An automation may need to interpret information, apply rules, query systems, wait for events and execute actions. We design each part with the technology that best fits its job.
Some processes are best solved with software and clear rules. Others need vision, natural language, semantic search, reasoning or processes that stay alive for hours or days.
We choose the architecture around control, cost, speed, privacy, maintainability and the constraints of the environment.
Interpret documents, language, context and signals when rigid rules are not enough.
Apply rules, permissions, validations and state before producing a real effect.
Execute the right action in the right system and keep track of the process.
It can help to:
Its output can be used as data, a proposal, a classification or an intermediate step. How it enters the process depends on how important the next action is and what risk comes with it.
It validates rules, checks conditions and permissions, keeps process state and carries out actions in real systems: create a record, change a status, send information, continue a workflow or pause until a condition is met.
The combination is especially useful when one part of the work needs interpretation and another needs precise execution.
It can connect to APIs, databases, email, storage, ERP, CRM, e-commerce, ticketing, internal applications or legacy systems.
Each integration is designed around the interface that really exists, the permissions, the volume and the constraints of that system. The connection technology matters less than making the complete process work reliably.
An automation may need to distinguish who is requesting an operation, what information it is allowed to read, which actions are permitted and when an approval is required.
That control is designed in proportion to the process. Updating a simple internal record and executing a sensitive action on a critical system do not need the same treatment.
Inference and other services can run locally, in private infrastructure or through external providers. The choice depends on privacy, data residency, volume, latency, availability, quality, existing infrastructure and operating cost.
A cloud service may be the right fit for one project; in another, it may make sense to keep certain capabilities inside the client's environment.
A single automation can need different capabilities, and they do not all have to depend on the same provider.
When it adds value, we separate the functional need from the specific implementation so we can choose different models according to quality, cost, privacy or availability and reduce unnecessary dependencies.
Alongside the normal path, we review relevant errors, exceptions, validations and edge cases.
When a part of the automation uses AI, the tests need to measure that specific task: what it needs to understand, at what level of quality, and what happens when the result is not reliable enough.
Model, provider, database, deployment, agents, storage and infrastructure are design decisions. We make them once we know what the automation needs to do and the conditions it will work under.
When several automations start sharing connectivity, knowledge, state, permissions and operation, it can make sense to build a common foundation instead of repeating those capabilities in every solution.
See ARGOSIf the project depends on privacy, your own infrastructure, legacy systems, local models, availability or another important condition, tell us. We will include it in the design from the start.
