Our approach

Enterprise teams already have capable applications. Work still stalls between systems, documents, teams, and approvals. Our approach is built around closing those gaps in a way your organization can own, operate, and improve.

Five principles

Process before technology

We start with the workflow, the people in it, and the decisions it depends on. Only then do we ask which capability, whether a rule, an integration, an AI agent, or a change to how a team works, is the right response. This keeps the solution attached to a business outcome rather than to a feature.

Existing platforms as the starting point

Salesforce, Oracle, and SAP already hold your data, your permissions, and your users. We use platform-native capabilities where they fit and extend them only where they do not reach. Every custom component is documented so that your team knows what is standard and what is yours to maintain.

Integration and data readiness as part of delivery

An agent or an automation is only as useful as the data it can reach and the systems it can act in. Integration design, data quality checks, and access design are part of implementation, not a separate project discovered late.

Human accountability and controls

Every workflow we design makes clear who is accountable for what. Permissions, approval boundaries, evaluation criteria, and audit requirements are designed in from the start. Anything that commits the business, such as a payment, an order change, a customer communication, or a remediation in production, is authorized by a person.

Ownership beyond go-live

Solutions degrade without attention. Knowledge goes stale, releases change behavior, and usage patterns shift. We stay engaged through application management and continuous improvement, or we hand over to your team with the documentation and runbooks needed to operate confidently.

Choosing the right kind of automation

Not every process needs an autonomous agent. We use a simple rule to decide what each step should be:

• Deterministic automation handles known rules. If the logic can be written down and tested, an integration or a rules-based workflow is faster, cheaper, and easier to audit.
• AI assists with interpretation and ambiguity. Classifying a free-text request, extracting facts from a document, drafting an explanation, or retrieving the right knowledge are tasks where a model adds value, with its output checked and grounded.
• Humans retain decisions needing judgment or authority. Approvals, commitments, exceptions outside agreed bounds, and anything with a regulatory or contractual consequence stay with a person.

These three are complementary. Most useful solutions combine all of them.

The agent lifecycle

When an AI agent is the right response, we take it through a lifecycle that produces evidence at each stage:

1. Discover. Define the problem, the users, the data and systems involved, the boundaries of what the agent may do, and the measures that will tell us whether it is working.
2. Design. Specify the workflow, the approval points, the knowledge sources, the tools the agent may call, and the fallback behavior when it cannot proceed.
3. Connect. Integrate with the platforms, grant the minimum permissions required, and ground the agent in approved enterprise knowledge.
4. Evaluate. Test against agreed quality criteria with representative cases, including the ones that should be refused or escalated.
5. Deploy. Roll out in a controlled way with monitoring, rollback, and disable behavior in place and understood by the people who own it.
6. Monitor. Watch for failed actions, quality drift, stale knowledge, and unusual cost, and manage changes to prompts, configuration, and knowledge under version control.

Scope and duration are established in discovery. We do not commit to a production agent on a fixed schedule before we understand the environment.

Transitioning application management

Application management covers the applications your organization already runs, not only the solutions we built. The transition follows a consistent sequence:

1. Discover the current estate. Applications, integrations, customizations, open issues, and the people who know them.
2. Agree scope and responsibilities. Which applications, which services, which severities, and who does what between your team, QxOrbit, and the vendor.
3. Transfer knowledge. Documentation, runbooks, access, and shadowing so that nothing depends on a single person’s memory.
4. Baseline the service. Establish how incidents, problems, changes, and enhancements are currently handled and measured.
5. Operate. Run the agreed service with incident triage, root-cause analysis, configuration changes, release impact assessment, regression testing, and integration monitoring.
6. Improve. Use what the service learns to reduce recurring incidents, retire workarounds, and prioritize enhancements.

Service hours, severity definitions, response targets, and vendor-escalation responsibilities are agreed for each engagement rather than published as a standard offer.

Starting a conversation

If this way of working matches how you want to approach a problem, tell us about your priorities or explore the use cases to see how these principles apply to specific workflows.