01
Reference
What source, claim, permission, or human contribution is in force?
Signal Fidelity Group
The bottleneck for AI
isn’t intelligence.
It’s trust.
Signal Fidelity Group is building an immune system for language — a closed-loop inference control system for the era of AI-mediated communications.
We preserve the meaning of language.
Signal fidelity for humans, organizations, and agentic AI.
Three forms of signal transmission. One solution.
The Lineage
Communications has always run on trusted intermediaries.
For a century, PR has been about earning the public’s trust by borrowing the credibility of others. Journalists, then radio, then television. Then the internet flattened access to information and the intermediary shifted to peers — bloggers, social media influencers, organic search. Through every era, communications chased the same prize: earned third-party validation.
The Shift
The intermediary just changed.
The trust intermediary is no longer a journalist, an analyst, or a search result. It is an AI inference. Quickly and comprehensively, large language models have become the layer through which messages reach stakeholders — summarized, rewritten, retrieved, and recommended before a human ever sees them.
The trust intermediary used to be a journalist. Then a peer. Then a search result. Now it’s an AI inference.
The Control Gap
When the intermediary is a machine, the old playbook doesn’t reach it.
Briefings, embargoes, key messages, spokesperson training — none of it governs an AI model. AI systems no longer just generate content. They infer, summarize, retrieve, rewrite, route, recommend, and act. The risk is no longer that the output is wrong. The risk is that the output drifts — away from the source, the claim, the permission, the evidence, the audience, or the intent it was supposed to preserve.
That drift creates business risk:
- A creative work loses the human contribution that gives it value.
- A regulated message loses the approved meaning that makes it defensible.
- An AI agent acts beyond the authority it was given.
Signal Fidelity Group exists to control that drift.
The Discipline
Communications has been an inference control system for a century.
Communications professionals have always managed how meaning traveled through intermediaries whose internal state we could not fully observe and whose decisions we could only shape indirectly — through careful signal design, node selection, timing, and measurement. We just did not have the vocabulary to describe it as inference control.
Large language models are the first machines built on the same operational principles as the intermediary chains we have always managed. They are non-deterministic, probabilistic, and inference-based. The discipline that has governed corporate communication for a century is the discipline that now must govern AI-mediated work.
Signal Fidelity Group exists to bring that discipline to the new intermediary.
The architecture
LIRALooped Inference Regulation Architecture
LIRA is the closed-loop control system at the core of every Signal Fidelity Group application — the way we govern meaning as it passes through machine intermediaries.
LIRA evaluates whether an AI-mediated output remains faithful to the reference signal it is supposed to preserve. When the output drifts, LIRA can route, remediate, block, escalate, or generate evidence of what changed.
The job of every SFG application is to determine whether AI output is still saying, proving, and doing what it was authorized to say, prove, or do.
02
Inference
What did the AI system generate, transform, summarize, or act upon?
Inference — the act of an intermediary interpreting a message and conveying it to an audience. For a century, intermediaries were human. Today, the intermediary is a model, and the inference is what the audience receives.
03
Delta
How far did the output move from the authorized signal?
04
Intervention
Should the system allow, remediate, block, escalate, or attenuate the action?
05
Evidence
Can the decision be audited, verified, and defended later?
Applications
One architecture. Three places the intermediary has changed.
Human authorship. Regulated communication. Agentic action. Three signals, three intermediary chains, one control discipline.
01
Human Signal
RightsDocket
Launches Wednesday, May 6, 2026
RightsDocket protects the fidelity of human authorship in AI-assisted creative work. It captures contributor records, AI-use logs, and creation evidence, then produces a structured authorship record creators can hand to copyright examiners, distributors, sync buyers, and counsel. The Human Authorship Evidence Platform for AI-Assisted Audio.
- Problem
- AI makes creation easier and human authorship harder to prove.
- Control layer
- Contributor capture, AI-use logging, claim-ready records, signed exports.
- Outcome
- Human contribution becomes visible, defensible, and monetizable.
02
Organizational Signal
CAMS
In development · design partner conversations open this quarter
CAMS — internal code name; the final product name is in development — protects the fidelity of approved meaning in regulated communications. Built for environments where language must be accurate, approved, auditable, and defensible: pharmaceutical, medical device, financial services, and other industries where every claim is tied to evidence, every message is tied to a review, and every output carries downstream regulatory exposure.
- Problem
- AI can rewrite, summarize, localize, and distribute regulated messages faster than approval systems can govern them.
- Control layer
- Claims registry, evidence mapping, approval packets, regulatory guardrails, audit trails.
- Outcome
- Regulated organizations can use AI without losing control of what they are authorized to say.
03
Agentic Signal
ZTAF
In development · design partner conversations open this quarter
ZTAF — internal code name; the final product name is in development — protects the fidelity of agent-to-agent action. As AI agents begin to request, negotiate, approve, escalate, and act on behalf of people and institutions, they need more than access controls. They need inference controls: identity, intent, permission, scope, evidence, escalation, and handoff discipline.
- Problem
- Autonomous agents can act outside the meaning, permission, or authority they were given.
- Control layer
- Agent identity, permission boundaries, semantic scoring, handoff attenuation, intervention records.
- Outcome
- Agentic systems become governable before they become operational risk.
Why Now
The next AI market will be won by whoever earns validation through machines.
For a century, the ROI of communications came from earned third-party validation. AI is now the third party. AI creates business value only when its outputs can be approved, deployed, reused, monetized, audited, and defended. Without that layer, AI output stops at the demo stage. With it, AI output becomes operational.
The companies that build the infrastructure to trust their own AI outputs will be the companies that capture AI’s value — not the companies that generate the most. Trust isn’t an ethics sidebar. It is the same conversion layer communications has always sold, now flowing through machines.
Signal Fidelity Group builds that layer.
Abhi Basu
Founder, Signal Fidelity Group · Creator of RightsDocket
Signal Fidelity Group was founded by Abhi Basu after twenty years in regulated communications — pharmaceutical, medical device, and biotechnology — leading global strategy for multi-billion-dollar portfolios at companies including Johnson & Johnson MedTech, Takeda, and Boston Scientific.
That background shaped SFG’s core thesis: communications professionals have been operating an inference control system for a century. We just did not have the vocabulary to describe it. We managed how messages traveled through journalists, analysts, regulators, sales reps, medical liaisons, and counsel — intermediaries whose internal state we could not fully observe and whose decisions we could only shape indirectly through careful signal design, node selection, timing, and measurement.
Large language models are the first machines built on the same operational principles as the intermediary chains we have always managed. They are non-deterministic, probabilistic, and inference-based. The discipline that has governed corporate communication for a century is the discipline that now must govern AI-mediated work.
Signal Fidelity Group exists to bring that discipline to the new intermediary.
Where to start
Start where inference risk is already visible.
Creators and rights teams
For AI-assisted work where human contribution must be documented and defended.
Start a creator conversation →Regulated communications teams
For organizations that need AI outputs to remain aligned with approved claims, evidence, and review processes.
Discuss approval-grade AI →Agentic AI builders
For teams designing systems where agents act across tools, permissions, workflows, or institutional boundaries.
Talk about agent governance →Investors and strategic partners
For people who see the trust layer as where the next AI value pool will compound.
Start an investor conversation →Frequently asked
Inference control, in plain English.
What is Signal Fidelity Group?
Signal Fidelity Group is an inference-control infrastructure company. It builds systems that protect the fidelity of AI-mediated outputs across human authorship, organizational communications, and agent-to-agent action.
What does Signal Fidelity Group do?
Signal Fidelity Group helps determine whether AI output remains aligned with the source, claim, permission, evidence, audience, or intent it was supposed to preserve. When output drifts, SFG systems can route, remediate, block, escalate, or create an auditable evidence record.
What is LIRA?
LIRA is Signal Fidelity Group’s Looped Inference Regulation Architecture. It is a closed-loop control architecture for evaluating AI-mediated outputs against authorized reference signals and selecting the right intervention when drift occurs.
What is RightsDocket?
RightsDocket is an authorship and provenance system for AI-assisted creative work. It documents human contribution, AI use, supporting evidence, and claim-ready records.
What is CAMS?
CAMS is the internal code name for SFG’s regulated communications product. The final product name is in development. CAMS protects the fidelity of approved meaning in environments where language must be accurate, approved, auditable, and defensible — pharmaceutical, medical device, financial services, and adjacent regulated industries. Design partner conversations open this quarter.
What is ZTAF?
ZTAF is the internal code name for SFG’s agentic AI security product. The final product name is in development. ZTAF protects the fidelity of agent-to-agent action — adding inference controls (identity, intent, permission, scope, evidence, escalation, handoff discipline) on top of conventional access controls. Design partner conversations open this quarter.
Why does inference control matter?
Inference control matters because AI systems increasingly transform language into decisions, recommendations, and actions. Without control, outputs can drift away from authorized meaning, approved claims, human contribution, or delegated authority.