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Case Study · Internal Product · AI Tooling

The Engine Behind Every AI Decision We Ship

Our proprietary AI benchmarking platform, built at Vertekx and used on every AI initiative we run. It executes our full suite of AI pipelines: document classification, OCR, and beyond, against thousands of test cases per run, so models, prompts, architectures, and costs are compared on evidence, never on intuition.

Multi-Model ComparisonPrompt EvaluationCost AnalysisIn-Memory SecurityProduction Gates
30xR&D throughput increase
10 → 1,000Test cases per run
100%Decisions backed by data
Why We Built It

The AI landscape changes monthly. Gut feel doesn't scale.

01
Weeks of manual R&D per decisionComparing two models on a real pipeline meant hand-running samples, collecting outputs in spreadsheets, and eyeballing the differences. Multiply that by every prompt variation and the calendar disappears.
02
A landscape that won't sit stillNew models and versions land monthly, each claiming to be better. Without a repeatable harness, every release forced a choice: re-test everything by hand, or ship on faith.
03
Regressions hiding in upgradesA model that improves on averages can quietly fail on your edge cases. Ten hand-picked samples will never catch what a thousand real-world cases will.
04
Claims we couldn't defendTelling a client a pipeline is 94% accurate means nothing without the evidence behind it. We needed numbers we could show, reproduce, and stand behind.
What It Does

One run answers the questions that used to take weeks

Point it at any AI pipeline, load a test suite, and every candidate configuration runs against the same cases under the same conditions. The output is a side-by-side scorecard, not an opinion.

The FoundationFull pipeline executionRuns our entire AI suite: document classification, OCR, validation, and beyond, exactly as production would. Real pipelines, real cases, not simplified proxies.
A / BModel vs modelCandidates run the same cases under the same conditions. Accuracy, latency, and failure modes land in one side-by-side scorecard.
“…”Prompt variations at scaleEvery prompt candidate is scored across the full suite, so wording changes are measured, not debated.
$ / caseCost per outcomeToken and compute costs are tracked per case, per configuration. The scorecard shows what accuracy actually costs at volume.
v2 → v3Regression trackingEvery run is comparable to the last. When a model update moves a number, we know exactly which cases moved it and why.
Production-readiness gatesA configuration ships only when it meets thresholds on thousands of cases. Marking something production-ready is a measurement, not a meeting.
The Product

Inside the platform

Document Classification Benchmark Testing with Azure and Gemini Pipeline
Document Classification Benchmark Testing with Azure and Gemini Pipeline
Side-by-side model comparison: Nova, LLaMa and Paddle
Side-by-side model comparison: Nova, LLaMa and Paddle
Flexible test suite development
Flexible test suite development
From Our CTO
“

This platform changed how we do AI, full stop. R&D cycles that took weeks of manual testing now finish in hours, so we evaluate every promising new model the week it releases instead of the quarter after. Nothing ships until the benchmark says it's ready, and when a client asks why we chose a model, we show them the data. Our decisions are backed by actual numbers, not guesses, and that has made us both faster and far more confident in what we put into production.

CTO
Chief Technology OfficerVertekx
Security by Configuration

Sensitive test data lives in memory, then it's gone

Security is configurable per test suite. When benchmarks involve sensitive data - PCI data like credit card details, or personal information - the platform processes it entirely in memory. Nothing touches disk, nothing is logged, nothing is cached. The data exists exactly as long as the test needs it.

Step 1Loaded in memorySensitive test data, including PCI data like credit card details, is loaded straight into memory. It never touches disk, logs, or caches.
Step 2TestedPipelines run against the in-memory suite. Only scores and metadata are recorded, never the sensitive payloads themselves.
Step 3VerifiedResults are checked and signed off while the data still exists. Verification is the last moment it is needed.
Step 4GoneThe moment testing completes, the data is released. No archive, no residue, no trace anywhere in the system.

Once results are verified, there is no trace of the sensitive data anywhere in the system. That is not a cleanup job, it's the architecture.

The Impact

Every Vertekx AI project passes through it

The accuracy figures in our Tourist Tax Refund case study were earned here before they were ever claimed. Production-readiness at Vertekx is a gate, not a judgment call: a configuration ships only when the benchmark proves it on thousands of real-world cases.

30x
R&D ThroughputEvaluation cycles that took weeks now finish in hours, so we test more ideas, more often.
1,000
Cases per RunFrom ten hand-picked samples to a thousand real-world cases, every single run.
100%
Data-Backed DecisionsEvery model choice, prompt change, and go-live is justified by benchmark evidence.

Want your AI decisions backed by evidence?

The same platform that de-risks our AI work can de-risk yours. Let's talk about benchmarking your models, prompts, and pipelines before they reach production.

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