Cloud & Intelligence

We connect the services and data behind your product.

Behind an app, a connected device or a team’s workflow, information needs somewhere to go and rules need somewhere to run. We connect those parts through backend services, data flows and useful automation, adding AI where it has a clear role.

Discuss your project See how we approach it

Bring a new product, disconnected tools, or a system that needs to evolve.

What should the system make possible?

A team might need to stop entering the same information twice, an app might need a shared account system, or a device might need to send measurements someone can act on. The starting point is the work the system should support and the people who depend on it.

Understanding the existing tools, information and responsibilities helps define what needs to connect, what can stay as it is, and where new software would be useful.

The people: Who relies on the system, and what do they need from it?
The information: Where does it come from, and what decision or action should it support?
The connections: Which apps, devices and services need to work together?

The connections are part of the product.

Saving a record, sharing a measurement or requesting an AI suggestion crosses several parts of a system. Each part needs a clear responsibility: where information is authoritative, who may change it, and what should happen next.

Not every product needs every layer. Some work belongs on a device or within an existing application; other work benefits from a shared service. Cloud infrastructure is one way to support the product, rather than a reason to move everything into it.

Backend services: Handle the rules and shared functions that apps, devices and teams depend on.
Identity and access: Define who or what can use a service, see information and make changes.
Data and information: Organize, store and process information so it can support a specific question or action.
Integrations and automation: Connect existing tools and services, with clear triggers, responsibilities and failure behavior.
AI capabilities: Add assistance for a defined task, with appropriate context, evaluation and human control.

How we make product decisions

Human input defines the outcome. Data helps test assumptions and understand how the system behaves. Automation and AI need a specific job, with limits that follow the consequences of getting it wrong.

Start with the information someone needs.

A report should answer a question. An alert should help someone decide what to do. Define that use before collecting more data, and consider its quality, freshness and source alongside the numbers.

Make automation understandable.

Define what starts a workflow, what it may change and how someone can see the result. Repeated requests, partial failures and exceptions need explicit handling so unfinished work is visible.

Give AI a bounded role.

Summarizing information, finding relevant material or drafting a response can be useful tasks for AI. Each needs suitable context, examples to evaluate against and a way to handle uncertain or incorrect output. Actions with meaningful consequences need clear permissions and review.

Keep human judgment in the loop.

AI can also assist our research, implementation and evaluation, with people reviewing its output. That is a separate decision from adding AI to your product. A straightforward rule or a better integration may be all the product needs.

Plan for everyday operation.

The work continues beyond a successful request in a demonstration. Usage changes, dependencies become unavailable and people need to understand what happened. These questions shape the design:

What information belongs in the system?: Identify its source, purpose, access requirements and how long it needs to be kept.
What happens when a dependency fails?: Decide how delays, unavailable services and repeated requests affect the product and its users.
What demand and cost should it support?: Consider expected usage, response times, storage and external-service costs before choosing the architecture.
How will someone know what needs attention?: Define useful logs, measurements and alerts, along with who responds to them.
How will changes and recovery work?: Agree on testing, deployment, backup and recovery needs, and who will maintain the system.

Availability targets, data-handling requirements, recovery arrangements and ongoing support depend on the project. Managed services still need configuration, monitoring and someone responsible for them.

Choose the technology around the requirements.

Firebase and Google Cloud are part of the toolkit. The choice of services, databases and AI models should follow the product’s data, integrations, operating needs and budget. Existing infrastructure and the team that will maintain it also matter.

Services and APIs: Backend application logic and interfaces that connect apps, devices and external services.
Data storage and processing: PostgreSQL, Firestore and data pipelines chosen for the information and access patterns involved.
Cloud services: Firebase and Google Cloud services such as Authentication, Cloud Functions and Cloud Run, where they fit the requirements.
Workflow integration: API and event-based connections for moving information and coordinating work between systems.
AI integration: Model APIs and relevant product context for scoped assistance, with checks on outputs, access and cost.

Tell us what needs to work together.

Describe the product or workflow, the people who rely on it and the tools you already use. Include where information gets stuck, what you want to automate and any constraints around access, cost or existing infrastructure.

Discuss your project