We build AI applications that answer from your company's own documents and cite the source, and agents that carry out tasks with human approval. Where it makes sense, the solution connects to your Power Platform apps and your data pipelines.
The model is rarely the bottleneck. What holds things back is scattered data, processes with no owner and nobody measuring the result.
A generic chatbot doesn't know your documents, rules or systems, and confidently answers what it doesn't know.
An answer without a cited source won't pass an audit or convince the person who has to decide.
Knowledge is scattered across PDFs, SharePoint libraries, emails and spreadsheets, and none of it is structured for search.
The proof of concept looks great in the demo, but nobody measured the accuracy rate before taking it into operations.
The cost per query only shows up on the invoice, once usage has already grown.
Nobody has defined where the data lives, which provider receives the text and who can ask what.
We start with the data. The documents and records the AI will consult get the same care as an analytics pipeline: ingestion, cleaning, splitting into passages and indexing for search. For each question, the search picks the passages closest to the topic, the model answers based on them and the answer includes a reference to each passage. This technique is called RAG.
When the task requires action, the agent steps in: it queries systems via API, fills in records and routes steps, with explicit limits and a pause for someone on your team to approve before any action that saves, sends or approves something. Access can be through a Power Apps app, a Power Automate cloud flow or an API.
Before scaling up, you see three numbers measured on your own case: accuracy on real questions, response time and cost per query.
One use case at a time, with the success criterion defined before the first line of code.
Together with you, we pick a business question or task, map where the data lives and define how the result will be measured.
Ingestion, cleaning and indexing of the documents and records the AI will consult, following the access rules agreed with you.
A set of real questions with expected answers measures accuracy, time and cost. If the criterion is not met, we adjust or recommend not moving forward.
The AI goes where the work happens, in a Power Apps app, a Power Automate flow or via API. Queries are logged for audit, with retention and access defined with you.
We track quality and cost, keep the knowledge base up to date and expand to new use cases.
Examples to guide the assessment, not delivered projects.
Whoever builds the app and the pipeline also builds the AI. Data doesn't get lost in handoffs between vendors.
Confirmation pauses and the agent's ability to resume from where it stopped are tested without calling the model, so tests do not spend paid quota or need access keys. Answer quality is measured separately, with the evaluation questions from step 03.
Agents with explicit limits and human approval before any action that saves, sends or approves something.
The code isn't tied to one vendor: switching the answering model means changing the configuration, rerunning the evaluation questions and, if accuracy drops, adjusting the instructions, without rewriting the application. Switching the model that indexes the knowledge base requires reindexing the documents.
Not everything needs to be ready. Step 01 identifies which documents and records the use case requires, and organizing that base is part of the project, using the same data engineering we apply to dashboards.
We define this with you before starting, according to your company's data policy: where the knowledge base is hosted, which model provider receives the text and what that provider may do with it under its own terms of use, who can query what, and whether the documents contain personal data, including sensitive data. What the LGPD, Brazil's data protection law, requires to send them to that provider is validated by your DPO or legal team.
It depends on query volume and the chosen model, which is why we measure the cost per query in the proof of concept, before you decide to scale up. Beyond the model, you also pay for hosting the service and the search index and, depending on the integration, a premium Power Apps or Power Automate license.
Through the proof of concept measured on real questions from your business, before scaling up. Applied AI is our newest practice and does not have a public case study yet, so the decision to scale depends on three numbers measured on your case: accuracy, response time and cost per query.
What it costs: applied AI projects start with the Assessment or go straight to Implementation. See plans and price ranges
Book a free discovery call. In 30 minutes we talk through a candidate use case and where the data it would use lives.