Interface
Screens, flows, states, feedback, sources and user experience.
AI SOLUTIONS
AI applied to real products, processes and operations.
We develop agents, web and mobile tools, automations and intelligent features connected to the client's systems, data and workflows.
We also allocate specialists and assemble squads for projects that require artificial intelligence, full-stack engineering, integrations, QA and product experience.
Receive an initial range in a few minutes and decide whether to send your demand for human review.
An AI solution is still a software product.
To work inside a real operation, AI needs much more than a model or a good prompt.
It needs interface, business rules, authentication, data, integrations, logs, tests, cost control, security and support.
We combine these layers to turn a use case into a functional, integrated solution that can evolve.
Engineering
Front end, back end, APIs, authentication, permissions and deployment.
Data
Sources, documents, internal databases, structures and access governance.
Integrations
Systems, tools, webhooks, automations and external APIs.
Validation
Traditional QA, AI Evals, Golden Sets and critical cases.
Operation
Logs, costs, failures, monitoring and evolution after launch.
PILLARS
Four ways to put AI into motion.
We can build the solution, expand the client's technical capacity or incorporate AI into the delivery process itself.
AI solution development
We create agents, tools, automations and intelligent features integrated with existing products, systems and operations.
Web, mobile, SaaS, intelligent search, RAG, documents and CMS.
Specialists for AI projects
We allocate professionals prepared to work on projects that combine AI, Python, full-stack development, APIs, data and integrations.
Full Stack, Python, AI Backend, Automation, QA and Tech Lead.
Squads for AI projects
We assemble multidisciplinary teams to build or evolve products, agents, automations and tools with AI.
Tech Lead, development, QA, UX/UI and DevOps according to the demand.
AI-supported delivery
We also incorporate AI into our process to support analysis, prototyping, documentation, development and QA.
Technology accelerates parts of the work. Responsibility and review remain human.
SOLUTIONS
Solutions that connect AI to real needs.
Technology is defined by the problem, the data, the integrations and the level of autonomy that is appropriate for each operation.
Agents and assistants
Solutions that interpret requests, consult information, apply rules, use tools and route situations for human review.
Web and mobile tools with AI
Products, logged areas, apps and internal tools that incorporate intelligent resources into a complete digital experience.
Automations and internal tools
Flows with n8n, Make or custom development to organize information, process inputs and connect systems.
Search and knowledge bases
Semantic search, RAG and assistants capable of consulting documents, content, systems and internal databases with access control.
AI in existing products
New features for SaaS, platforms, portals and systems that are already in operation.
AI for CMS and content
Editorial assistants, classification, tagging, review, localization, search and automations connected to CMSs and content operations.
PROFILES
Professionals who understand AI without losing sight of software.
AI projects still require front end, back end, APIs, authentication, databases, integrations, QA and operations.
We assemble hybrid profiles capable of working on the full solution, not only on model configuration.
Full Stack Developer for AI projects
Python, Node.js, TypeScript, React, Next.js, Vue, model APIs and integrations.
Python / AI Backend Engineer
FastAPI, document processing, RAG, semantic search, agents, tools and APIs.
Front-end / Product Engineer
Conversational interfaces, streaming, states, source visualization and web or mobile experiences.
Automation Engineer
n8n, Make, APIs, webhooks, queues, integrations and process automation.
QA and AI Evals
Functional tests, critical cases, Golden Sets, regression, response validation and tool-use validation.
Tech Lead for AI projects
Architecture, stack decisions, security, costs, integrations, governance and production criteria.
These profiles may be available internally, recruited by us, work individually or compose a squad. Timelines depend on stack, seniority, volume and availability.
STACK
Technology defined by context, not hype.
The stack is defined according to the project. Python is an important capability, but it is not the only way to build an AI solution.
Engineering and product
Python, FastAPI, Node.js, TypeScript, React, Next.js and Vue to build the application around AI.
Automations and workflows
n8n, Make, APIs, webhooks, queues and integrations to connect flows and systems.
AI capabilities
Models, RAG, semantic search, vector databases, agents, tools and AI Evals according to the use case.
PROCESS
From idea to production, without skipping steps.
The path changes according to the level of clarity, maturity and risk of the initiative.
Demands that are already defined can move to development, specialist allocation or squad work without unnecessary stages.
01
Diagnosis and feasibility
We understand the problem, users, data, integrations, risks and criteria that define success.
When the demand is still unclear, we can start with an AI Feasibility Sprint.
02
Proof of Value
We build a functional slice to validate quality, usefulness, cost, latency and fit before a larger investment.
03
Production build
We evolve the solution with interface, architecture, authentication, permissions, integrations, logs, tests and deployment.
04
AI Care
After launch, we monitor use, failures, costs, sources, evaluations, integrations and new features.
GOVERNANCE
Useful AI, not empty promises.
We do not promise total automation, absolute precision or generic productivity gains.
We start from a clear use case, validate quality, cost and risk at a controlled scale, and only then move forward.
Data and access
We map which data can be used, who authorizes access and which information requires additional protection.
Logs and traceability
We record interactions, tools used, failures and relevant interventions whenever the architecture allows it.
QA and evaluations
In addition to traditional QA, we validate responses, sources, tool use, critical cases and behavior in adverse situations.
Human review and fallback
Critical, irreversible or legally, financially or reputationally sensitive actions need controls proportional to risk.
Autonomy where it creates efficiency. Human supervision where there is risk.
ESTIMATE
Estimate your AI project in a few minutes.
Talk to our assistant, describe your demand and receive an initial investment range based on the type of solution, profiles involved and likely complexity.
At the end, you can request a human review and send the full context to our team.
Indicative estimate
The estimate may change after validation of scope, data, integrations, availability, timing and technical requirements.
MODELS
AI in the right model for your project's stage.
We can work on a defined-scope delivery, reinforce the team with a specialist, assemble a squad or support the solution after launch.
One-off Deliveries
For Feasibility Sprints, Proofs of Value, agents, tools, automations or features with defined scope.
Allocated Specialists
To add Full Stack, Python, AI Backend, Automation, QA or Tech Lead capacity to the client's team.
Dedicated Squads
To build or evolve solutions with a multidisciplinary and exclusive team.
Ongoing Support
To monitor costs, quality, integrations, evaluations, sources and evolution after launch.
WHITE LABEL
AI solutions and teams behind your brand.
Agencies, digital production companies, software houses and consultancies can count on us to develop solutions, allocate specialists or assemble squads in a white label model.
We follow the partner's flows, tools, rituals and presentation model without exposing Hit to the final client.
EXPERIENCE
Digital experience to build the layer AI needs.
Our experience in web and mobile development, CMS, integrations, QA, content and digital operations forms the base needed to turn AI into a functional product.
+130clients and partners
+700digital deliveries
15years in the market
15countries with delivered projects
Turn your AI idea into a concrete next step.
Describe your need and find out whether the best path is a Feasibility Sprint, a defined-scope solution, a specialist or a full squad.
FAQ
Frequently asked questions.
Does Hit develop AI agents?
Yes. We can develop agents and assistants connected to knowledge bases, rules, APIs and systems, with the level of autonomy appropriate to the operation's risk.
Do you create web and mobile tools with AI?
Yes. We combine AI with web, mobile, back-end, UX/UI, integrations, QA and support.
Do I need to arrive with the project fully defined?
No. When the demand is still unclear, we can start with an AI Feasibility Sprint to map the use case, data, risks, integrations and next steps.
Do you allocate professionals for AI projects?
Yes. We can allocate individual professionals or assemble full squads according to the project's needs.
Which technologies do you use?
The stack varies according to the need. We can work with Python, FastAPI, Node.js, TypeScript, React, Next.js, Vue, n8n, Make, model APIs, RAG, semantic search and vector databases.
Do you train proprietary models?
Training foundational models from scratch is not our main focus. We usually use existing models and build the application, data, integrations and experience around them.
How do you validate quality?
We combine traditional QA with AI Evals, Golden Sets, critical-case tests, tool validation, human review and follow-up after launch.
Is it possible to work with internal data?
Yes, as long as the use is technically, contractually and legally appropriate. Data, providers, permissions, retention and access levels need to be evaluated before implementation.
Can the solution be white label?
Yes. AI solutions, specialists and squads can operate entirely in a white label model.
How does the assistant estimate work?
The assistant collects information about the demand, identifies a likely composition and presents an initial range. This estimate can be sent for human review before becoming a proposal.