AI and LLM evaluation for organizations that need more than a benchmark number. We measure how systems perform on representative data, how they fail at the edges, how they behave inside the workflow that surrounds them, and whether the resulting evidence holds up to regulatory and audit scrutiny.
Most AI projects skip evaluation. A model is selected on a vendor demo, integrated against a small set of happy-path examples, and shipped on the assumption that benchmark-grade performance will survive contact with production. It rarely does. The gap between how a model scores on a clean dataset and how it performs once embedded in a real workflow is where most AI failures live.
The failure modes are predictable. Models that score well on English benchmarks degrade sharply on multilingual or code-switched field data. Systems that look accurate on average fail on the long tail where the operational cost of a wrong answer is highest. Hallucinations appear in production that no one saw in development. Workflow integration assumes operator behavior that does not match how the role is actually staffed. Regulators ask for evaluation evidence the team did not generate.
Evaluation closes that gap. Done well, it surfaces the operational failure modes before they cost something. Done poorly, or skipped entirely, the system itself becomes the evaluation — and the cost of finding out lands somewhere it should not.
Implementation science is a research discipline focused on how complex interventions perform once they move out of controlled settings and into the field. It originated in health and public-sector research, where the question is rarely whether a treatment works in a trial — it is whether the same treatment survives the workflow, staffing, organizational fit, and adoption constraints of an operating environment.
The methodology maps directly onto AI evaluation. A benchmark number is the controlled-trial result. The harder question — and the one this discipline is built to answer — is what happens once the system has to operate inside a real environment with real users, real data, and real consequences.
Whether operators can actually use the system under realistic time and attention constraints — not under demo conditions.
Failure modes, adversarial robustness, and harm surface assessed proportionate to the system's risk tier.
Whether the system's behavior matches the specific operational, regulatory, and domain expectations of where it will run.
Whether the workflow and human factors around the system support sustained use — not just initial pilot enthusiasm.
Whether the system improves the outcome it is supposed to improve, measured against a baseline the organization already lives with.
Whether the evaluation evidence holds up over time as data drifts, operators rotate, and the surrounding workflow evolves.
Every evaluation engagement is scoped to the question being asked, but the dimensions that determine fitness for deployment are stable across contexts. These are the six we measure against — sized proportionate to risk tier, but never selectively omitted for a high-stakes system.
Quantitative performance against a representative evaluation set. Task-specific accuracy, precision and recall, calibration, latency, cost-per-call, and stability across runs. Reported with confidence intervals and against a baseline of credible alternatives.
Structured analysis of how the system fails and what it costs when it does. Hallucination patterns, edge-case behavior, distribution-shift response, and silent failures the system does not signal. Each failure mode is documented with reproducible inputs and a severity classification.
Performance on data that resembles what the system will actually see — messy, partial, multilingual, out-of-distribution, or temporally drifted. Models that look strong on clean benchmarks often degrade sharply on field data; this is where that degradation gets measured.
How the system performs inside the human workflow that surrounds it. Whether outputs are actionable, whether reviewers can verify them efficiently, whether the system improves or degrades cycle time, and whether adoption is plausible at the scale the deployment plan assumes.
Adversarial robustness, prompt-injection resistance, data exfiltration paths, bias on protected categories, and harm surface analysis. Sized to the system's risk tier — proportionate for low-risk internal tools, structured red-teaming for high-risk and regulated deployments.
Whether the evaluation evidence will hold up to the regulatory and audit regime the system will operate under — privacy, sector-specific compliance, procurement scrutiny, or model-card and impact-assessment requirements. Evaluation artifacts produced in formats audit functions can consume.
We do not run a single fixed protocol against every system. The methods below are combined and weighted based on the system's risk tier, the question being asked, and the regulatory or audit regime the evidence has to satisfy. Every method we use is documented in the evaluation report so the methodology is auditable and the findings are reproducible.
Evaluation datasets built from your data, your use cases, and the input distribution the system will actually face — not off-the-shelf academic benchmarks. Sampling is documented; coverage is justified; results are reported against a baseline of credible alternatives so the numbers are interpretable.
Structured probing for prompt injection, jailbreaks, hallucination triggers, identity and data exfiltration, bias on protected categories, and domain-specific abuse patterns. Attack trees and probe sets are documented so the exercise is reproducible and the residual risk is legible.
Quantitative measurement combined with qualitative inquiry — structured interviews with operators, observational analysis of workflow integration, and survey instruments where appropriate. Human factors that determine real-world performance are visible in the evaluation, not assumed away.
A structured scoring rubric across the dimensions that determine whether a system can actually be deployed — technical fitness, operational fit, workflow absorbability, safety posture, regulatory fit, and supportability. Output is a single readiness assessment with the underlying evidence preserved.
Side-by-side evaluation of the candidate system against credible alternatives — competing models, prior systems, or non-AI baselines. The most actionable evaluation is rarely how good is this system; it is how does this system compare to the realistic alternatives for this decision.
Evaluation engagements arrive through a small number of recurring triggers. Each shape is scoped differently — a vendor selection study has different artifacts than a regulatory submission, and a post-incident analysis has different urgency than a routine pre-deployment review.
You are choosing between competing AI vendors or model providers for a defined use case. You need a structured comparison against your data, your performance dimensions, and your operational constraints — not vendor-supplied benchmarks.
You have built or procured a system and are weeks away from production. Before it goes live you need an independent evaluation that answers whether it performs on representative data, where it fails, and whether it is fit for the deployment context.
The system requires documented evaluation evidence for a regulator, auditor, ethics review board, or procurement function. The artifacts have to satisfy a specific external reviewer, with methodology and findings preserved in a format that reviewer can consume.
An internal team has built a prototype that looks promising in demo. Before scaling it, an independent evaluation determines whether the demo-level performance survives contact with field data and operational workflow.
A deployed AI system has produced a material failure — a wrong decision, a safety event, a regulatory finding, or a costly false positive. A structured post-incident evaluation determines the root cause, the failure surface, and the remediation required before continued operation.
Evaluation engagements at G.E.T AI Labs are led by Dr. Tyler Marshall, PhD, MPH — Adjunct Assistant Professor in the Department of Psychiatry at the University of Alberta, with a research focus on the evaluation and implementation of complex interventions in real-world settings. He brings the methodology directly: systematic reviews, qualitative research, survey design, and applied clinical studies — the mixed-methods toolkit that implementation science was built to deploy.
Technical evaluation methodology is paired with Tejas Vyas's AI architecture background — Principal Investigator at the AI Hub at Durham College, with doctoral research in artificial intelligence and computer vision and contribution to fifteen-plus applied AI industry projects. The pairing means the evaluation evidence reads correctly to both a regulator and an engineer: methodologically defensible and technically literate.
The team's institutional roots — University of Alberta, Durham College AI Hub, HubSpot — are documented on the team page along with the wider bench of researchers, engineers, and domain specialists assembled per engagement.
G.E.T AI Labs is independent of any AI vendor. Evaluation findings are reported as they are.
Direct answers about evaluation methodology, the distinction between evaluation and testing, pre-deployment validation, adversarial testing, and engagement timelines.
AI evaluation is the systematic assessment of an AI system against the conditions under which it will actually be used. It goes beyond standardized benchmarks to measure performance on representative data, failure-mode behavior, workflow integration, safety profile, and operational readiness. A serious evaluation answers four questions: how well does the model perform on the task as it actually appears in the field, how does it fail and at what cost, can the surrounding workflow absorb its outputs, and is the resulting system fit to deploy in this specific context.
Implementation science is the study of how complex interventions perform when they move from controlled environments into real-world settings. It originated in health and public-sector research, where a treatment that works in a trial often fails in the field for reasons that have nothing to do with the treatment itself — workflow, staffing, organizational fit, adoption, sustainability. Applied to AI, the lens is identical. A model that scores well on a benchmark is the controlled-trial result. Whether it performs once embedded in a clinical workflow, a procurement process, or a public-sector case file is an implementation question. Implementation science gives evaluation a structured methodology for answering it.
AI testing typically refers to functional verification — does the system produce expected outputs on a defined set of inputs, does the API contract hold, does the integration pass its tests. AI evaluation is broader. It asks how the system performs under realistic input distributions, how it degrades at the edges, what its safety and failure profile looks like, whether human operators can actually use it, and whether it meets the regulatory and audit bar required for its setting. Testing is a subset of evaluation. Evaluation includes testing plus everything else needed to judge fitness for a specific operational context.
Yes — pre-deployment evaluation is one of the most common engagement shapes. We design a representative evaluation dataset from your data and use cases, define the performance dimensions that matter for your context (accuracy, calibration, safety, latency, cost, equity, explainability, regulatory fit), and run a structured benchmark with adversarial probes. The output is a written evaluation report with quantitative findings, qualitative observations, identified failure modes, and an implementation-readiness assessment. Pre-deployment evaluation is particularly important for regulated environments where post-deployment surprises carry real cost.
Yes. Adversarial testing is a component of most evaluation engagements, sized to the risk profile of the system. We probe for prompt injection, jailbreaks, hallucination patterns, distribution-shift failure, identity and data exfiltration, bias amplification on protected categories, and domain-specific abuse vectors. For high-risk systems we run structured red-team exercises with documented attack trees. For lower-risk systems we run a proportionate set of adversarial probes inside the broader benchmark.
A focused pre-deployment evaluation typically runs four to eight weeks from kickoff to written report. A full implementation-readiness assessment — including workflow integration analysis, mixed-methods user research, and operational risk review — typically runs eight to twelve weeks. Vendor model selection evaluations against a defined shortlist are often shorter, two to four weeks. Each engagement is scoped to the question being asked; we do not pad evaluations with work that does not change the decision the evaluation is supporting.
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