Ingest
Bring approved documents, records and structured information into a governed knowledge path.
IVEON / AI Engineering / 02
Generative AI Engineering
Engineer generative systems around enterprise knowledge, model choice, evaluation and controls so fluent output becomes dependable production behavior.
GET STARTEDLLM Strategy
The model is a replaceable component. The behavior expected from the system is the engineering target.
Choose the model after defining the behavior the enterprise needs.
Generative AI systems become expensive to change when the first architecture is built around one provider, one prompt pattern or one demonstration. We begin with the task: what information the system may use, how current that information must be, what output is acceptable, what uncertainty looks like, and what happens after the model responds.
That operating contract informs model selection. Context capacity, latency, modality, tool use, deployment requirements, cost and controllability all matter, but they matter in relation to the workflow. Different tasks may justify different models, and the routing layer should allow those choices to evolve without rewriting the application around them.
We isolate prompts, retrieval, model access, tool execution and evaluation behind explicit services. This keeps behavior inspectable and makes it possible to compare model or configuration changes against the same production expectations rather than treating every upgrade as a new experiment.
The objective is not maximum generation. It is controlled generation: grounded where evidence matters, constrained where actions matter and observable enough to improve deliberately.
RAG & Enterprise Knowledge
Ground the answer in the information the enterprise actually owns.
Structure
Chunk, enrich and preserve metadata required for access, provenance and retrieval quality.
Retrieve
Search the relevant context using task-aware retrieval rather than sending the entire knowledge estate to the model.
Rerank
Prioritise evidence according to relevance, authority and the operating context of the request.
Reason
Combine instructions and retrieved evidence inside the model boundary defined for the task.
Verify
Evaluate output, preserve source context and route uncertain or sensitive cases to the appropriate review path.
Model Selection & Routing
One system can use different models without becoming model-dependent.
Route by requirement, not novelty.
Reasoning depth, response time, context size, modality and deployment constraints should determine which model handles a task.
Design alternatives before the primary path fails.
Provider limits, degraded quality or unavailable services should trigger defined fallback behavior rather than undefined application failure.
Keep application logic outside the provider interface.
A routing layer can normalize model access, policies and telemetry so the enterprise can change the underlying model without rewriting the workflow.
Compare changes against the same task definition.
Model changes should be evaluated against production scenarios, expected outputs and failure modes before they become the new default.
Evaluation & Guardrails
A good answer is a testable condition, not a visual impression.
Evaluation should reflect the task the system performs: retrieval quality, factual support, format, tool use, refusal behavior, latency and the conditions that require escalation.
Guardrails are most effective when they are layered. Input controls, retrieval boundaries, model instructions, tool permissions, output checks and human review each address a different failure surface. No single filter substitutes for system design.
Production Architecture
Separate the generative loop from the systems it can affect.
Experience
AssistantKnowledge workflowAgent interfaceAPI consumer
Orchestration
Prompt servicesModel routingTool policySession state
Knowledge
RetrievalVector / searchMetadataSource authorization
Model Layer
Foundation modelsSpecialized modelsEmbeddingsRerankers
Operations
EvaluationTelemetryVersioningIncident handling
Production Generative AI
At production scale, model access becomes infrastructure. Routing, context preparation, evaluation, quotas, security boundaries and telemetry need the same operational discipline as the applications that depend on them.
Related Engineering Proof
Generative capability belongs inside a governed platform.
Explore the platform pattern that connects enterprise data, model services, reusable integration and production controls.
View Case StudyGrounding, routing and evaluation turn model access into an enterprise service.
The architecture keeps knowledge, models, orchestration and controls separable so each layer can evolve without obscuring system behavior.
Start a Project
Build generative AI around the task, not the demo.
Bring us the knowledge environment, workflow and operating constraints. We will define the retrieval, model, evaluation and production architecture required to make generative AI dependable.
GET STARTED