[01]
Diagnostic-first
We start from how your team actually operates and the constraints you are accountable to, before recommending a single tool.
aequai ~ / about
$ aequai about --mission
An operator's studio for accountable AI.
AequAI's mission is human-centered automation for fairer work and a more livable world. Most companies do not have an "AI problem", they have an operating problem. Tools are everywhere; ownership, boundaries, and evidence are not. We design the layer in between.
$ aequai whoami
A service and readiness layer, not a tool vendor.
// what we are
AequAI is a service and readiness layer for AI adoption, evidence operations, governance, simulation, and safe next-step decisions. We come in where AI is already being used in fragments and turn it into an operating model: what each department adopts, how agents act safely, who approves what, and where DPP or tokenization readiness genuinely reduces risk.
The company direction is practical: help teams adopt AI with clear workflows, fair responsibility, human approval, and evidence before claims. Implementation can be scoped separately or handed to platform partners.
// what we are not
[ ]An automation agency or a generic AI-training shop
[ ]A tool vendor, the service layer starts with workflow, evidence, and responsibility
[ ]A replacement for qualified advisors, legal, tax, AI Act, securities, DPP, carbon, and tokenization topics become question packs and advisor handoffs
[ ]A source of compliance, certification, or public-approval claims
$ aequai define ai-evidence-operations
Why "AI Evidence Operations".
AI Evidence Operations are governed AI workflows that collect, structure, review, approve, log, and maintain business evidence before it becomes a claim, report, product passport, or system action.
The risk is rarely poor prompts. It is unclear ownership, missing evidence, weak approval gates, and outputs that move too quickly from draft to decision. Our approach is evidence-first and human-centered: name the workflow, the data boundary, the owner, the review step, the source evidence, and the safest next action.
$ aequai principles
Four principles, applied every time.
[02]
Boundary-clear
Every service says what it is and what it is not. We make no compliance, certification, legal, tax, or investment claims.
[03]
Human-in-the-loop
Approval gates, ownership, rollback, and logs are part of the design, not an afterthought.
[04]
Evidence-based
You leave with reviewable artifacts and an implementation-partner brief, work that survives scrutiny, not slideware.
$ aequai operator --helen
Operating experience, not just method.
// helen · operator-cockpit direction
Helen is AequAI's first operator-cockpit direction, an internal model for approvals, evidence, logs, and accountable human control. Helen informs how we think about operating AI: a named owner, gated actions, recorded decisions, and a rollback path.
// boundary Helen is a development direction that informs our operator model. It is not a deployed client system, and it is not presented as customer proof.
Ali Osman Ozen
Founder & operator
AequAI is led by an operator developing Helen, its operator-cockpit direction, approvals, logged actions, and rollback treated as first-class. That is the discipline we bring to your workflows: where the real bottleneck is, what an action actually costs if it goes wrong, and the smallest safe step that moves you forward.
$ aequai rnd --internal
We develop our boundaries first.
Each offer is backed by internal development, frameworks, checklists, claim-safety language, and synthetic sample reports, so client work stands on tested ground rather than being improvised on the call.
track 01
Service architecture
The service catalogue, delivery templates, claim-safety language bank, and review structure.
track 02
Tokenization readiness
No-token-first screening, rights questions, and architecture options developed in local sandboxes and test environments only, no token creation or sale, and no chain or network actions.
track 03
DPP readiness
Product-data models, supplier-evidence mapping, and carbon-evidence question sets, no official passport issuance, no green-claim approval.
track 04
Simulation method
Synthetic-data workflow simulation: agent roles, event logs, approval gates, and failure modes before real authority.
These are internal development directions and synthetic examples. They are not deployed client systems, customer proof, or external validation.
$ aequai contact --map operations
Let's map where AI fits in your operations.
One conversation. We will review what is working, what is stalling, and the next concrete step, whether it is with us or not.