Agent and workflow architecture
A workflow map identifying triggers, state, tools and approval boundaries. A pilot covers one measurable task, with a clear stop condition.
All capabilities
Applied intelligence
AI & agentic systems
We design and build AI products and agentic workflows that connect language models with your data, tools, business rules, and human approvals.
Rubix / ServicesSystem 02
Inside a reviewable workflow
Synthetic example: document intake to a reviewed action. Open a stage to explore the control. This illustrates the approach; it does not process documents or call an AI service.
1Intake
A synthetic request arrives with a document. Check file type, required fields and whether the request is in scope before passing it onward.
2Trusted knowledge
Retrieve only approved sources the user is allowed to access. Keep the source reference with the proposed answer; missing evidence is a review condition.
3Model and tools
Use rules for deterministic work and a model only where interpretation helps. Expose narrowly scoped tools; credentials remain on the application server.
4Evaluation
Check the output against the task contract and representative examples. Measure unsupported answers, missed fields, latency and accepted-task cost.
5Human approval
A reviewer sees the evidence and proposed change. The application checks identity, permissions and the approved payload before executing a write.
6Result and recovery
Record the outcome and allow correction. Retry safely, avoid duplicate actions and keep an explicit fallback to the existing process.
01What we deliver
Built around your product.
We plan the engagement around your goals, team, and technology. These are the parts we most often bring together for this kind of work.
Retrieval and data integration
A source inventory, permission model and retrieval path. Answers carry source evidence; missing or conflicting information routes to review.
Evaluation, guardrails, and observability
A representative evaluation set, failure categories and operating dashboard. Track accepted-task cost, latency and quality before increasing autonomy.
Human-in-the-loop product design
A review interface for proposed actions, exceptions and corrections. Application code controls permissions, retries and writes; a model response is not authorization.
Is this the right starting point?
For operations and product teams with a repeated workflow, accessible data and a person who can judge whether the result is useful. Start with one intake-to-review path before automating a department.
Where it may not fit
Use deterministic rules when they solve the problem. An agent is a poor investment when volumes are low, source data is unavailable, or nobody can review errors. High-impact actions need explicit approval and a recoverable failure path.
From first conversation to handover
- Select a repetitive task and measure the manual baseline with the people doing it.
- Connect a bounded data sample and read-only tools; compare model outputs with expected results.
- Pilot with human approval, operational logs and a fallback to the existing workflow.
- Expand only after agreed quality, latency and cost thresholds are met. Hand over runbooks, tests and support responsibilities.
Relevant published work
Explore the published workflow and our contribution. These cases substantiate the stated product work; they do not imply every capability above was part of each engagement.
02Common questions
What clients usually ask first.
What is an agentic AI system?
An agentic AI system combines language models with tools, data, memory, and clear controls so it can complete multi-step tasks. We design these systems around specific business goals, human approvals, and the realities of running AI in production.
Can Rubix integrate AI into an existing product?
Yes. We can add a focused AI feature to an existing application or build a new AI product. We start with the workflow, available data, and business result so the technology has a clear job to do.
What would a first pilot do?
For example, a synthetic intake workflow can classify a request, retrieve approved information and draft an action for review. We agree on the input, output, systems and acceptance checks before building. This example describes our approach, not a client deployment.
Who pays for model usage?
Our starting preference is a customer-owned OpenAI API account with direct provider billing. Rubix charges for adoption, implementation, connections and agreed support. We estimate usage from representative tasks and set resource limits.
How do you handle confidential work?
We have agentic workflow experience that cannot be described publicly. We can explain our engineering approach without sharing client identities, data or private implementation details.
Can an agent act without approval?
Only within an explicitly agreed permission boundary. Sensitive writes, external communications and irreversible actions should pass an application-controlled approval step. Failed actions need safe retry and recovery behavior.
