An independent, benchmark-anchored verdict on whether your AI behaves correctly under financial-services regulatory frameworks — and whether it's safe to deploy. Not your tool grading its own homework. We run the evaluation; you receive the report.
100+ verified items · 25+ regulatory frameworks · 38 cross-regulatory
predict-first methodology · scored against a gold standard · academic validation in progress
Adoption is racing ahead of the evidence base underneath it. The window to get methodologically-defensible evidence in place is closing.
High-risk AI obligations under the EU AI Act apply from 2 August 2026. A Digital Omnibus delay to 2027–28 was provisionally agreed in May 2026 but is not yet law — firms are still expected to prepare now.
Per the EU AI Act: €35 million or 7% of global turnover, whichever is greater — applied to the deploying firm.
Industry surveys benchmark adoption; capability benchmarks test financial reasoning; regulatory frameworks specify obligations. None tested AI behaviour against gold-standard governance dispositions across FS frameworks — until the Mergetic Reliability Benchmark.
A bad decision doesn't wait for review — it propagates before anyone notices. Three structural problems make today's deployments an unstable equilibrium, and none is solved by the model grading its own work.
When one model both generates a regulated output and judges whether it's acceptable, the judgment is correlated with the generation. When the output is risky, the self-assessment trends with the same risk. The system can't catch its own worst failures.
EU AI Act Articles 12, 14 and 26 presume the recorded rationale can be independently verified. An audit trail that is the model narrating its own output does not satisfy that presumption in the form regulators will require.
Internal evaluations document your own due diligence; vendor benchmarks read as marketing. Neither is seen by a supervisor as third-party independent. Most safety tools score outputs — Mergetic judges them against a gold standard.
A productised, done-for-you evaluation. You bring a model or deployment; we run it against a fixed, versioned benchmark and return a scored verdict — with residual risk identified item-by-item, not hidden.
We agree the system under evaluation and the frameworks in scope for your workflow, under a short mutual NDA.
We run the benchmark on our infrastructure against gold-standard answers using a fixed, versioned rubric. Your effort is endpoint access.
You receive the Reliability Evaluation Report — verdict band, dimension scorecard, per-item results, critical findings and recommendation.
A clear basis for whether to deploy, and under what oversight — and a route into governed runtime if you choose it.
The evaluation works whether or not you give us access to your stack — so an IP- or security-sensitive firm is never blocked from getting a verdict.
We send you the benchmark prompts selected for your regulatory regime. You run them through your own AI stack and return the outputs. We score them against the gold-standard answers and produce the report. No access to your systems required.
With endpoint access under NDA, we run the full benchmark against your deployment ourselves, capture the outputs, and score against the gold standard. Your effort is endpoint access and a findings call.
A single cross-regulatory item engaging CBI vulnerable-customer guidance and GDPR Article 22 at once. This depth, times 100+ records, is the benchmark.
CBI CPC 2025 vulnerable-customer guidance and GDPR Article 22 (automated decisions) — two regulatory regimes at once.
Committed before execution: withhold the immediate automated decision and route to human review — on both vulnerability and automated-decision grounds.
Matched the predicted disposition — and surfaced an additional firm-side policy gap the prediction did not anticipate. A deeper finding than expected.
Two-layer scoring — a verdict band first, then a transparent roll-up — so the headline is legible to a risk committee and the reasoning is auditable.
A single Green / Amber / Red band and composite score, readable in seconds.
Accuracy · Risk & ethics · Compliance — scored separately, because they aren't the same thing on regulated tasks.
Side-by-side on identical items: ungoverned behaviour, and behaviour under independent governance.
Every record, colour-coded by verdict. Residual risk surfaced item-by-item, not averaged away.
Fabrications and high-risk failures surfaced, with a deployment-readiness recommendation and oversight conditions.
The scoring method and dimensions, so the verdict can be defended — and reproduced on the next version.
Every item carries a gold-standard answer — the disposition a regulator-aligned expert says is correct, committed before your model runs. Your AI is measured against that, not against its own confidence and not against a generic accuracy metric. That's why an evaluation stands up as third-party evidence where an internal benchmark or a vendor model card won't.
The outcome envelope is committed before the item runs, documented in a peer-review-ready paper. Academic benchmarks fix gold answers at publication; predict-first pre-commits the disposition for every record — the difference between a prediction and a rationalisation.
Outputs are scored on weighted dimensions calibrated for the FS risk profile — substantive correctness, citation reliability, disposition appropriateness — not a single accuracy metric.
Distinguishing a wrong output from a fabricated one from an accepted adversarial prompt — not a correct/incorrect binary.
38 items invoke two or more frameworks at once, testing reasoning across overlapping and conflicting requirements — a depth of multi-framework coverage we're not aware of in any public benchmark.
Inter-rater reliability coding of the dataset is engaged with the Applied Innovation Unit, University of Galway, under Innovation Voucher IV20250487.
The dataset has evaluated two independent implementations of the same architecture. The cross-architecture comparison is documented in the in-preparation academic paper.
In the IAPP 2026 AI-governance vendor landscape, the field splits cleanly: firms that evaluate AI, and firms that govern it at runtime. Mergetic is the only entry that does both — and the evaluation is the front door to the governance.
The structural gap is not a feature — it's an engineering programme. For an evaluation firm to add runtime governance is 12–18 months of work plus patent risk. For an enforcement vendor to add an FS-calibrated, cross-regulatory benchmark requires regulatory expertise that isn't distributed across AI-safety teams.
And the two products feed each other. Every evaluation sharpens the benchmark; every governed decision generates new scenarios. The asset improves through use.
Judge separated from generator
Per-decision binding veto
Hash-chained, tamper-evident
Any model, any cloud
The only entry in the IAPP 2026 landscape to claim all four pillars — and the only one to publish empirical validation across two independent implementations.
The benchmark evaluation tells you whether your AI is safe to deploy — it scores your outputs against gold-standard answers. Mergetic Runs is the separate, optional next step: a structurally independent governance layer that holds binding veto over every output, live in production. The two are sold separately, and adopted in sequence only if you choose — the evaluation is simply the front door.
Most AI tools score outputs.
Mergetic Runs gates them.
A controller can approve an output and an independent governance layer — with its own reasoning and its own rulebook — can still block it. That's the structural separation a single model can't reproduce. Fail-closed by design: nothing acts without clearance.
Mergetic Runs — the Control Tower for AI. In pre-pilot, targeting first paid engagements Q3 2026. Every decision is hash-chained for a tamper-evident audit trail.
Explore Mergetic RunsThe controller approved this output. Governance independently blocked it and routed it to human review — a structural check a single-model pipeline cannot reproduce.
The evaluation reads differently depending on where you sit. Pick the one that fits.
Your FS clients ask you to evaluate their AI against their regulatory obligations — and today you answer with ad-hoc frameworks no regulator would accept. The benchmark's predict-first methodology and cross-regulatory structure aren't replicated by any public competitor. Carry co-branded evaluation reports into client AI-governance files.
Deliverable: co-branded reliability reports, "delivered by [your firm]"FS prospects stall in procurement on "deployment risk" — your model cards read as marketing and audit firms lack a benchmark methodology. A governance-layer reference that complements your model, and an independent harness that gives your sales team a scored report citable in FS RFPs.
Deliverable: scored report on a named model, with a readiness bandYour supervisor expects methodologically-defensible evidence that your deployed AI is governed adequately. Internal evaluations document your own due diligence — they're not seen as third-party independent. An independent, scored evaluation scoped around a specific regulatory workflow, for your AI-governance file — and, where you choose, a route into governed runtime.
Deliverable: independent scored evaluation for your AI-governance fileEvery step is low-commitment and reversible — the risk is front-loaded onto us, not you. The deeper material opens up as the conversation gets serious.
This page, the Executive Briefing and the one-page summary. Establishes category and credibility. Forward them freely.
The methodology brief and your sector-specific deck. We identify a candidate evaluation and agree a short mutual NDA.
A sample evaluation against an open model, walked through as a report, so you assess deliverable quality before any commitment.
We run the benchmark against your named system and deliver the report, alongside an engagement letter or LOI.
Mergetic is built and operated by Gratitude Beacons Ltd, an Irish company, anchored in a verifiable IP estate and an independent academic engagement. Every claim on this page has a published artefact behind it.
Irish Patent 2025/0516 (filed Nov 2025); EPO international filing completed 30 April 2026, prosecuted by Mewburn Ellis. The split-authority methodology itself is protected IP.
Inter-rater reliability coding at the Applied Innovation Unit, University of Galway, under Enterprise Ireland Innovation Voucher IV20250487. A named academic partner, not a self-assessment.
A research paper documenting the architecture, cross-architecture reproduction, and empirical findings is in pre-submission preparation, targeting a peer-reviewed AI-governance venue.
Request a Reliability Benchmark evaluation, or a conversation about where governed runtime could take your deployment.
john@mergetic.com