AIM Governance Gateway · Partner Preview

Your AI made a
correct decision.
For the wrong reason.

In an automated pipeline, no one reads the reasoning. They only see the output. AIM is the governance layer that screens AI at the reasoning level — not just the conclusion — before anything propagates downstream.

aim · governance · live session

Input · AI Pricing Agent Output

status: force_majeure_active
spot_price: 450 ← noted
execution_price: 150
conclusion: "price frozen at baseline"

↓

AIM Verdict · Blocked

BOUNDARY_VIOLATION
reason: STRUCTURAL_CONTAGION
Conclusion correct. Reasoning routes through spot_price — prohibited by CLAUSE 8. Downstream tasks will inherit contaminated path.
action: regenerate → corrected_input

↓

Healed Output · Endorsed

ENDORSED · attempt 2
reasoning_path: "clause-direct"
spot_price: not referenced
execution_price: 150
basis: "CLAUSE 8 ABSOLUTE only"

See it live

AIM in action — live governance session.

Watch AIM intercept a structurally contaminated AI output, construct corrective feedback, and endorse the healed output — all without human intervention.

Force Majeure pricing governance — live intercept
Autonomous regeneration loop — closes in real time
Full telemetry — attempt count, verdicts, endorsement record

aim · live demonstration

Force Majeure pricing · live intercept CONFIDENTIAL · PARTNER PREVIEW

01 · The Problem

AI governance has
a blind spot.

Standard evaluation checks whether the output is correct. Nobody checks whether the reasoning that produced it is safe to propagate.

Correct conclusions. Contaminated paths.

An AI can produce the right answer by reasoning through a prohibited or ungrounded reference. The number looks right. The audit log reveals a problem later.

Agentic pipelines compound silently.

Contaminated reasoning at step two becomes the grounding assumption at step five. By the time it surfaces, the source is invisible and untraceable.

No human reviews agentic outputs.

In automated workflows, outputs pass directly to the next task. Without a governance gate, ambiguous reasoning travels downstream — silently, at scale.

02 · The Solution

AIM: the governance layer that catches
what evaluation misses.

A deterministic constraint enforcement layer — model-agnostic, independent of the AI it governs — that screens every output at the reasoning level before downstream action.

Reasoning-level screening

AIM maps every AI output against a defined safe action set. It evaluates logical structure — not just conclusions — to detect structural contagion before it propagates.

Autonomous regeneration loop

Non-compliant outputs are not simply rejected. AIM constructs structured corrective feedback upstream and re-evaluates automatically until the output is endorsed.

Deterministic and auditable

The same input always produces the same verdict. Every attempt, correction, and endorsement is logged — reproducible and ready for compliance reporting.

Model-agnostic by design

AIM governs the AI output, not the AI model. Connects to any foundation model — GPT, Claude, Gemini, custom — without re-engineering your existing stack.

03 · How It Works

Four steps. Closed loop.
No manual re-prompting.

Natural language goes in. Endorsed, structurally clean output comes out. Everything in between is governed, deterministic, and logged.

STEP 01

Input arrives

Your AI model produces an output — a decision, pricing action, compliance assessment. AIM receives it before it reaches any downstream task.

INTERCEPT

STEP 02

Reasoning evaluated

AIM's constraint operator maps the output against the defined policy set. It checks logical structure — not just the conclusion — for contaminated references and prohibited paths.

EVALUATE

STEP 03

Violation corrected

If a violation is detected, AIM constructs precise, actionable feedback and sends it upstream. The generating model revises. AIM re-evaluates. Loop continues until endorsed.

CORRECT

STEP 04

Clean output proceeds

Only outputs with a structurally clean, clause-direct reasoning path are endorsed and proceed to downstream tasks. Every verdict is logged and auditable.

ENDORSE

04 · Proof of Concept

Live example: Force Majeure
pricing governance.

The scenario that convinced our first enterprise partner — an AI with the right answer, and the wrong path to get there.

Live governance session · Force Majeure pricing

INPUT

AI agent output — conclusion numerically correct

Agent acknowledges Force Majeure is active. Notes spot price is 450. Concludes execution price remains 150. Payload: {"status":"frozen","execution_price":150}

BLOCKED

AIM verdict: STRUCTURAL_CONTAGION

Conclusion is correct. But reasoning acknowledges and routes through spot price — explicitly excluded by Clause 8 Absolute. Any downstream repricing trigger or audit query will encounter the prohibited reference.

LOOP

Structured correction sent upstream

AIM constructs precise feedback: remove spot price reference, ground reasoning in Clause 8 Absolute only. Model revises. AIM re-evaluates.

ENDORSED

Attempt 2 — clause-direct reasoning confirmed

Spot price not referenced. Basis attributed solely to Clause 8 Absolute. Reasoning structurally clean. Output proceeds. Final status: Repaired.

Key insight: AIM did not block this output because the number was wrong. It blocked it because the reasoning path was open to interpretation. In a downstream agentic task, that contaminated reasoning would have produced a compliance failure no one would have caught.

"AIM screens at the reasoning level, not just the output level. This is the precise gap it is designed to close."

Debajit Das

Founder, AI Robotics Technology

Under the hood: AIM's constraint architecture draws on set-theoretic control (invariant set enforcement) and runtime verification principles — deploying a deterministic finite-state monitor from formal policy specifications. Peer-reviewed foundations available to enterprise partners on request.

Applicable across domains

Finance & Trading

Pricing agents, compliance checks, automated deal execution

Legal & Contracts

Clause interpretation, counterparty negotiation governance

Industrial

Safety-critical inspection outputs, certification workflows

Enterprise Policy

Internal comms, policy alignment, human + AI content governance

05 · The Agentic Risk

Without AIM, your pipeline
is a liability.

The shift to agentic AI changes the risk profile entirely. What was recoverable in a human-reviewed workflow becomes a compounding, invisible failure in an automated one.

Without AIM

Correct conclusions with contaminated reasoning pass standard screening and enter pipelines unchecked

Ambiguous reasoning compounds task-to-task — step five inherits step two's errors as grounding assumptions

By the time a failure surfaces, the source is invisible. Root cause analysis is guesswork.

Manual re-prompting when a blocked output is caught — not viable at production scale

With AIM

Every output is evaluated at the reasoning level before it proceeds — structural contagion caught at the gate

Non-compliant outputs corrected automatically — no manual intervention, no pipeline interruption

Full telemetry: every attempt, verdict, correction, and endorsement is logged — auditable and traceable

Model-agnostic enforcement — works with any foundation model, integrates as a sidecar to your existing stack

06 · Open Edge Release

Explore AIM in your environment.
Before you commit to anything.

We are releasing an open-source front-end in the next 6–8 weeks. Download, configure your interaction protocol, and connect to the cloud-hosted governance endpoint.

Download the front-end code
Configure an interaction protocol for your own environment. No infrastructure overhaul required.
Connect to any AI model
Model-agnostic. Plug in GPT-4, Claude, Gemini, or your own fine-tuned model.
Cloud governance endpoint
Constraint enforcement logic lives in the cloud. The local layer calls it on demand.
Enterprise controls intact
Operates within your existing access permissions. No new permissions required.

# AIM Edge — configure once, govern everything

aim_config = {

  "governance_endpoint": AIM_CLOUD_URL,

  "model": "your-model-here",

  "policy_set": "your-policy.json",

  "max_attempts": 3,

  "telemetry": True

}

# Wrap any AI call

result = aim.evaluate(

  input=agent_output,

  config=aim_config

)

# Result

result.verdict  # ENDORSED / BLOCKED

result.attempts  # 1 or 2

result.audit_log  # full trace

result.endorsed_output  # clean payload

07 · Why Act Now

The window is open.
It will not stay open.

Organisations that govern their AI pipelines now own the audit trail, the trust narrative, and the commercial advantage.

0^%

of agentic AI outputs currently screened at the reasoning level across most enterprise deployments

6–8^wk

until open-source edge release — early partners get direct onboarding and priority access

2^×

average attempts to endorsed output in live sessions — the loop is fast, and it closes

"Your organisation does not need to understand every line of the constraint logic. You need to know that when your AI reasons incorrectly — the problem stops here." — AI Robotics Technology

08 · Get Started

The risk is commercial,
not technical.

You need one workflow, one policy, and four weeks. We do the configuration. You see the governance in action on your own data.

Proof of Concept
Programme

Your workflow. Your AI model. Your policy constraints. AIM deployed and governing — in your environment, on your terms.

NEGOTIABLE ENGAGEMENT TERMS

What is included

✓

One end-to-end workflow — you identify the use case; we configure the governance layer around it

✓

Policy constraint mapping — your rules, formalised into AIM's constraint set, by our team

✓

Live governance sessions — watch AIM intercept, correct, and endorse in real time, with your data

✓

Full telemetry export — every verdict, correction attempt, and endorsement record for your audit team

✓

Model-agnostic — connects to whichever AI model is already running in your pipeline

How commercial risk is managed

✓

Fixed PoC scope — no open-ended commitments; bounded to a defined workflow and timeframe

✓

No infrastructure overhaul — AIM deploys as a sidecar; your team's time commitment is minimal

✓

Edge release available — explore system behaviour independently before committing to a full PoC

✓

Governance, not replacement — AIM does not replace your AI stack; it makes it defensible

✓

Priority onboarding — warm introductions from trusted contacts receive dedicated configuration support

09 · Contact

One workflow is all it takes.

Tell us which part of your AI pipeline keeps you up at night. We will show you what AIM sees — and what happens next.

Request a PoC → Get early edge access

debajit@airobotics.technology

airobotics.technology

Governance · Agentic AI · AIM Platform

Notify me when live →

Coming in 6–8 weeks

Your AI made acorrect decision.For the wrong reason.