Studio AI — AI First
Most companies add AI to existing products.
HTI AI helps you conceive products where artificial intelligence is the foundation — not a feature added later.
Engineering led by MIT-trained specialists
How most companies work
Product ready. "Let's add AI here."
Generic chatbot glued onto legacy systems.
AI that prints reports. Humans that decide.
Task automation without real learning.
How HTI AI works
Product conceived from the AI vision. Architecture thinks AI from the first wireframe.
Agents trained in your business context — not generic models with prompts.
AI that acts, decides and learns. Humans supervise and scale.
Processes that evolve with each interaction. Not static automation.
O modelo aprende com seus dados. Nós garantimos que os dados merecem ser aprendidos.
Real case
ZekkoCRM's sales team had data. Had dashboards. Had reports. But still asked the same questions every Monday: which leads are hottest right now? Who is the real ICP that closes the most? What are the numbers trying to say?
HTI AI made the data answer those questions. In natural language. In real time. Without a BI analyst in the way.
"Today the sales team talks to the data and the data responds. It's not a dashboard — it's an analyst that never sleeps, never errs, and knows every lead in the funnel."
Services
Six capabilities. One single principle: AI is conceived together with the product, not added later.
01
Application development where AI is the central decision layer from the first commit. Not an add-on — the entire architecture was designed for it.
02
Cognitive systems that interact, decide and execute complex tasks without human intervention. Sales, support, analysis, operations and compliance agents.
03
Training language models in your business context and vocabulary. The model learns your company, your customers, your market.
04
Eliminating operational friction via autonomous and adaptive workflows. Unlike traditional automation — it learns from each execution and improves on its own.
05
From vision to product in production. SaaS, platforms and internal tools where AI is the central competitive advantage.
06
For leaders who need to understand where and how AI can transform the business — before building anything.
Verticais
Expertise técnica aplicada a setores onde dados e decisão custam caro.
Detecção de fraude, automação de sinistros e subscrição inteligente — sobre bases Oracle, PostgreSQL e SQL Server que já existem.
Conhecer os serviços →NovoModelos de crédito, prevenção a fraude transacional e automação de compliance regulatório.
Conhecer os serviços →NovoIA clínica e administrativa sobre dados sensíveis — com governança LGPD, ANS e CFM desde a arquitetura.
Conhecer os serviços →NovoCrédito rural, rastreabilidade EUDR, risco climático e compliance MAPA — sobre bases ERP agrícola que carregam décadas de histórico produtivo.
Conhecer os serviços →NovoRoteirização dinâmica, previsão de demanda e torre de controle preditiva — sobre bases que já sustentam sua operação hoje.
Conhecer os serviços →NovoDue diligence, análise contratual, pesquisa de jurisprudência, prazo processual e monitoramento regulatório — com citação rastreável obrigatória.
Conhecer os serviços →Our AI team is formed by specialists with MIT background — not self-taught with online courses. The difference is in depth: we know what's inside the models, not just how to use them.
How we work
Strategic session
45 minutes with a senior specialist. You present the vision — we map where AI can be the foundation, not a complement. No commitment.
AI-First Architecture
We design the product architecture with AI at the center. Model selection, data strategy, agents and automation points.
Iterative development
Sprint by sprint with functional deliveries. Training models in the business context, agent integration and continuous validation.
Continuous learning
The product in production learns. Each interaction refines the model. We monitor, adjust and evolve.
FAQ
A first use case — fraud, support, underwriting, credit — usually reaches production in 8 to 12 weeks. Value shows up when the model runs on trustworthy data: that's why we start with the data layer, not the model.
We use whatever fits the problem. Claude (Anthropic) and models on AWS Bedrock cover most cases with adequate cost and governance. Fine-tuned or open-source models come in when there's a regulatory requirement, critical latency or sensitive data that must stay in the client's environment.
Tenant-isolated architecture, masking, encryption at rest and in transit, auditable logs of prompts and responses, and optional execution inside the client's VPC. Every automated decision has an explainable trail for regulatory audit.
No. We work on top of the database you already have. We add vector layers, feature store and context pipelines around the current engine — without rewriting the legacy or forcing an unnecessary migration.
It depends on volume, model and usage pattern. We design the architecture to control cost from day 1: semantic caching, model routing by complexity, well-tuned RAG and context limits. TCO is predictable and reportable.
Both models exist. We can deliver the finished product, transfer it to the internal team with training, or continue as an AI-first squad operating together — including senior DBA 24×7 sustaining the data layer.
A 45-minute discovery session. You present what you want to build — our team maps how AI can be the foundation. No commitment. With a senior specialist. Result: clarity on what's possible and how to start.