See how PrimenAI approaches real AI business problems.

Business problem

Employees spend significant time searching across policies, procedures, shared documents, and internal knowledge sources to answer recurring operational questions. The same questions are asked repeatedly, answers vary between teams, and there is no reliable way to tell whether an answer is current.

Why it matters

Time lost to searching is rarely measured but is continuously paid for. Inconsistent answers create rework, compliance exposure, and avoidable escalations to senior staff who are the only people who know the correct handling.

What the system does

• Governed enterprise knowledge retrieval across approved repositories
• Retrieval-augmented generation so answers are grounded in source material
• Source citations returned with every response
• Role-aware access so users only see content they are permitted to see
• Defined human escalation for low-confidence, sensitive, or policy-bound questions
• Auditability of questions, retrieved sources, and returned answers
• Feedback and evaluation loop for continuous answer-quality improvement

Governance & human oversight

• Access control aligned to existing identity and permission models
• Source-level permissions honoured at retrieval time, not filtered after generation
• Prompt and input logging with retention terms agreed during solution design
• Answer traceability — every response links back to the documents used
• Human escalation paths for defined question categories
• Sensitive-content handling rules and restricted-topic behaviour

Example success measures

Example success measures — not reported client results. These are the metrics an engagement could define and track.

• Average time to find an answer
• First-answer usefulness rate
• Escalation rate to human experts
• Knowledge coverage across question categories
• Evaluated response accuracy against a reviewed answer set
• Adoption and repeat-usage rates
• User satisfaction scores

What an engagement could produce

• Working proof of value on a bounded knowledge domain
• Integration design for identity, repositories, and target channels
• Evaluation framework with a reviewed question and answer set
• Governance controls covering access, logging, and escalation
• Deployment roadmap from pilot scope to production
• Operating model defining ownership, review cadence, and content maintenance

Business problem

Leadership teams are asked to approve AI investment without a shared view of where value exists, whether the organization can absorb the change, or which single use case should go first.

Why it matters

Sequencing errors are the most expensive category of AI mistake. Building before readiness work produces systems that cannot be integrated, governed, or adopted — and the cost surfaces after the build budget is spent.

What the system does

• Readiness assessment across the seven PrimenAI readiness dimensions
• Opportunity identification and scoring on business value and feasibility
• Use-case framing with baseline, target outcome, and named ownership
• Sequenced 90-day path with an explicit production decision point
• A recommendation that may conclude AI is not the right mechanism

Governance & human oversight

• Findings recorded with the evidence and interviews they came from
• Data, integration, and security constraints surfaced before design
• Named owner identified for each prioritized opportunity
• Success criteria agreed in writing before any build is proposed

Readiness heatmap

Illustrative example only — sample values, not client scores.

Opportunity matrix

Illustrative example only — sample opportunity categories, not identified client opportunities.

Opportunities are plotted on two axes: business value on the vertical axis and feasibility or readiness on the horizontal axis. Opportunities that score high on both are candidates for a Proof-of-Value Build. High-value, low-feasibility opportunities become readiness work. Low-value opportunities are deferred regardless of how straightforward they look.

• Knowledge assistant
• Document intake automation
• Support triage
• Reporting automation
• Internal workflow assistant

Example success measures

Example success measures — not reported client results. These are the metrics an engagement could define and track.

• Readiness score movement per dimension between assessments
• Number of opportunities qualified against value and feasibility thresholds
• Time from sprint completion to a documented production decision
• Proportion of readiness actions closed before build begins

What an engagement could produce

• Readiness heatmap across seven dimensions
• Opportunity matrix scored on business value and feasibility
• One prioritized, fully framed use case
• 90-day path with a defined production decision point
• Recommended next engagement, including the option not to build