Document reading
Invoices, delivery notes, contracts and forms read and turned into structured data, including layouts the system has not seen before. This is where most companies are still paying a person to re-type what a machine already produced.
AI Agents · Orchestration · Automation
AI Agents and Business Automation
Workflows that read, decide and write back into the systems you already run — narrow, supervised, and built to hand back anything they are unsure about.
Increase productivity with AI automation and intelligent agents applied to business processes (BPM). We develop bespoke AI solutions to automate administrative, sales and operational tasks, reduce repetitive work and accelerate critical processes. Our AI agents can interpret information, execute tasks, interact with systems and support teams across business workflows. Solutions integrate with your existing applications, data and processes, delivering efficiency gains without replacing your entire technology infrastructure.
What we build
AI Agent and Systems Orchestration
We coordinate AI agents, models, data, APIs and applications within a single operational flow, defining tasks, decisions, human validation, error handling and context transfer between systems. AI orchestration transforms isolated agents and integrations into a coordinated, monitored process that is ready to operate at scale.
System integration
Connections to the ERP, CRM, email and spreadsheets already in use — through APIs where they exist, and through the same interfaces your team uses where they do not. Nothing gets replaced to make the automation possible.
Human review points
We design the review before the automation: which decisions a person confirms, what happens when confidence is low, and how an error surfaces afterwards. An agent that never escalates is not more capable, only less honest.
An AI agent is a workflow, not a chatbot
An AI agent is a workflow that can take several steps on its own before returning a result: read a document, look something up in another system, decide what to do next, write the outcome somewhere. That is the whole of it. The word carries more mystique than the thing deserves.
It differs from a chatbot, which answers a question and stops, and from classic automation, which follows a fixed script and fails when the input is not shaped the way it expected. An agent handles variation — an invoice laid out differently, an email that says the same thing in different words, a field that is simply missing.
The trade-off is real and worth stating plainly: an agent can be wrong in ways a rule cannot. A rule fails loudly. An agent can produce something plausible and incorrect. That is why the ones worth deploying are narrow, supervised, and built to report what they were unsure about rather than to guess quietly.
Automation pays off first in the seams between systems
The slowest part of most business processes is not any single system. It is a person moving information between two systems that never learned to talk to each other — reading a number off one screen and typing it into another, checking whether an order matches a delivery, forwarding an email with a summary attached.
These tasks share the three properties that make automation worthwhile. They repeat often enough to pay back. They depend on information that is already written down somewhere. And a mistake is visible before it becomes expensive, because a person sees the result in the normal course of their work.
This is also why we do not recommend replacing an ERP or a CRM in order to automate around it. Replacing working software is expensive, disruptive, and rarely addresses the actual bottleneck. We build against what is already there. It is the same principle behind our AI consulting for SMEs.
Where data goes is a design decision, not a detail
Some workflows run entirely within the company’s infrastructure; others send defined fields to a model provider. This decision is made with you before development, not discovered later in a configuration file.
Whenever data leaves your systems, we document what is sent, to whom, under which conditions, whether it is retained and for how long. Under GDPR, this documentation is an obligation and evidence that a Data Protection Officer may request.
For sensitive personal data, using European infrastructure or the company’s own network can be safer — and less expensive than addressing the legal consequences of a decision made by default.
AI Governance is an operational responsibility, not only a legal one
AI Governance defines who approves each use, which data and models are permitted, when human oversight is required and how decisions, changes and incidents are documented.
BigLearn connects these rules to the company’s architecture and real operational processes. We assess risk, transparency, explainability, providers, cybersecurity and responsibilities so that controls work in practice, not only on paper.
Learn about our AI Governance, Compliance and AI Audit service.
Which administrative, commercial, operational and Human Resources processes can be automated?
A strong starting point is a repetitive task with available information and an outcome someone can verify before an error becomes expensive.
Administrative processes
Document reading and classification, data entry and reconciliation, invoice and order checks, email triage, report preparation and system updates.
Commercial processes
Lead qualification, proposals and quotes, CRM updates, research, follow-up, customer support and a clear handover to a person at the right point.
Operational processes
Planning and scheduling, purchasing and stock, cross-system checks, incident handling, alerts, quality control and forecasting from available data.
Human Resources and AI
Onboarding, document preparation, internal policy search, HR service desks, interview scheduling and training are useful starting points. Candidate filtering or ranking, performance evaluation, monitoring and decisions about workers require enhanced assessment, data governance, genuine human oversight, and design that supports GDPR and EU AI Act compliance.
We can integrate established platforms or build through our AI boutique and bespoke development approach. Training and handover prepare the team to operate the solution safely and consistently.
Agents like these, running
All three describe the same idea in different settings: a workflow that takes several steps before returning a result, with a person deciding at the end.
AI assistant for life insurance
An insurer replaced its «Call Me» button with an assistant that talks, gathers what the quote needs and returns a result in the same interaction — at 11pm or on a Sunday.
Delivered project. No conversion figures published.
Read the caseEvent configurator for a hotel group
Eight properties across three regions replaced the email quote request with a seven-step journey that returns a proposal to the client and an already-qualified lead to the sales team.
Delivered project. Catalogue figures, September 2026.
Read the caseAI for complaint handling in local government
An agent that reads the complaint, summarises it, extracts the location, classifies the responsible service, geolocates the incident and drafts a reply — with three levels of human supervision.
Architecture description, PoC executed.
Read the caseAI in real estate: an email sales assistant
A property development that answers by itself over email, with AWS Lambda and an LLM using the project’s real knowledge — with nothing for the buyer to install.
Delivered project. No conversion figures published.
Read the caseAI for HORECA: hours, allergens and orders
A traditional Lisbon pastry house answers questions about hours, products and allergens through conversation — and takes orders — at any hour.
Built and ready, not yet in production.
Read the caseAI for hotels: a guest journey agent
From booking to after checkout, over email: welcome, trip preparation, pre-check-in, contextual upselling and structured feedback. The booking starts the conversation.
Architecture description, PoC executed.
Read the caseFrequently asked questions
What is an AI agent?
An AI agent is a workflow that can take several steps on its own before returning a result: read a document, look something up in another system, decide what to do next, and write the outcome somewhere. It differs from a chatbot, which answers and stops, and from classic automation, which follows a fixed script and breaks when the input varies. The useful agents are narrow and supervised.
How is an AI agent different from the automation we already have?
Classic automation follows a fixed path and fails when something is not where it expected. An agent can handle variation: an invoice laid out differently, an email that says the same thing in other words, a field that is missing. The trade-off is that an agent can also be wrong in ways a rule cannot, which is why the ones worth deploying report what they were unsure about instead of guessing silently.
Do we have to replace our ERP or CRM?
No. We build against the systems you already run, through their APIs where they have them and through the same interfaces your team uses where they do not. Replacing working software is expensive and is rarely the reason a process is slow. The slow part is usually a person moving information between two systems that never learned to talk to each other.
What happens when the agent gets something wrong?
It should hand the case back rather than proceed. We design the review point before we design the automation: which decisions a person confirms, what the agent does when its confidence is low, and how an error is detected after the fact. An agent that never escalates is not more capable — it is less honest about what it does not know.
Where does agent automation pay off first?
In the seams between systems. Reading a supplier invoice and entering it, matching an order against a delivery note, drafting a reply that a person approves, checking one system against another for discrepancies. These are tasks that are repetitive, that depend on information already written down, and where a mistake is visible before it becomes expensive.
Does our data leave the company?
That depends on the design, and it is a decision we make with you rather than for you. Some workflows can run entirely on infrastructure you control. Others send specific fields to a model provider, in which case we document exactly what is sent, to whom, and under which terms — including what the provider does with it and whether it is retained. Under the GDPR that documentation is not optional.