A pressure worth addressing
Teams repeat high-volume knowledge work manually
We look for repeatable work where assistance can improve time or capacity without hiding important judgement from the team.
AI and workflow automation
Put AI inside a real workflow where it can save time without losing control.
Nivarix identifies a useful operational job for AI, then engineers the data, evaluation, human decisions and system actions required to make it dependable in everyday work.
For teams with repeated knowledge work, document-heavy processes or AI experiments that have not reached production.
The change we are working toward
AI with a defined operational job, clear control points and a place inside the production system.
- Workflow assessment
- AI-assisted features
- Human review controls
- Evaluation and monitoring
When this is the right conversation
An AI demonstration is easy. A trustworthy production workflow needs more around it.
Useful AI has a defined job, suitable information, measurable quality, clear limits and a controlled path when confidence is low or the consequence of error is high.
A pressure worth addressing
Important information is trapped in documents or conversations
Documents and conversations can become searchable, structured inputs when access, freshness and source traceability are designed carefully.
A pressure worth addressing
An AI proof of concept exists but is disconnected from production
We connect the model to the application, data and operating controls needed for real users rather than leaving it as an isolated experiment.
A pressure worth addressing
A product needs intelligence without giving up control or auditability
Approval, escalation, evidence and audit trails keep people responsible for decisions that should not be delegated blindly.
What you can ask us to own
Design the workflow, intelligence and controls as one production system.
We combine product design, automation, data engineering and software delivery so the AI feature has a clear purpose and a responsible operating model.
Workflow and opportunity assessment
Evaluate tasks, frequency, information, risk and expected value to choose an AI opportunity worth engineering.
AI-assisted features or automation
Build assistance for classification, extraction, drafting, search, summarisation or decision support inside the product or workflow.
Knowledge and document pipelines
Prepare documents and business knowledge for controlled retrieval, transformation and traceable use by the application.
Human approval and exception paths
Define approval, escalation and exception behaviour so people remain in control where context or consequence requires it.
Evaluation, observability and iterative improvement
Test quality against representative examples, monitor production behaviour and improve prompts, data and workflow over time.
How the work moves
Prove the workflow before expanding the intelligence.
We define the operational job, controls and evaluation first, then introduce AI in measured releases that people can review and the wider system can support.
Select
Choose a workflow where AI has a specific job.
We examine task volume, inputs, judgement, risk and the value of assistance before choosing a model or interface.
Opportunity assessment and success criteria
Design
Define the human and system workflow together.
Inputs, outputs, evidence, permissions, approvals and exception paths become part of the product design.
Controlled workflow and technical blueprint
Prototype
Test quality with representative work.
A focused implementation exposes model behaviour, data gaps, usability and the real limits of automation.
Evaluated prototype and go-forward decision
Integrate
Connect intelligence to production actions.
The feature is engineered into the application, data and operational environment with security and observability.
Production AI workflow and operating controls
Improve
Monitor behaviour and strengthen the system.
We can refine evaluation, prompts, information and surrounding automation as usage and business needs evolve.
Measured, continued improvement
Choose a practical starting point
Start with the workflow and the decision it needs to improve.
We can identify a viable opportunity, validate an existing idea or engineer a defined AI-assisted feature into production.
Opportunity is still broad
AI workflow assessment
For teams that need to find a useful, feasible and responsible AI opportunity before investing in a build.
Assess the workflowUse case is defined
Prototype and evaluation
For a specific task that needs representative testing, interaction design and a clear production decision.
Discuss the use caseReady for production
AI feature engineering
For teams ready to connect an AI-assisted workflow to real users, business data and operational controls.
Compare engagement modelsHow do we know whether a workflow is suitable for AI?
A useful candidate usually has repeated work, accessible information, recognisable quality criteria and a manageable consequence of error. We assess these factors before recommending a build.
Can people review AI output before it affects the business?
Yes. Human approval, confidence thresholds, evidence, escalation and exception paths can be designed into the workflow based on the level of risk and judgement involved.
Can Nivarix use our internal documents and knowledge?
Yes, where access and usage are appropriate. We design ingestion, permissions, freshness, retrieval and source traceability so the application uses business information in a controlled way.
Do you support existing AI prototypes?
Yes. We can evaluate an existing prototype, identify the gaps between demonstration and production, and engineer the surrounding product, data, security and operational controls.
Start the AI conversation
Bring the repeated task, the information around it and the decisions people still need to own.
We will help you decide where AI is useful and engineer the complete workflow required to use it responsibly.
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