AI Automation
AI automation for real business processes
We identify processes where AI can save time, extract data, prepare answers or trigger controlled actions, and then connect them to systems already used by the team.
What we focus on
- Less routine
- Automation started from repetitive loads and actual volume.
- Controlled sources
- Answers based on the company's approved information.
- Verified actions
- Rules and approvals for major impact operations.
- Monitoring costs
- Usage and consumption shall be tracked on the flows implemented.
What can you build
Clear options for automation
Choose the page close to your need. If it is not obvious where it needs to be started, we use the initial analysis to choose the best between impact and effort.
AI agent for business
AI agents for business
AI agents responding, classifying, summarising or preparing actions in connected systems.
See serviceChatbot AI for business
Chatbot AI
Chatbot Ai for support, leads and frequently asked questions, connected to the website and company rules.
See serviceprocessing AI documents
Processing documents with AI
We extract, classify and structure information from documents, emails or files.
See serviceAutomation of sales with IA
Sales automation
Fluxes for leads, follow-up, scoring, summaries, emails and commercial reports.
See serviceAPI integrations for applications
Integration with AI platforms
We integrate AI platforms and models into websites, applications, dashboards and internal flows. We choose the right solution for the project requirements.
See serviceAI engineering services
AI engineering
We design and implement AI functionalities that enter into products, applications and operational flows.
See serviceGeneral AI for business
Generative AI
Functionalities for generation, summary, classification or assistance in repetitive processes.
See serviceRAG AI for knowledge base
RAG and knowledge bases
AI systems that respond using documents, procedures, pages or data controlled by the company.
See servicedata analytics for business
Data analytics
Reports, data models and dashboards for decisions based on actual information.
See servicemachine learning for companies
Machine learning and predictions
Models for scores, classifications, predictions or recommendations where sufficient data is available.
See serviceautomation of business processes
Automation of business processes
Automation for repetitive task between CRM, email, documents, billing, reports and applications.
See serviceAI for customer support
AI for customer support
AI assistants for frequently asked questions, documentation, statuses, triage requests and assisted answers.
See serviceAI Automation for Business
AI is worth implementing where it saves time or improves control
AI automation should not be treated as an isolated experiment. We choose processes where there is sufficient volume, rules and data: customer support, documents, leads, reports, knowledge search or actions prepared for human verification.
- Clear use caseWe start with a specific problem: repeated questions, document processing, drafting responses or extracting data.
- Controlled sourcesFor AI chatbots, RAG and AI agents, we define which sources the system may use and which cases require human escalation.
- Limits and human verificationImportant actions must have approvals, logs and rules, especially when affecting customers, transactions or sensitive data.
- Monitoring costsWe choose the model, prompts, caching and rate limits to keep costs predictable.
Choice
CHATBOT, AGENTS, RAG OR AI integrations
Not every problem needs the same kind of AI. Sometimes a rules-based workflow is enough; other cases need RAG, connected tools or document processing.
Chatbot AI
Best suited when: you want quick website responses to common questions, customer support or lead qualification.
Keep in mind: Chatbot must determine, by clear rules, when it can continue the conversation and when it is necessary to transfer the case to a person.
AI agents
Best suited when: the system must read context, use tools and prepare actions.
Keep in mind: agents with no clear limits can produce hard-to-verify results.
RAG AI
Best suited when: the answers must be based on approved documents, procedures or pages.
Keep in mind: weak, old or unindexed sources will produce poor responses.
Integration with AI platforms
Best suited when: you want AI functionalities integrated in applications, dashboards or internal flows.
Keep in mind: without logs and monitoring, costs and errors are difficult to control.
Quality signals
How to reduce risk in an AI project
AI can save time, but it must be part of a controlled system. We establish rules, sources, evaluation and monitoring before introducing it into real workflows.
- Assessment of the use case: volume, data, risk and time saving.
- System instructions, approved sources and escalation rules.
- Logs for questions, answers, costs and actions prepared.
- Tests with real scenarios prior to launch to clients or team.
- Monitoring and adjustment after the first conversations or documents processed.
Results
What has to change after the project
The value of a project is seen in clearer processes, more useful pages, better data and an easier platform to manage.
- Low operational timeWe take repetitive steps in automation, and the team intervenes where decisions or checks are needed.
- Faster responsesWe organise information and response rules so that requests can reach the right person or flow more quickly.
- Automatically extracted dataWe take the useful fields from documents and messages, with validations and the possibility of checking before use.
- Flow controlWe follow the state of executions, exceptions and human interventions so that automation can be verified and corrected.
Process
A predictable route with clear decisions
The process remains simple: we understand the context, we create the solution, we build on modules and we launch with clear checks.
Choosing a use case
Definition of workflow
Control and safety rules
Integration by API
Monitoring of results
Recommended pages
Pages related to this type of service: technologies, fields of activity, guides and complementary services.
Frequently Asked Questions
Before we begin the project
Choice, stages and support for AI Automation.
01 / Choice
What's useful to know
Does that replace people?
The aim is to reduce repetitive work and response times. From the start, we define what can be automated and which decisions remain with people.
Do we need clean data?
Yes, but data quality matters. We start with an audit to establish what can realistically be automated.
02 / Planning
Purpose, budget and stages
What can the project include?
For AI Automation, starting points are: AI Agents; Document Processing; Sales Automation; Integration with AI Platforms. In the offer we specify deliverables, limits and responsibilities; the list adapts to your situation.
How do we set the budget and the timetable?
We separate data preparation and integration from the cost of use. The volume, models, external services and the level of human verification influence the estimation; we start with a limited and measurable flow.
03 / Continuity
Verification and support
How do you verify the result before handover?
We use representative examples, including difficult cases, and compare results with validated answers or rules. We track errors, costs and required interventions: a fluent response is not proof of accuracy.
What happens after handover?
Updates of sources, adjustments of rules and reassessments of results may be required after launch. We set cost monitoring, usage limits and escalation to a person for sensitive situations.