KAHRELUM OS · evidence-to-decision control plane

I build and evaluate AI and research systems that stay human-approved before action.

KAHRELUM OS is the stack. Modules include RECORD LOCK, Intel Tripwire, and the AI Red-Team Dashboard. Three ways to work with me: buy a ready-to-use evaluation toolkit, commission a custom research/decision system, or hire an authorized AI red-team assessment.

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Three offers

Choose the outcome you need.

Prices increase with customization, implementation effort, analysis, reporting, and responsibility for delivery.

01 · Toolkit

AI Evaluation Toolkit

$15–$99

Starter and Pro evaluation material for developers, evaluators, and consultants: setup guidance, curated test objectives, review templates, and reusable evaluation workflows.

Proof: AI Red-Team Dashboard Community Edition and commercial test structure.

View product → · Repository

02 · Build

Custom Research / Decision System

$500–$1,500

A scoped research, evidence-verification, or decision-support workflow tailored to your use case. Deliverables can include structured schemas, source rules, evaluation logic, documentation, and deployment support.

Proof: KAHRELUM OS modules — RECORD LOCK, Intel Tripwire, and deployed research products.

Inspect modules → · Request build

03 · Assessment

Authorized AI Red-Team Assessment

$2,500–$5,000

A performed assessment for an authorized AI application: scope definition, adversarial testing, findings analysis, severity/risk classification, remediation recommendations, written report, and debrief.

Proof: deployed red-team demo, sanitized sample finding, transparent methodology, and inspectable source.

See sample finding → · Repository

Proof before pitch

You can inspect the work before buying.

Deployed products

Live public products demonstrate AI evaluation, evidence verification, research architecture, and decision-support workflows under KAHRELUM OS.

View modules →

Machine-enforced quality

Selected public repositories include JSON Schemas, domain tests, link checks, security policies, release metadata, and scheduled production smoke monitoring.

Inspect RECORD LOCK →

Human accountability

AI may assist with coding and synthesis. Research direction, evidence standards, analytical judgment, QA, and final publication decisions remain human-directed.

Read architecture →

KAHRELUM modules

Public proof of capability

Product names stay as modules under KAHRELUM OS. The deeper ecosystem is here for verification, not as the first thing a buyer has to decode.

01 · OS

KAHRELUM OS

Architecture and doctrine.

Evidence-to-decision control plane: claim classification, verification, human approval gates, and auditable action states. Not a kernel OS — an application-layer control plane.

Open architecture → · Live doctrine

KAHRELUM OS portfolio preview

02 · PLATFORM

RECORD LOCK

Evidence-verification and controlled-publication layer.

Claim-level records with provenance, counterarguments, falsifiers, confidence controls, revision conditions, and public-safe reference architecture.

Open platform → · Repository

03 · AI EVALUATION

AI Red-Team Dashboard

Authorized model-behavior evaluation and commercial assessment workflow.

A public Community Edition plus a deployed buyer-facing demo showing test categories, a sanitized finding, pricing, and a commercial inquiry path.

Open demo → · Repository

04 · OPERATING PICTURE

Intel Tripwire

Staged operating-picture and source-health workflows.

Research signals, source discipline, and auditable decision workflows for work that must remain reviewable.

Repository →

05 · PUBLIC REFERENCE

The Epstein Record

Source-first evidence-classification architecture.

A sanitized public reference demonstrating provenance, privacy controls, evidence ceilings, corrections, schema validation, and production smoke monitoring.

Open product → · Repository

06 · METHODOLOGY SURFACE

GrindWire / methodology site

Published methodology and calls record.

Evidence verification, constraint-first forecasting discipline, and public methodology under the KAHRELUM standard.

Repository →

How to buy

One message is enough to start.

Send the service you want, your target outcome, and your approximate budget. Do not send credentials, API keys, private customer data, or sensitive security details in the first message.

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