AI automation services
Automate the work nobody should be doing by hand.
Our AI automation services take the high-volume, repetitive work your team does by hand — invoice processing, data entry, lead routing, reconciliation — and rebuild it as AI agents and workflows wired to your real systems. Business process automation measured in task-time saved, not features shipped.
Unlocking Tech is an AI engineering company based in Lisbon, Portugal, providing AI automation services to SMEs and scale-ups in Portugal, the UK and the rest of Europe. We take one high-volume workflow — invoice processing, data entry from email and documents, lead routing, bank reconciliation — map it, and rebuild it as an AI agent or workflow wired into your CRM, ERP and inbox, built in n8n or in custom code depending on volume and risk. Each workflow is fixed-price, reaches production after two to four weeks of build, runs with a person approving the ambiguous cases and an audit trail of every action, and is measured in task-time saved against a manual baseline. Your team owns it, or we operate it monthly.
Everything you need.Built for production.
No prototypes. No guesswork. Just production-ready systems that save time, reduce errors and scale.
A documented map of the workflow
Your current process written down step by step — who does what, in which system, how long it takes, and where it breaks. Useful on its own, even if you stop there.
Hours back per week, measured
Task-time saved reported against the manual baseline we captured before building — not a vague efficiency gain. Across shipped agents we've averaged a 78% task-time reduction.
Connected to the systems you already run
Read/write links to your CRM, ERP, inbox or database via API or webhook. The automation joins your stack; there's no migration and no parallel tool to maintain.
A human still in the loop where it counts
Approval queues, confidence thresholds and exception routing to a person. Judgment stays with your team; only the mechanical steps are automated.
An audit trail of every action
What ran, what it did, what it skipped and why — logged. The automation is reviewable, not a black box you have to trust.
A clear owner after go-live
Either we operate and maintain it on a monthly basis, or your team takes it over with a runbook and handover. No orphaned automation.
How we work.From manual to production, fast.
Five steps from one painful workflow to a measured, owned automation in production.
30 minutes to pick one high-volume workflow that hurts. We confirm the volume, who does it today, the systems involved and the access we'd need. An engineer, not a salesperson.
We document the workflow end to end and mark which steps need human judgment and which are mechanical. We capture the manual baseline — minutes per task times volume. This is where we tell you honestly if it shouldn't be automated.
A working automation in staging within days, wired to your actual data with read/write access, with human-in-the-loop checkpoints where the map said judgment matters.
The automation runs alongside the manual process on real cases. We compare outputs, tune the rules and prompts, and only hand off the steps it's provably reliable on. Nothing goes live on trust.
Production with monitoring. We report task-time saved against the baseline from step two. Then we either operate it monthly or your team takes it over with the runbook.
We’re not for everyone.We’re for operators who are ready to fix it.
If any of these sound familiar, we should talk.
COO or Head of Operations
Leading a 50–300 person company
- Hours a day lost to invoice entry, order processing or reconciliation
- The next move is hiring two more people, and you'd rather not
- You have the systems, but not the engineering to connect them
Outcome: Operations run smoother, with less effort and more control.
Founder or CTO
Whose team’s time is going to glue work
- Engineers and ops staff babysitting integrations and copying data between SaaS tools
- Reports assembled by hand, quietly eating senior capacity
- A fragile chain that breaks silently and has no owner
Outcome: Your team builds again. Instead of maintaining duct tape.
Teams outgrowing RPA or no-code tools
Past the easy 60%, stuck on the rest
- The remaining 40% needs judgment rule-based RPA can't handle
- A no-code flow that's become spaghetti nobody trusts
- No proper error handling and no one senior owning the architecture
Outcome: Production-grade automation you can trust at scale.
Senior engineers. No handovers. No fluff.
What do AI automation services include?
AI automation services cover five things, and the price reflects all of them: mapping the process to find where the hours actually go; designing the workflow and its failure modes; building the workflow automation and wiring it into the systems you already run — CRM, email, ERP, spreadsheets; the reliability layer that separates production from a demo — error handling, escalation to a person on ambiguous cases, logging, a cost ceiling per run; and operating it with the result measured in task-time saved. The first two weeks are mapping and design, the rest is build — you see the workflow running against your real data before the end of the first month.
Business process automation with AI: what to automate first
Start with the process that scores highest on six things: volume (dozens a day, not a few a month), repetition (mostly the same format), input quality (native PDFs and digital data beat bad scans), an integration surface (a documented API), a low cost of error (the output lands as a draft a person checks) and a clear owner. In practice the winners are almost always the same: invoice processing into the ERP, data entry from email and documents, lead generation and routing into the CRM, bank reconciliation, support triage and the weekly report someone still builds by hand. Score each candidate from 1 to 5 per line: above 20 it's worth building, below 14 leave it for later. Operational efficiency comes from doing one of these well, not five badly.
AI automation examples: three workflows in production
Seed Real Estate, Portugal: two workflows in n8n — one moves deal management from ClickUp to Slack with Airtable as the system of record, the other reconciles bank statements against InvoiceExpress invoices and uses OpenAI to categorise each transaction; reconciliation now runs on its own in a 6-hour cycle and Excel is gone as the system of record. OmniTrade Brasil: an AI agent reads 1,200 construction-project CSV files a month, scores each lead against configurable rules and pushes the qualified ones into HubSpot, turning days of manual screening into hours. Grupo Filtrarte, Portugal: two AI agents — lead routing and post-service review collection — working across HubSpot, Outlook, WhatsApp and Google Sheets; lead verification takes 15 minutes and no leads are lost to slow manual routing.
AI automation tools: n8n, Make, Zapier, Power Automate or code?
We pick the tool per workflow, after the process map, not before. n8n is what we use for most business workflows: it can be self-hosted, keeps data inside your infrastructure when it has to, and lets us drop into code where a node isn't enough. Zapier and Make are fine for simple SaaS-to-SaaS triggers your team can own. Power Automate makes sense when you already live in Microsoft 365. Robotic process automation (RPA) tools such as UiPath drive a screen the way a person would — useful for a system with no API, fragile when the screen changes. Intelligent automation is RPA plus AI — today mostly generative AI — for the unstructured parts; agentic AI goes a step further and lets an LLM choose the next step within guardrails. When volume, latency or compliance demand it, we write custom code instead.
What does AI automation cost? Pricing and ROI
A first serious AI automation project typically lands between a few thousand and a few tens of thousands of euros, at a fixed price per workflow agreed before the build; our packaged AI agents start at a one-off setup of €1,500 per agent for a single workflow. What moves the number is rarely the model: it's how many systems the workflow touches, whether they have a documented API, how messy the inputs are and how many exceptions need a person. On top sit two running costs — operation, monthly if we run it, and model usage, capped per run. The ROI test is simple: hours saved per week × loaded hourly cost × 52, against the build plus 12 months of running cost. If it doesn't pay back within 12 months at today's volume, we tell you to wait — and you keep the process map.
How to choose an AI automation partner (and when not to hire one)
Ask four questions and listen for specifics. What happens when the workflow fails at 3am? Without exception routing and alerts there is no reliability. How will we know it works? There should be a metric and a manual baseline before anything is built. What does it cost to run in the worst month? No cost ceiling means the budget is a guess. Who owns what gets built? The workflows, the code and the credentials should be yours, with no lock-in. Then ask who actually builds it — an engineer or a salesperson with a template. And don't hire anyone if the volume is a few cases a month, if the process is broken and needs fixing first, or if a single Zapier or Make flow your team can maintain already does the job.
AI chatbot development services
Our AI chatbot development services build conversational assistants that do work, not ones that read from a script. Each chatbot is grounded in your own data — docs, knowledge base, past tickets — so answers are specific to your business, and it takes real actions in your tools: it looks up an order, creates or updates a CRM record, opens a ticket, triggers a workflow and hands off to a person when it should. It runs where your customers or your team already are — website, WhatsApp, email or an internal tool. Every chatbot ships with guardrails: retrieval limited to approved sources, clear limits on what it can do, logging and an escape hatch to a human. This is not a rule-based FAQ bot with a decision tree: it understands the request, pulls the right context and finishes the task.
What technology is used in AI chatbots?
Four layers. A large language model (LLM) — OpenAI's GPT models, Anthropic's Claude or Google's Gemini — chosen per use case on quality, cost, latency, language and where the data may be processed. Retrieval-augmented generation (RAG): your documents are indexed so the model answers from approved sources and can cite them, instead of from memory. Tool calls: typed actions into your CRM, ERP or database, each with its own permissions and validation. And the reliability layer: an evaluation set of real questions run before every change, a log of every conversation, and rules for when to hand off to a person. The model is the easiest part to swap; retrieval, tools and evals are where the engineering goes.
Security, GDPR and the audit trail
Before anything connects, we agree what counts as personal data under GDPR in the workflow, where it may be processed and what must never leave your infrastructure — for EU and UK companies that usually decides between a self-hosted and a managed setup, and which model region to use. The automation runs with its own least-privilege credentials, never a person's login. Every run is logged: what came in, what the AI decided, what was written and who approved it. Write-backs land as drafts or behind an approval queue until the numbers justify more autonomy. That log is what your DPO and your auditors will ask for, and it is far cheaper to build in from day one than to bolt on later.
AI automation vs RPA vs workflow tools
Where AI automation fits against workflow tools (Zapier, Make), robotic process automation (UiPath, Power Automate) and doing it by hand — and where it doesn't.
AI automation What we build: n8n or code + LLMs | Workflow tools Zapier, Make — rule-based flows | RPA UiPath, Power Automate | Manual / scripts | |
|---|---|---|---|---|
| Handles unstructured input (text, docs, email) | ✓ | — | — | — |
| Reads and writes your live systems | Via APIs | Via pre-built connectors | Brittle UI clicks | If coded |
| Adapts when the process changes | Re-prompt or retrain | Edit the flow | Rebuild the bot | Rewrite the script |
| When a source system changes | Raises an exception to a human | Fails the run; alert if configured | Silently breaks | Silently breaks |
| Time to first workflow | 2–4 weeks | Hours to days | Days to weeks | Varies |
| Cost at 10× volume | Model cost per run, capped | Per-task pricing | Per-bot licensing | More people |
| Best for | Judgment and unstructured work | Simple SaaS-to-SaaS triggers | Stable screens with no API | One-off or tiny volume |
Selected work
Frequently asked questions
AI automation services rebuild the repetitive, high-volume work your team does by hand — invoice entry, order processing, reconciliation, data moved between tools — as agents and workflows wired to your real systems. Unlike rule-based RPA, AI handles the steps that need judgment, with a human in the loop where it counts and an audit trail of every action. You're measured on task-time saved against a manual baseline, not features shipped.
A first production workflow is a fixed-price project, typically between a few thousand and a few tens of thousands of euros; our packaged AI agents start at a one-off setup of €1,500 per agent for one workflow. The number moves with the systems involved, the quality of their APIs and the exception rate — not the model. Most single workflows reach production after two to four weeks of build. After go-live it's a fixed monthly fee if we operate it, or nothing extra if your team owns it. No per-task charges.
There isn't one best platform — there's a right one per workflow. n8n covers most business process automation and can be self-hosted; Zapier and Make suit simple SaaS-to-SaaS triggers; Power Automate fits companies already on Microsoft 365; RPA tools such as UiPath are for systems with no API. When volume, latency or compliance push past what a platform handles cleanly, custom code wins. Pick the tool after mapping the process, never before.
Pick one high-volume workflow that hurts and measure it: minutes per case times cases per week. Map it step by step and mark which steps need judgment. Automate the mechanical steps, run the automation in parallel with the manual process on real cases, and only hand over what it gets right. Measure task-time saved against the baseline, then choose the second workflow. A 30-minute process call with an engineer is enough to pick the first one.
We won't automate a broken process — fixing it first is usually the bigger win, and we'll say so before you spend money on a build. We won't take a person out of a decision that needs judgment just to claim it's fully autonomous. And if the volume is too low to ever pay back the build, we'll tell you it's not worth automating.
They overlap and often run together; the difference is the job to be done. Automation takes an existing internal, repetitive workflow and makes software do it, measured in task-time saved. AI agents (under Solutions) are customer- or domain-facing agents packaged for a specific industry. Custom AI development builds a new product or feature you ship to your users. Integration is the capability of connecting AI to your systems — automation uses it to do a specific job end to end. If you're not sure which you need, that's what the first call is for.
That's why we run it in parallel before trusting it and build real error handling, exception routing and monitoring rather than a happy-path script. When a source system changes, the automation raises an exception to a human instead of silently writing garbage. And there's a named owner after go-live, so when maintenance is needed it's clear who fixes it. Senior engineers build it — no juniors, no handoffs.
Yes. Our AI chatbot development services design, build and ship conversational assistants connected to your systems. We ground each one in your data so it answers from your real content, wire it into your tools so it can act — look up an order, update a record, open a ticket — and add guardrails plus a handoff to a person. You get a working assistant in production on your website, WhatsApp or internal tools, not a demo.
It depends on three things: how many systems the chatbot reads from and writes to, how many channels it runs on, and how much of your content needs cleaning before it can answer reliably. A single-workflow assistant on a clean knowledge base sits at the low end — our packaged AI agents start at a one-off setup of €1,500 per agent. A chatbot that acts across a CRM, an ERP and WhatsApp is scoped as a fixed-price project, with a ceiling on running cost per conversation.
A rule-based chatbot follows a fixed decision tree. It only handles the paths someone scripted, and anything off-script breaks or dead-ends. The assistants we build understand the request in plain language, retrieve the right context from your data, and act in your tools to finish the task. They handle cases nobody scripted, escalate to a person when they're unsure, and improve as you refine the data and guardrails behind them.
Start your deployment.
Talk directly to a principal engineer.
No sales team.
No discovery workshops.
No procurement circus.
We scope, build and ship.
- Reply within 24h
- Engineer-led assessment
- Written proposal
- Lisbon · UK time zone
No commitment. Just an engineer.