Artificial Intelligence

AI applied to a real task in your business.

Fidalgo identifies where Artificial Intelligence can help and builds the integration needed to use it at work. We start with a defined problem, available data and a way to assess the output.

Discuss an AI application

Who it is for

For teams handling many requests, documents or internal queries that need to reduce repetitive work while retaining control over decisions.

Problems we address

Information spread across documents makes answers and their sources difficult to find.
Triage, data extraction and drafting responses require recurring manual steps.
An AI prototype works in isolation but does not yet connect to company systems and access rules.

Possible deliverables

Assistants and document search

Approved sources, retrieval with RAG and answers with references. The design considers document updates and access by user or team.

Integrated automation

Request triage, data extraction and support for existing workflows. APIs and system integrations with human review and defined limits on automatic actions.

A pilot with evaluation criteria

Tasks and examples to compare quality, total handling time and cost with the current process. The pilot helps decide what needs fixing and whether expansion makes sense.

Estimate API costs for your AI project.

Count tokens locally and compare model costs by volume, caching and capabilities. Use estimates as a starting point, then validate quality and usage with examples from your operation.

Calculate tokens and compare AI models

How to start

Describe a recurring task, who performs it and where the information lives. In the initial conversation we assess source access, volume, integrations and what an acceptable answer looks like. Technology follows that context.

From context to operations

Define the use case
Choose a task and record the current process, constraints and examples for evaluation.
Prepare data and access rules
Map sources, updates and permissions. Decide which answers or actions require review by a person.
Build and evaluate the pilot
Integrate within a limited scope and compare answers, failures, review effort and costs with the initial baseline.
Decide how to proceed
Document results and address failures. If the pilot justifies further work, plan operations, monitoring and expansion.

Questions about working together

Do I need to choose a model or use Amazon Bedrock?

No. The choice depends on data, integrations, environment and cost. Bedrock may be an option for companies on AWS, but it is not a requirement to start.

Will AI answer correctly in every case?

No. We define evaluation examples, references, usage limits and human review. Deterministic rules and transaction data should still be checked against the systems responsible for them.

How are budget and timing determined?

After assessing the task and data access, we define scope, deliverables and acceptance criteria in the proposal. Model usage, storage and other services are part of the cost analysis.

RAG: How to Use Company Documents in an AI Application

Learn how RAG connects AI to internal documents and what to assess in retrieval, permissions, source updates, and answer quality.

What Is Amazon Bedrock, and When Is It Worth Using?

Learn how Amazon Bedrock works, when to use it, and how to assess quality, security, and cost before putting generative AI into production.

Next step

Tell us which task you want to improve, which systems are involved and how your team checks the result today.