- Decision and human authority
- Data provenance and evaluation sets
- Error, bias and fallback requirements
Technology / Applied AI
Put AI inside a controlled workflow.
Applied AI for document understanding, knowledge support, prediction and workflow assistance, with explicit human review, measurable performance and clear exception paths.
Service overview
Create useful automation without hiding uncertainty, source evidence or decision authority.
Applied AI for document understanding, knowledge support, prediction and workflow assistance, with explicit human review, measurable performance and clear exception paths.
- Service model
- Use focused specialist support for a defined gap, or assemble a multidisciplinary team around a complete workstream. The structure follows the interfaces and decisions in the scope.
- Delivery
- Choose a project, dedicated team, hybrid engagement or managed service. Milestones, client responsibilities and acceptance criteria are agreed before execution.
- Control
- Scope, assumptions, evidence, ownership and review rhythm remain visible. Changes and unresolved risks are recorded with an accountable decision owner.
- Machine learning
- LLM applications
- RAG
- Evaluation and uncertainty record
- Approval and monitoring boundaries
- Fallback, rollback and change evidence
- Data engineering
- Digital platforms
- Managed Services
Lifecycle positionFrame
Task, value, risk and human authority.
Core capabilities
Capabilities included.
Each capability explains the work itself, how it connects with the surrounding product or operating system, and the evidence needed to make the result usable.
Document intelligence
Classify, extract, validate and route information from business documents.
Practical coverage
Combine automated preparation with completeness and quality checks.
Knowledge assistance
Retrieve relevant enterprise information and prepare grounded responses.
Practical coverage
Keep the source context available to the user.
Prediction and scoring
Support forecasting, prioritization and anomaly investigation.
Practical coverage
Make features, uncertainty and model limits reviewable.
Workflow copilots
Prepare summaries, recommendations and draft actions inside existing work.
Practical coverage
Assist the accountable person rather than create a parallel tool.
Intelligent routing
Classify and direct cases while escalating low confidence or unusual work.
Practical coverage
Use uncertainty to control automation depth.
AI operations
Monitor quality, drift, feedback, cost, latency and change.
Practical coverage
Treat the model as a governed production component.
Solutions
Solutions for common delivery needs.
The starting point can be a technical problem, a capacity gap or the need for a complete delivery workstream. CX24 first clarifies the current state, desired outcome and decision ownership.
Teams spend time extracting and reformatting routine information.
How CX24 respondsTask, value, risk and human authority. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Knowledge exists but is difficult to find during a live case.
How CX24 respondsData, evaluation and workflow context. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
A model prototype lacks evaluation, workflow integration or monitoring.
How CX24 respondsModel enabled experience and integration. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Automation must preserve human approval for higher risk decisions.
How CX24 respondsQuality, failure modes and controls. The scope, responsible owners and evidence needed for closure are agreed before execution begins.
Delivery approach
A clear path from scope to evidence.
The exact gates change by service, but ownership, review and measurable outputs stay explicit. Each stage establishes the information needed to enter the next one responsibly.
Frame
Task, value, risk and human authority.
Stage outcomeUse case contract is prepared or updated before the work advances.Prepare
Data, evaluation and workflow context.
Stage outcomeEvaluation set is prepared or updated before the work advances.Build
Model enabled experience and integration.
Stage outcomeWorkflow integration is prepared or updated before the work advances.Validate
Quality, failure modes and controls.
Stage outcomeMonitoring model is prepared or updated before the work advances.Operate
Monitor performance and improve with evidence.
Stage outcomeAgreed next stage evidence is prepared or updated before the work advances.What you receive
Typical deliverables.
Deliverables are adapted to the client environment and agreed acceptance criteria. The aim is to leave behind usable engineering, technology or operating capability, not presentation material alone.
Use case contract
Users, decisions, inputs, outputs and limits.
Assumptions, ownership, dependencies and the agreed review status are recorded with the output.Evaluation set
Representative cases and acceptance thresholds.
Working files, source information and revision status are organised so the client team can continue using them.Workflow integration
Model service, interface, review and exceptions.
Results include the relevant checks, open issues, limitations and approval evidence, not only the final conclusion.Monitoring model
Quality, drift, latency, cost and change controls.
Final handover identifies accepted scope, residual risk, next actions and the accountable owner for each action.Technology and methods
Machine learningLLM applicationsRAGDocument AIComputer visionMLOpsHuman reviewEvaluation
Evidence clients can review before acceptance.
Proof is defined through the engagement itself: accepted outputs, a visible decision trail, agreed measures and a usable handover.
Accepted outputs
Deliverables are mapped to agreed criteria, version status, accountable owners and review decisions.
Decision trail
Inputs, assumptions, interfaces, changes, exceptions and approvals remain connected to the work.
Delivery measures
Progress, quality, risk, backlog or service measures are selected for the actual engagement.
Usable continuity
Working files, source information, runbooks and handover actions leave the client able to continue.
Client names, project details and outcome claims are published only with permission. Evidence for a specific engagement is confirmed through the agreed scope, reviews and acceptance records.
Choose a delivery structure that fits the responsibility.
Team shape, governance, commercial structure and acceptance are matched to the outcome CX24 is asked to deliver.
Defined project
Bounded scope, milestones, deliverables and acceptance criteria for a specific outcome.
Dedicated team
Stable specialist capacity integrated with client leadership, standards and delivery rhythms.
Delivery centre
Multidisciplinary capacity with named governance, shared methods and transparent reporting.
Managed service
Recurring responsibility operated against controls, service levels and improvement measures.
Build · Enable · Operate