Evaluation
Define representative test sets, expected behaviors and measurable quality criteria for AI outputs.
AI GOVERNANCE & EVALUATION
Make AI measurable, controllable and operationally trustworthy.
IFDS engineers governance directly into AI systems — combining evaluation, permissions, guardrails, observability and human oversight so organizations can move from experimentation to controlled production.
THE PRODUCTION GAP
A working AI demo is not yet an operational system.
AI systems behave differently from conventional deterministic software. Their outputs depend on models, prompts, context, retrieved knowledge, tools and constantly changing inputs.
That creates a fundamental production question: how do you know the system continues to behave well enough to trust?
Governance becomes useful when it is translated into technical mechanisms that can be measured, tested, monitored and enforced.
OUR APPROACH
Governance should live inside the architecture.
Policies alone cannot control a production AI system. The architecture needs mechanisms that determine what the system may access, what it may do, how its quality is measured and when a human must intervene.
01
Define acceptable behavior
02
Measure it continuously
03
Restrict permissions and actions
04
Preserve traceability
05
Escalate uncertainty
CONTROL LAYERS
The technical controls behind trustworthy enterprise AI.
Different AI systems require different levels of autonomy. The control model should match the business risk.
Guardrails
Enforce business rules, validation, output constraints and execution boundaries around probabilistic models.
Identity & permissions
Control which data, tools and actions users and AI agents are authorized to access.
Observability
Capture model interactions, retrieval, tool calls, latency, costs, failures and workflow decisions.
Human oversight
Introduce explicit review and approval points when uncertainty or business impact requires human judgment.
Auditability
Preserve enough context to understand what happened, which sources were used and which actions were taken.
AI EVALUATION
If quality cannot be measured, it cannot be governed.
Evaluation turns subjective impressions into explicit engineering criteria.
01
Task quality
Does the system actually accomplish the business task?
02
Grounding
Are answers supported by appropriate evidence and sources?
03
Tool behavior
Are the right tools being called with valid arguments and permitted actions?
04
Safety boundaries
Does the system respect defined constraints and escalation rules?
05
Regression
Did a model, prompt or workflow change make previously good behavior worse?
RISK-BASED CONTROL
Not every AI action needs the same level of control.
LEVEL 01
Assist
AI prepares information or recommendations. A human makes the final decision.
Research · Drafting · Decision preparation
LEVEL 02
Act with boundaries
AI performs defined actions within explicit permissions and business rules.
Workflow execution · Routing · Data updates
LEVEL 03
Escalate high-impact decisions
Sensitive, uncertain or consequential situations require explicit human approval.
Exceptions · High-value actions · Sensitive cases
OBSERVABILITY
Understand what the AI system is actually doing.
Production operations require visibility across the full AI workflow — not only the final model response.
06
Business outcome
05
Agent & workflow decisions
04
Tool calls & actions
03
Retrieved knowledge
02
Model input & output
01
Identity, context & permissions
WHERE GOVERNANCE MATTERS
Especially where AI moves from answering to acting.
Autonomous workflow execution
Control what agents can access, which actions they may perform and when human approval is required.
Permission-aware knowledge AI
Evaluate retrieval quality while ensuring users cannot retrieve information outside their authorization.
Production AI applications
Monitor model behavior, quality, latency, cost and regressions as products evolve.
Human-AI decision processes
Define clear boundaries between automated recommendation, execution and accountable human decisions.
DELIVERY
Turn governance requirements into engineering controls.
01
Define
Identify acceptable behavior, risks and business consequences.
02
Measure
Build evaluations and test sets around the actual use case.
03
Control
Implement permissions, guardrails, review and escalation mechanisms.
04
Operate
Monitor real behavior and continuously evaluate changes and regressions.
MOVE AI INTO PRODUCTION
Can you prove your AI system is behaving as intended?
We help organizations design the evaluation and control layer required to operate AI confidently in real business processes.
FROM IDEA TO PRODUCTION
Build the AI capability that changes a business metric.
IFDS combines business analysis, AI architecture and production engineering in one delivery team.
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