Institutional AI-evaluation & safety-research lab · data-sovereign

Safety, logic, and alignment of frontier AI models — independently evaluated.

A highly specialised evaluation lab in boutique format: senior depth instead of crowd, with sensitive data processed locally and under NDA — without cloud-leakage risk. The evaluation practice is matched by an applied research agenda on the safety of multi-agent AI systems.

MODEOn-premises TERMSNDA-bound DEPTHSenior, not crowd DUALOperations + research

CODE→CAPITAL

From the engine room of model evaluation into a dedicated wealth- and reinvestment architecture.

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What we do — CTC AI Operations

Root-level evaluation of how frontier models reason, where they fail, and whether their behaviour holds under pressure — judged by a senior practitioner, not distributed annotation.

Root-level model evaluationLogic, correctness, robustness — assessed at the level of reasoning, not surface output.

RLHF & preference-data qualityEvaluation of the training signal itself, not only the model's responses.

Dataset QA & annotation auditsChecks for consistency, bias, and coverage gaps across labelled data.

Agentic-AI safety & red-team auditsBehaviour under adversarial conditions and open-ended tool use.

How engagements run

→Local processingSensitive material is handled on-premises; no third-party cloud handoff.

→Under NDAEvery engagement is scoped and governed by a strict confidentiality agreement.

→Clients in the abstractEnd clients are never named — engagements are referenced only as work with leading AI labs.

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Research initiative

The evaluation practice feeds an applied research agenda — two separate but mutually reinforcing questions. Operations asks whether a given model is safe and correct; research asks how the safety of multi-agent systems can be measured and proven at all. Grounded in CTC's ongoing evaluation work.

Central question How can cascading failure modes and emergent risk behaviour in multi-agent AI systems be systematically identified, quantified, and formally verified — before such systems reach production?

TF-1

Evaluation taxonomy for multi-agent interactionsA structured map of failure modes specific to agent networks: cascading hallucinations, delegation loops, implicit collusion, goal drift across agent boundaries.

TF-2

Quantitative risk metrics for agent networksMeasurable indicators adapted from distributed-systems safety — fault-propagation probability, cascading-failure index — applied to autonomous-agent dynamics.

TF-3

Testbed architecture for controlled evaluationA reproducible, on-premises sandbox for multi-agent scenarios, including adversarial injection and stress testing.

Funding: selected threads are being prepared for submission to non-dilutive research funding in multi-agent safety. Grants are upside, not foundation — the evaluation practice sustains itself.

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Why CTC

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Industry and research in one unitOperational evaluation access that pure academics lack, paired with methodological depth that pure vendors don't offer.

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Data sovereignty by designOn-premises processing under strict NDA. The value is demonstrable data custody, not raw compute.

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Senior depth, not crowdEvaluation carried out by a principal practitioner — depth and judgement over distributed volume.

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NDA disciplineConfidentiality is structural. Client identities stay private; references remain in the abstract.

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Interdisciplinary founder DNADeep AI craft paired with a finance- and capital-markets mindset — the basis of the "Code to Capital" thesis.

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Model & growth thesis

PHASE ICurrent focus active core practice · entity in formation

CTC AI Operations & Research

The evaluation lab and its applied research agenda. Practised today through senior mandates with leading AI labs; the company is being formed around it.

PHASE II year 2 · in preparation

CTC Advisory

Secure-by-design IT infrastructure, RAG systems, and professional client presences for the DACH mid-market — a capability the company's own brand and web infrastructure already demonstrates.

PHASE III target state · separate entity

CTC Wealth

Reinvestment and wealth architecture — license-compliant in its own regulated structure.

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Leadership

Founder & CEO · Principal Investigator

Marian E. Arenskrieger

Owns the evaluation lab, the research agenda, the transfer of live mandates into the entity, the structural and tax setup, and the long-term capital architecture.

→arenskrieger.dev · Principal-Investigator profile

Co-Founder · VP of Commercial

Commercial & client-facing seat

Active in adjacent AI contracting (voice auditing, AI ad review); builds the go-to-market layer of the Advisory arm and grows into higher-value evaluation work.

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Digital presence

codetocapital.aiInstitutional enterprise platform in build arenskrieger.devPrincipal-Investigator profile live