Artificial Intelligence solution development
AI solution development is the application of generative AI, machine learning and automation models to a company's real processes, integrated with its systems and data. Wisalo, a software house based in Curitiba, Brazil, founded in 2019, designs, builds and puts into production assistants, automations and predictive models, focused on business results and responsible use of data.
Overview
AI creates value when it is connected to the right process, with reliable data and a clear way to measure whether it is working. That is why we start with the business problem, not the model: we identify where AI saves time, reduces errors or improves the experience, and only then choose the technique.
We have hands-on experience with conversational AI: our portfolio includes Globalbot's AI chatbot platform, with omnichannel support and process automation. We build solutions that combine language models with the company's knowledge bases and internal systems, with controls for security, privacy and response quality.
We treat AI as production software. That means measuring response quality with evaluation sets, logging system behavior without exposing personal data, controlling model usage costs and planning for what happens when the model gets something wrong or becomes unavailable. In sensitive processes, the final decision stays with a person, and the system makes it clear when a response was generated by AI.
Who it is for
Companies with high support volume
Support and sales teams that answer the same questions over and over and want assistants that handle the simple cases and route the complex ones.
Document-heavy operations
Legal, finance and administrative teams that read, classify and extract information from contracts, invoices and forms.
Digital products that want AI features
SaaS platforms looking to add intelligent search, summaries, recommendations or assistants inside the product itself.
Managers who need to anticipate scenarios
Businesses with historical data that want to forecast demand, risk or behavior to make better decisions.
Problems we solve
Slow, repetitive customer service
AI chatbots and assistants answer frequent questions at any time and hand off to people when needed.
Knowledge that is hard to find
With RAG, employees and customers ask questions in natural language and get answers based on the company's own documents.
Manual document tasks
Extraction, classification and verification of information in text and images, automated with natural language processing and computer vision.
Decisions without data support
Predictive analytics models turn the company's history into estimates that support planning and prioritization.
AI pilots that never reach production
We take proofs of concept to production with integration, monitoring, cost control and continuous quality evaluation.
What we deliver
AI chatbots and assistants
Conversational assistants for websites, apps and messaging channels, integrated with the company's systems.
RAG and knowledge bases
Search and answers grounded in the company's documents, with source citations and access control.
Natural language processing
Classification, entity extraction, summarization and text analysis in Portuguese, English and Spanish.
Computer vision
Reading and analysis of images and scanned documents to automate checks and inspections.
Predictive analytics
Machine learning models trained on historical data to estimate demand, risk or propensity.
Process automation
Workflows that combine AI and business rules to perform repetitive tasks across systems, with human oversight when needed.
Technologies and stack
We work with market-leading language models and established machine learning libraries, chosen according to the cost, privacy and quality each case requires:
- OpenAI
- LangChain
- Python
- TensorFlow
- PyTorch
- Hugging Face
- Vector databases
- Node.js
How we work
Opportunity assessment
We map available processes and data and prioritize the use cases with the highest return and lowest risk.
Proof of concept
We validate feasibility with real data in a small scope, defining objective quality criteria.
Development and integration
We build the solution integrated with existing systems, with security, privacy and cost controls.
Evaluation and release
We test responses with evaluation sets, roll out gradually and monitor behavior in production.
Continuous improvement
We adjust prompts, data and models based on real usage and user feedback.
Frequently asked questions
Does my company need a lot of data to use AI?
Not always. Assistants based on language models and RAG work with the documents the company already has. Predictive models, on the other hand, depend on consistent historical data, and we assess that during the diagnosis.
What is RAG and when is it recommended?
RAG (Retrieval-Augmented Generation) is a technique in which the AI model queries a document base before answering. It is recommended when answers need to be based on the company's own up-to-date information, reducing made-up answers.
How do you handle the privacy of data used by AI?
We apply LGPD (Brazil's data protection law) from the design stage: we minimize the personal data sent to models, mask sensitive information, control who can access each knowledge base and review the terms of use of each AI provider.
Will AI replace my team?
Our focus is to take repetitive tasks off the team and leave people with the work that requires judgment. In sensitive processes, we design workflows with human review before any decision.
Do you integrate AI with the systems we already use?
Yes. We integrate assistants and automations with CRMs, ERPs, internal systems and messaging channels through APIs, so AI works with the company's real data and workflows.
Shall we talk about your project?
Tell us the context, goals and constraints of your challenge. A Wisalo specialist will get back to you with the next steps.
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