped systems that run unattended for years, integrate with equipment and software nobody documented, and keep behaving when the input is unusual.
That is the work we come from, and it is what separates an automation that survives contact with your operation from one that is quietly switched off after a month.
- We have shipped systems that run unattendedOur background is embedded devices and industrial IoT — products that must work for years without anyone watching. That discipline is exactly what automation needs and what most AI projects lack.
- We integrate with the awkward systemsLegacy software, industrial protocols, databases with no documentation. The systems where your real data lives are rarely the ones with a clean API.
- We design for failure, because it happensConfidence thresholds, escalation paths, retries, audit logs and a defined safe state. Automation without those is a liability waiting for an unusual input.
- We will tell you not to automate somethingIf a process is low volume, genuinely judgement-heavy, or about to change, the honest answer is that automating it will cost more than it returns. You get that answer before you spend, not after.
- You own everythingCode, workflows, credentials and infrastructure. No lock-in, no platform you cannot leave, and no dependency on us to keep it running.