top of page

SimLab™ 
Scenario Simulation & Output Grading

What SimLab™ Scenario Simulation & Output Grading Is

SimLab™ is a controlled “wind tunnel” for AI applications. Clients run realistic scenarios through an AI workflow, score the outputs against a rubric, and use those grades to optimize prompts, context, guardrails, and escalation rules before deployment (and continuously after).

What SimLab™ ​ Helps Leaders Do

It turns “we think it’s working” into measured reliability:

  • See how the system behaves under pressure (edge cases, ambiguity, conflicting constraints).

  • Quantify quality (accuracy, policy compliance, tone, completeness, latency, cost).

  • Identify failure modes (hallucination, overconfidence, missing citations, unsafe guidance).

  • Improve the application systematically (not by vibes).

  • ​

SimLab™ in plain English

  1. Pick scenarios (customer complaint, contract clause, HR policy question, incident report, board deck request).

  2. Run the simulation through the AI workflow (agent + tools + context).

  3. Grade the output using a scorecard (human, SME panel, or calibrated evaluator).

  4. Compare versions (Prompt A vs Prompt B; Context Gate on/off; different retrieval sources).

  5. Optimize based on what fails, not what impresses.

Core Components Of SimLab™ 

1) Scenario Library

  • Golden Path: common, expected requests.

  • Edge Cases: incomplete info, contradictory inputs, tricky user behavior.

  • Adversarial: prompt injection, policy evasion, data-exfil attempts (if relevant).

  • Operational Stress: peak volume, latency targets, cost constraints.

3) Variation Engine (A/B testing for AI behavior)

SimLab™ can compare:

  • Different prompts/system instructions

  • Different context packs (what’s included/excluded)

  • Different retrieval settings (top-k, recency, sources)

  • Different guardrails (refusal thresholds, escalation triggers)

  • Different agent routing (specialist vs generalist)

2) Grading & Rubrics (the heart of it)

A standardized scoring model that fits the use case. Typical dimensions:

  • Correctness (facts, reasoning, math)

  • Completeness (did it do all required steps)

  • Safety / Policy (did it violate guardrails)

  • Tone / Brand (aligned voice, clarity)

  • Evidence (citations, references, traceability)

  • Actionability (clear next steps, decisions supported)

  • Efficiency (token cost, time, tool calls)

 

Outputs become scores + failure tags (e.g., “Missing constraint”, “Overconfident”, “No escalation”)

4) Optimization Loop

Each cycle produces:

  • A prioritized list of fixes (“Change context order”, “Add policy excerpt”, “Tighten refusal”, “Add ask-back question”)

  • Updated “best known configuration.”

  • A regression suite so improvements don’t break other scenarios

Deliverables (What You Get)

  • SimLab™ Scenario Pack (20–60 scenarios tailored to their workflows)

  • SimLab™ Scorecard (rubrics + weights + pass/fail thresholds)

  • Model/Prompt Configuration Log (what changed, why, and impact)

  • Reliability Dashboard (baseline vs improved; by scenario type and failure mode)

  • Executive Decision Memo (what’s safe to launch, what isn’t, and risk posture)

​

​

Most AI failures aren’t “model problems.”
They’re design problems: unclear constraints, weak context, missing escalation rules, and no way to measure quality. SimLab™ makes AI adoption feel like engineering again:


test → grade → improve → verify → deploy.

We're Ready When You're Ready

bottom of page