Knowledge assistants
Search manuals, procedures, and internal materials, with answers grounded in sources and access based on each user’s role.
Example: find the rule for a request without searching through multiple documents.Applied artificial intelligence
From information
to the next action.
Assistants that consult documents. Agents that support tasks. Solutions that connect knowledge, data, and processes to your team’s work.
Available services
AI where work
needs to move forward.
A project can start with a specific task and grow as results are validated with the team.
Process agents
Workflows that retrieve information, prepare a task, and perform authorized actions in systems, with logging and approval at agreed steps.
Example: review a request and prepare to route it to the right person.Document reading and data extraction
Turn proposals, orders, and reports into organized fields, resolving inconsistencies and reviewing the results before use.
Example: extract the supplier, amount, deadline, and terms from a proposal.Request screening and service support
Classify requests, identify missing information, and prepare responses for the team to review.
Example: organize the queue by topic and route each case to the person responsible.Data and management copilots
Query metrics and summarize reports using defined sources and business rules, with access to the details behind the numbers.
Example: compare periods and find the data that explains a change.AI integrated with your tools
Experiences across websites, internal apps, and workflows, connected to available sources through APIs or integrations assessed as part of the project.
Example: bringing the assistant into the application your team already uses.Independent project / Work hub
See information
become a deliverable.
Explore three business scenarios. Compare the input, check the suggestion, and try the review step.
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Let's talk about this projectData and analyses prepared for demonstration. Changes stay on this page; no AI or external system is activated.
From document to record.
Compare the proposal with the extracted fields. Correct a value, check the information, and generate the sample record.
Input / Received proposal
Proposal MP-041
Supplier: Oficina Técnica Norte. Service: preventive maintenance of 12 pieces of equipment. Total amount: BRL 4,800.00. Completion timeline: 10 business days after approval. Payment: 30 days after completion.
Demo documentProposed output / Fields for review
One request. One person to take it forward.
See how context can help organize the queue. Review the priority and simulate the suggested routing.
Input / Received message
Sales teamI need to view my branch's sales dashboard, but I get an access denied message. My manager has already approved the request.Demo request
Proposed output / Triage
- Topic
- Report access
- Suggested assignee
- Data team
- Next step
- Check the access profile and the manager's approval.
Access must be validated by the person in charge; it is not granted automatically.
An answer you can verify.
Explore sourced answers, incomplete documentation, and guidance that needs review.
Choose a question
Answer with a source
What should I review before submitting a request?
Check the department, material, quantity, and justification. Also verify that the attachment required for this type of request is included before forwarding it for review.
At your company, the workflow uses your context.
Sources, fields, rules, assignees, and integrations are defined as part of the project. These examples show the experience that can be built and validated.
A start with a clear scope
One routine first.
A solution that evolves.
The pilot brings together one task, a set of information, and a group of users. We test authorized real-world cases and adjust the solution before expanding its use.
- Deliverables: working workflow, evaluation criteria, and usage guidance.
- Project definitions: sources, access, integrations, and review points.
- Ongoing support: follow-up, maintenance, and operating costs agreed on in the proposal.
From pilot to everyday use
An AI project
is also an
operations project.
- 01
Choose the problem
Map the routine, who is involved, and what defines a good result. Decide what AI should do and where review is needed.
- 02
Prepare data and access
Organize sources, examples, permissions, and available integrations. Select tools that fit the scope.
- 03
Build and evaluate
Test the workflow with authorized examples, compare results against defined criteria, and address failures before expanding use.
- 04
Deploy and monitor
Provide guidance, operating records, and a monitoring routine. Maintenance and further development are agreed on in the proposal.
Before getting started
Let's get into the details.
Do you build agents that perform tasks?
Yes. The project defines which tools the agent can use, which actions are allowed, and when a person must approve them. Integrations, execution logs, and error handling are part of the design.
Can I use my company's documents and data?
Yes. We assess source quality, the access needed, and where information will be processed. The solution must respect the permissions and restrictions defined for the project.
Can AI make mistakes? How do you handle that?
It can. We test representative examples, check outputs, and define what to do when information is missing or conflicting. Answers, extractions, and actions each have their own validation criteria.
I already use spreadsheets, Power BI, or another system. Can they be integrated?
I assess the available access methods, such as files, APIs, or authorized queries. The feasible integration and each tool's responsibilities are made explicit in the scope.
How do we get started, and which costs are included in the proposal?
We can start with a pilot for one routine. The proposal lists development, infrastructure, AI service usage, and any ongoing support separately, based on the tools and expected volume.
Let's talk
Which task deserves
an AI solution?
Tell me how your operations work and what you would like to improve.