AI with a measurable purpose and control
AI that supports decisions and automates processes.
We implement intelligent automation and genuine AI solutions for classification, forecasting, pattern recognition, document processing and decision support.
Four questions before deployment
Does AI make real business sense here?
You do not need to understand models or technology. We establish what should improve, what it costs, who makes the decision and what happens if the solution does not deliver the expected result.
Purpose and outcome
We define the problem and success measure: time, cost, quality or risk reduction.
The simplest suitable tool
We compare AI with simpler automation and do not add technology without a clear benefit.
A safe pilot
We test a limited process and only the data needed. A responsible person approves material outcomes.
Decision based on results
We show outcome, cost and limitations. The solution can be scaled, improved or safely stopped.
Control and accountability
Business data and decisions remain under control.
We treat AI as a supporting tool, not an autonomous authority. Before selecting a model, we define data classes, permitted processing locations, retention, access and supplier terms. Customer data is not sent to a public tool without an agreed legal basis and configuration.
- 01Data minimisation and classification before integration
- 02Environment choice: business cloud, isolated service or local model
- 03Control of retention, logs, permissions and processing region
- 04No training on customer data where required by the project and supported by the selected service
- 05Supplier terms verified instead of a blanket security promise covering every model
- 06Quality and data-disclosure resistance tests, with human control over decisions carrying material consequences
- 07Assessment of the organisation’s role, intended purpose and risk level for AI Act purposes
- 08A documented shutdown and exit plan for the integration
How the work proceeds
From business hypothesis to controlled deployment.
A pilot must prove more than a successful demonstration: output must be good enough for real data, users must understand limitations, and cost and risk must remain acceptable.
- 01
Purpose and data
We define the decision or activity, input data, process owner, success measure and cases in which AI must not act.
- 02
Prototype and baseline
We compare the model with current work or simpler automation and establish a test set without using data beyond the agreed scope.
- 03
Controlled pilot
We test quality, cost, latency, uncertain output, resilience to poor input and the human approval route.
- 04
Deployment and oversight
We version configuration, measure quality, record errors, define escalation and retain a safe shutdown or fallback process.
Clear boundaries
Technical deployment is not a legal opinion or permission for automated decisions.
Sigma Vision scope
Technical and process analysis, prototype, integration, safeguard configuration, quality testing, monitoring and documentation of accepted limitations.
Customer responsibility
Purpose, lawful use of data, decisions carrying material effects, user information, process ownership and risk acceptance remain with the organisation.
Separate specialist assessment
Legal opinions, formal conformity assessment, employment law, copyright, a DPIA or high-risk use may require legal counsel, a data protection officer or another competent specialist.
FAQ
Questions before an AI implementation.
The essential question is not whether a model can produce an impressive response, but whether that response can be used safely in a particular process and when it should not be trusted.
Should every process be automated with AI?+
No. Where rules are stable and unambiguous, conventional automation may be less expensive, faster and more predictable. We choose AI only where it provides measurable benefit at an acceptable error and oversight level.
Will the implementation comply with the AI Act?+
The AI Act applies in its main scope from 2 August 2026, while some provisions have different application dates. We establish the organisation’s technical role, intended purpose and preliminary risk level and prepare technical evidence, but do not replace a formal legal opinion or conformity assessment where one is required.
Will business data be used to train a public model?+
We do not assume that this is acceptable. Before integration we review the service, configuration, supplier terms, retention and the possibility of disabling training use. Actual controls depend on the selected product and agreement.
How do you reduce hallucinations and incorrect output?+
We use agreed sources, instruction and tool constraints, test sets, acceptance thresholds, uncertainty handling and human verification. We do not promise to eliminate all model errors.
Can AI decide independently about an employee or customer?+
We do not implement such a mechanism without a separate impact assessment, lawful basis, human role, challenge route and requirements appropriate to that use. By default the model supports, rather than replaces, the accountable person.
How do you establish whether a project is worthwhile?+
Before full deployment, we compare time, cost, quality and error rates with the current process or simpler automation. If the pilot does not confirm value, the recommendation may be not to proceed with AI.
Problem first, model second
Let us establish whether AI creates more value than cost and risk.
Describe the process, data sources, current effort and expected result. We will propose a small validation scope, success criteria and a point at which the project should stop or expand.
Assess the AI use case →