AI Agents for Businesses

Q: How is ROI measured?

The ROI of AI agents is measured using business KPIs: by analyzing indicators such as hours saved per process, error reduction, task automation, decrease in operational costs for manual tasks, and productivity improvement. Each business has its own objectives, which is why we define specific baseline metrics for each project.

Q: How do you ensure compliance with the AI Act?

We classify the system’s risk, document its operation, and ensure traceability and human supervision in critical decisions. We align the agent’s design with the EU AI Act framework and standards such as ISO 42001, so that legal, IT, and business share the same compliance framework.

We help companies turn the potential of generative artificial intelligence into real business solutions. We design and implement intelligent, secure and private agents, aligned with corporate goals and ready to create a measurable impact.

Our experience in artificial intelligence consulting lets us identify opportunities and develop AI agents for businesses that can automate complex tasks, optimise operations and improve decision-making.

By developing custom AI agents, we create tailored solutions that integrate business data, protect the privacy of information and adapt to each organisation’s specific needs, multiplying its operational capacity.

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Why LISDATA in agentic AI?

At LISDATA we are specialists in data governance and we understand that reliable agentic AI starts with data that is prepared, governed and aligned with the business.

We work with the full data cycle. We do not develop a generic agent. We prepare the internal data infrastructure so the solution is significantly more reliable, drastically reducing incorrect answers.

Our agentic artificial intelligence solutions are not simple bots that answer queries. They are intelligent systems able to understand the company’s context, access the right information and run actions safely.

By committing to AI agent development in your company, you will be able to:

Save 40% of operating time by automating operations.
Increase productive capacity by 30% without growing the team.
Cut the search for information to under 2 minutes when making key decisions.

What Makes Us Different as an AI Agents Consultancy?

As a data-expert AI consultancy, at LIS we combine strategy, security, confidentiality, and business data to develop customized and scalable AI agents.

Multi-agent architecture and orchestration

We design multi-agent systems that work together to solve complex end-to-end tasks.

Multi-agent orchestration coordinates several AI agents specialised in specific processes, which makes it possible to build more advanced solutions than a single agent and to collaborate across different areas of the business.

Implementing RAG (Retrieval-Augmented Generation)

We connect AI agents to the company’s real data sources through RAG architectures. That way the agents work with real, up-to-date, contextual corporate information.

With controlled access to the company’s data warehouse, the systems can run semantic searches and return more precise, reliable and verifiable answers, with a much lower error margin than an assistant without governed data.

Integrating agents with ERP, CRM and Power BI

We integrate intelligent agents with the main corporate tools so they can query information, run actions and automate tasks directly inside the existing technology stack.

We work with systems that integrate with ERP, CRM, Power BI dashboards and any API through native connectors and the MCP protocol, to make internal management and data analysis easier.

Tangible results: lower costs and operational efficiency

Developing AI agents helps optimise complete workflows and create a direct impact on the efficiency and profitability of the business.

Intelligent agents let companies cut operating costs by automating repetitive tasks with IPA and RPA systems, shorten the time it takes to look up internal information and standardise decision-making across departments.

That means more productive hours and fewer manual errors. Applied to a company’s finance department, for example, an AI agent can automate invoice review, detect errors in orders, classify expenses and prepare reports for the whole team.

The main results and benefits you will see in your company if you commit to agentic AI are:

Cutting operating costs with AI
Higher team productivity thanks to process automation with AI agents
Scalability without increasing investment
Faster, data-based decisions
Fewer errors and better operational quality
Standardising internal knowledge
Centralising knowledge across departments

Success story: AI agents in education

In this use case, CIFP Número Uno in Santander needed students and teachers to access complex technical documentation in several languages without losing practice hours. It is the same pattern we apply in corporate environments: index governed knowledge and expose it through a conversational agent.

The challenge

A large volume of complex technical information in several languages. Students at the centre spent too much time finding the right documentation for the activities, which reduced the hours dedicated to real practice and affected workshop efficiency.

Our solution

An initial consultancy was carried out with the commercial team and R&D. After that, the decision was to implement an AI-based conversational platform able to index and understand all the available technical documentation, so students could query it by asking questions in natural language.

The AI agents also made automatic translation into Spanish and the generation of summaries and outlines easier.

Technical execution

The technology used was aisQ AGENT adapted to education, a platform of LISDATA’s own that integrates natural-language queries, automatic translation and the generation of summaries for students and teachers.

Results and KPIs

Less time spent by students looking up information
Higher student motivation
Fewer repetitive tasks for teachers
Faster, more personalised training activities for students
24/7 availability to resolve questions
Immediate feedback

Success story: AI agents for property managers

At LISDATA, together with Grupo LAE and INNOVA, we developed a solution based on AI agents to make the daily work of property managers simpler and more efficient.

The challenge

The main challenge was the huge amount of information property managers had to consult every day. Above all, reaching the right documentation, interpreting regulations and consulting critical information about risks.

Our solution

An artificial intelligence project was developed in three blocks, each focused on answering a different need without affecting daily operations: a document chat, a legal chat and a risk chat.

Technical execution

With aisQ AGENT, a document chat was developed that connects in real time with internal repositories and uses a RAG strategy with time filtering.

The legal chat interprets questions about regulations and returns contextual, traceable and understandable answers.

The risk chat integrates through MCP with different databases to consult critical information about the communities, from fire-extinguisher checks to facility maintenance.

Results and KPIs

Minutes, budgets, contracts and agreements queried in seconds, without manual searches.
Information always up to date thanks to automatic synchronisation between document sources and databases.
More precise answers with the time context of each document.
A solution already adopted by hundreds of property managers and still growing.

Phase 1: Assessment and data governance

Before developing AI agents, we audit data quality in the organisation to identify information sources, how accessible they are and possible risks.

We also define the foundations of data governance so the system can work with reliable information, setting access policies, traceability, roles and controls that ensure all available information is used properly.
Phase 2: Architecture and RAG

We design secure environments in Azure AI Foundry using RAG technology, connecting the AI to the company’s data sources and making sure the data the agent consumes is true, secure and lawful.

When the privacy level requires it, we develop local AI agents hosted on the client’s own infrastructure, so sensitive data or intellectual property never leaves the corporate environment.
Phase 3: Training and personalisation

We adapt the agent’s behaviour to each company’s specific goals and processes. We adjust prompts, decision flows and business logic so the custom AI agents return more accurate answers and actions aligned with the business context.
Phase 4: Deployment and monitoring

We put the agents into production under the European AI Act, following standards such as ISO 42001 for artificial intelligence management systems and good practice in privacy and data protection.

We monitor and supervise their performance, security and evolution over time so the AI stays safe, ethical, transparent and ready to scale inside the organisation.

Tools and technologies we master

We work with a broad technology stack, using the best tools on the market, including AI models, orchestration frameworks and cloud infrastructure services that let us create robust AI agents adapted to each case.

Microsoft AI Foundry and Google ADK

As environments to design, develop and manage enterprise artificial intelligence solutions.

LangChain

To build advanced agent architectures and connect AI models with tools and information sources.

AutoGen

To develop multi-agent systems able to coordinate different specialised agents.

Azure OpenAI

To connect large language models inside the company.

Claude

We work with Claude as one of the advanced artificial intelligence models and we are currently in the process of becoming certified partners.

Vector databases

We implement vector databases as part of retrieval-augmented generation architectures, so agents can retrieve real information from large volumes of data.

What is the difference between agentic AI and generative AI?

Generative AI, as the name says, focuses on generating and creating content from instructions. The user makes a request with information such as text, images or code, and the AI answers on that basis.

Agentic AI goes one step further. AI agents, as well as generating answers on request, can also act, make decisions, query data and run processes inside the company by connecting to internal systems and tools.

What is the difference between agentic AI and generative AI?
Característica	Generative AI	Agentic AI
Goal	create content or answer questions	run actions and automate business processes
Behaviour	reactive, it answers prompts	proactive, it acts and makes decisions
Integration with systems	limited	it can connect to ERP, CRM, databases and APIs
Level of autonomy	low	high
Confidentiality	low if public models are used	high if custom agents are used
Data access	no access to corporate data	access to governed internal data in real time
Example of use	draft a report or generate an email	find stock information and place an order
Business impact	improves individual productivity	optimises complete processes and cuts operating costs

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How do you ensure my data remains private?

We design AI agents in secure and controlled environments, utilizing private architectures and data management systems that protect corporate information. Corporate information is not used to train public third-party models. We implement RAG solutions, access control, and governance policies in compliance with current regulations to ensure complete privacy and security.

How is ROI measured?

The ROI of AI agents is measured using business KPIs: by analyzing indicators such as hours saved per process, error reduction, task automation, decrease in operational costs for manual tasks, and productivity improvement. Each business has its own objectives, which is why we define specific baseline metrics for each project.

What is the difference between a chatbot and an LIS agent?

A chatbot responds in a conversational channel, which implies limited interaction, whereas AI agents developed by LIS on governed data are custom-made, connect to internal tools, and can execute actions (queries, updates, workflows) with human audit and supervision.

How do you ensure compliance with the AI Act?

We classify the system’s risk, document its operation, and ensure traceability and human supervision in critical decisions. We align the agent’s design with the EU AI Act framework and standards such as ISO 42001, so that legal, IT, and business share the same compliance framework.