Search with context and evidence.
Help teams navigate large internal knowledge estates while respecting source permissions and freshness.
IVEON / AI SOLUTIONS / 04
Generative AI
Enterprise knowledge systems, assistants and generative applications grounded in private information, real workflows and controlled model access.
GET STARTEDEnterprise Generative AI
The model is powerful. The enterprise system around it makes it useful.
Generative AI can interpret language, synthesize information and create new content across a wide range of tasks. Inside an enterprise, however, usefulness depends on much more than fluent output.
The application must know which information it is permitted to retrieve, how current that information is, when an answer needs evidence, which model is appropriate, how sensitive data is handled and where generated content must be reviewed before it enters a business process.
IVEON engineers generative AI as a complete production system: knowledge architecture, retrieval, model access, application logic, evaluation, observability, security and workflow integration. The objective is not a generic chatbot. It is a controlled capability designed around a specific enterprise problem.
RAG / Enterprise Knowledge
Ground generation in the information the business actually owns.
Sources
DocumentsKnowledge BasesStructured DataBusiness Systems
Preparation
ParsingMetadataPermissionsIndexingQuality Controls
Retrieval
Query UnderstandingSearch / Vector RetrievalRerankingContext Assembly
Generation
Model RoutingPrompt / PolicyStructured OutputCitations / Evidence
Control
EvaluationSafetyObservabilityAuditHuman Review
Assistants & Knowledge Systems
Design the interaction around the job, not the chat box.
Some use cases need conversational access to knowledge. Others need structured drafting, comparison, extraction, research, summarisation or a generative step embedded inside an existing application.
We choose the interaction pattern according to the work. The best interface may be a copilot, a search experience, a generated report, an API response or no visible “AI interface” at all.
Model Strategy
Choose models by workload, not by loyalty.
01
Quality where quality matters
Evaluate reasoning, language, structured output and domain performance against the actual task rather than broad benchmark reputation.
02
Latency where the workflow feels it
Different user experiences and machine-to-machine processes tolerate different response times. Architecture should reflect that.
03
Cost as an engineering variable
Route workloads, cache safely and select model classes according to the value and complexity of the request.
04
Portability where dependence matters
Abstract model access when the organisation needs flexibility across providers, deployment environments or future model changes.
Evaluation
Fluency is not an acceptance test.
Test against the work.
Build evaluation sets from realistic questions, documents, edge cases and failure conditions. Measure grounding, completeness, format adherence and use-case-specific quality before deployment.
Observe the system, not just the model.
Monitor retrieval behaviour, model outputs, latency, usage patterns, failures, user feedback and application state so quality can be investigated and improved over time.
Security / Private Data
Private context needs explicit boundaries.
Generative applications often become a new access path into sensitive enterprise information. Retrieval permissions, identity, model data handling, logging, retention and output controls must therefore be designed into the architecture.
We separate what a user can ask from what the system is authorised to retrieve and what the application is permitted to do with the result.
Use Cases
Generative AI belongs where language and knowledge are part of the work.
Support specialised decisions and drafting.
Bring relevant information into the workflow and help experts produce structured work that remains reviewable.
Read, compare and transform complex content.
Extract meaning across contracts, reports, policies, technical material and other document-heavy processes.
Put generation inside an existing product or process.
Use language models as a service behind enterprise applications when a separate assistant would add unnecessary interaction.
Synthesize across controlled sources.
Coordinate retrieval, comparison and structured synthesis while keeping evidence available for verification.
Related Proof
A shared foundation for governed AI applications.
The enterprise AI foundation case study illustrates the reusable model, data, integration and control layers that generative applications can build upon.
View Case StudyGrounded answers are an architectural outcome, not a prompt-writing trick.
Source preparation, permissions, retrieval, model selection, evaluation and user experience all shape the result.
Start a Project
What knowledge or language workflow should become dramatically easier?
We help define the use case, knowledge architecture, model strategy, controls and engineering path required to turn generative AI into a reliable enterprise capability.
GET STARTED