# Report: Visor.ai > GAMMA-Ray v0.0.155 (beta, for review; every rating is experimental). The GAMMA-Ray beta report on Visor.ai: Visor.ai AI agents (voice and chat) A3 · R2 · C1 · O2; 66 provisions lit, 11 sources captured. > Page: https://gamma-ray.sgit.ai/reports/visor-ai/ · Source: data/reports/visor-ai.json GAMMA-Ray report · Spectrum 0.2 beta · Public tier · read 2026-11-10 · not reviewed by the company yet Visor.ai sells a platform on which organisations build and run AI agents for customer service by voice and chat, plus an agent that scores call recordings. 1 solution carded of 4 found 11 sources captured and hashed 66 provisions lit in 8 of 8 instruments 5 examples of the agent at work, with sources Built only from Visor.ai's own public pages and, for company basics only, the news sources named beside each fact. Visor.ai has not seen this report. Nothing was tested and no credentials were used. If you are Visor.ai and something here is wrong or missing, the correction will be made and dated, and you can claim the card to add evidence. ## The agentic solutions One card per solution. Each level is computed from the evidence by a [published rule](../../spectrum/evidence-map/index.md); a dimension with nothing public reads "not yet evidenced", which is not a no. A card rates a product as documented, not anyone's deployment of it. ### Visor.ai AI agents (voice and chat) AI agents that answer customer phone calls, built as flows on the platform, with test suites, live supervision and EU AI Act checks. Visor.ai AI agents a product, as documented SPECTRUM 0.2 BETA · Public tier · 2026-11-10 A3 Autonomy Acts π Moderate · S1·R5·A1 ×3 R2 Reach Integrated π Moderate · S1·R5·A1 ×3 C1 Control Stated with options on · as shipped not yet evidenced π Moderate · S1·R5·A1 ×3 O2 Openness Documented with options on · as shipped not yet evidenced π Moderate · S1·R5·A1 ×3 Draft Stage: Building (8 of 16) In production Standout. Autonomy 3 (Acts): "those requests now run end to end as automated workflows" Next unlock. Openness 3 (Measured): public evidence that test or evaluation results are published would get there. Trust. FACT-Ray : 18 facts: 18 π Moderate · found by an agent, captured and hashed, checked by 2 other agents (same model family, shared repository); no human has signed it. Standards constellation. 66 provisions lit across 8 of 8 instruments. Salesforce EAAMM OWASP MCP AAGMM Microsoft NIST AI Act Evidence: 7 sources, captured and hashed · the card's page has the vault's read key · Not reviewed by its vendor yet · correct or claim this card [The full card, every answer and its evidence](../../spectrum/cards/visor-ai-agents/index.md) · [the governance layer](../../scans/visor-ai-agents/index.md) ### Chat AI Agent AI agents on WhatsApp, websites and messaging channels that answer from a synced knowledge base and hand over to live chat. *No card yet (generally available): this solution has not been assessed.* ### Nexa The platform's built-in agent that helps build, test and review agents, and proposes fixes that a person accepts. *No card yet (live (announced as new on the home page)): this solution has not been assessed.* ### Quality & Insights AI Agent Scores call recordings against a customer's own scorecard and turns the scores into coaching and contact-reason reports. *No card yet (generally available): this solution has not been assessed.* ## Our reading **The shape.** Visor.ai's AI agents read A3 · R2 · C1 · O2 (Building). A customer builds each agent as a flow on a visual canvas, with the model and speech services set on each node. The voice agent connects to telephony and to the customer's contact-centre platform; the chat agent answers from a knowledge base built from the customer's own documents and kept in sync with them. Nexa, the platform's own agent, reads real conversations on a schedule and proposes fixes. **Where the evidence is strongest.** A named customer shows the agent acting. At Generali Tranquilidade, common requests such as duplicate documents, address changes, adding a vehicle and cancellations run end to end as automated workflows through web services into the insurer's systems, and an advisor picks up in live chat when a person is needed. Visor.ai's customer stories give the figures: 80,000 broker conversations a month with the agent at Generali, and Max at Millennium BCP starting at 65% automation in 2021, with a goal of 80%. They are the customers' figures as Visor.ai publishes them. **What holds as shipped, and what with options on.** Acts and Integrated rest on the product pages and the Generali deployment. Control 1 holds only with the Issue Agent on: the customer writes in plain language what the chat agent must not handle, and matching conversations are flagged for a person. Openness 2 rests on a reliability note saying the models can occasionally fail, the knowledge base, and the security page's list of AI providers (Azure, Vertex AI Gemini, Deepgram, AssemblyAI, ElevenLabs). Its first step, a written statement of what the agents are for, is stated for healthcare deployments, so Openness is marked available too; a purpose stated for every deployment would make it hold as shipped. **What would move it.** Autonomy 4: a page saying the agent works on its own and a person approves only critical decisions. Reach 3: a published agent interface, such as an MCP server or an agent card, with documented authorisation narrower than the whole account. Control 2: a page saying which high-impact actions of the customer-facing agent need a person's approval, with a list of what it can reach. Openness 3: published evaluation results; the llms.txt at the site root already gives the machine-readable half of the rule. **Seen, and not counted.** The challenger lowered several credits a reader may meet on the site. Nexa's approval of the changes it proposes governs edits to an agent, not the actions the customer-facing agent takes. Take moves one live call to a person, which is escalation, not a way to pause or stop the agent. Watchtower monitoring and Nexa's alerts are shown with sample data, not in operation, and the Super Agent routes requests to specialised agents rather than coordinating them. Headline figures such as an automation rate of up to 85% are listed as announced. ## What would move it The one piece of public evidence that would lift each card next: - **Visor.ai AI agents (voice and chat)**: Openness 3 (Measured). Public evidence that test or evaluation results are published. ## The agent at work Examples from public pages that name a customer or give figures. Each source was captured and hashed on the date shown. ### Generali Tranquilidade Chat AI agent for customers and brokers; common requests (duplicate documents, address changes, adding a vehicle, cancellations) run end to end through web services; 80,000 broker conversations a month. "conversations between brokers and the AI agent every month" [Source](https://www.visor.ai/success-stories/generali/) (captured 2026-11-10T18:45Z, sha256 `5ad19f4d69ad64f9`) ### Generali Tranquilidade Requests run as automated workflows connected to the insurer's systems. "those requests now run end to end as automated workflows" [Source](https://www.visor.ai/success-stories/generali/) (captured 2026-11-10T18:45Z, sha256 `5ad19f4d69ad64f9`) ### Millennium BCP Max, a Chat AI Agent for the bank's sales network of more than 5,000 people, with live-chat handover to advisors. "Max, an AI agent built on Visor" [Source](https://www.visor.ai/success-stories/millennium-bcp/) (captured 2026-11-10T18:45Z, sha256 `bf836623286299e0`) ### Millennium BCP Automation of Max's questions started at 65% in 2021, with a goal of 80%. "Max started at 65% automation in 2021, and the goal is 80%" [Source](https://www.visor.ai/success-stories/millennium-bcp/) (captured 2026-11-10T18:45Z, sha256 `bf836623286299e0`) ### Farmácia Sá da Bandeira Home page figure of 77% interaction success rate attributed to this customer. "Interaction Success Rate" [Source](https://www.visor.ai/) (captured 2026-11-10T10:41Z, sha256 `03497c0c550e4bc7`) ## The company - Site footer names Portugal as headquarters "Portugal – Headquarters" [Source](https://www.visor.ai/) (captured 2026-11-10T10:41Z, sha256 `03497c0c550e4bc7`) - Started in 2016 "Visor.ai began its journey in 2016" [Source](https://www.visor.ai/company/) (captured 2026-11-10T10:41Z, sha256 `f4592014a8d289e8`) - Describes itself as an agentic AI platform across voice, chat and quality "Today, Visor.ai is an agentic AI platform" [Source](https://www.visor.ai/company/) (captured 2026-11-10T10:41Z, sha256 `f4592014a8d289e8`) - Offices listed include Lisbon, Porto, Madrid and Barcelona "Global Presence, Local Expertise" [Source](https://www.visor.ai/company/) (captured 2026-11-10T10:41Z, sha256 `f4592014a8d289e8`) - Legal entity named in the privacy policy "Legal Name PT:** Visor.ai, Portugal S.A." [Source](https://www.visor.ai/privacy-policy/) (captured 2026-11-10T10:41Z, sha256 `0055c5fcebc30d78`) - States ISO/IEC 27001:2022 certification; certificate available on request "Visor is certified to ISO/IEC 27001:2022" [Source](https://www.visor.ai/security/) (captured 2026-11-10T18:45Z, sha256 `f337c67022110924`) - Lists ten subprocessors, all in the EU "Ten subprocessors handle data for Visor. All ten are in the EU." [Source](https://www.visor.ai/security/) (captured 2026-11-10T18:45Z, sha256 `f337c67022110924`) - Partner in the PRR-funded Center for Responsible AI agenda led by Unbabel "LÍDER DO CONSÓRCIO | Unbabel, Lda." [Source](https://www.visor.ai/pt/prr/) (captured 2026-11-10T10:41Z, sha256 `52a248dec2398acc`) ## The people Public and professional only, from the company's own pages or a public professional source. - **Gonçalo Consiglieri**, Co-founder & CEO. [Source](https://www.visor.ai/company/) - **Bruno Matias**, Co-founder & CTO. [Source](https://www.visor.ai/company/) - **Afonso Fonseca**, Engineering Director. [Source](https://www.visor.ai/company/) - **Rita Anjo**, Customer Success Director. [Source](https://www.visor.ai/company/) ## The graph The company, its people and the stories on the left; each solution, the captured sources behind its card, the indicators those sources evidence and the provisions of the instruments those indicators touch, to the right. "Touched" never means "complies". people and stories company solutions 7 sources 18 indicators 66 provisions, by instrument I03: Generali's own team builds workflows and automated services on the platform without a developer, connected to the insurer's systems through web services. I08: At Generali, the platform was connected to the insurer's systems through web services, so four common requests (duplicate documents, address changes, adding a vehicle, cancellations) run end to end as automated workflows. I10: The voice agent connects to telephony and to the customer's contact-centre or CCaaS platform. I11: Generali connected the platform to its own systems through web services. I17: At Generali the chat agent handles conversations from start to finish; when a conversation needs a person, an advisor picks it up in live chat. I20: For healthcare, the agents' scope is administrative contact, written down before work starts. I22: A reliability note states that the models can occasionally fail and that providers do not guarantee total availability. I36: Each agent runs twelve automatic checks against the EU AI Act and the GDPR, each citing its article. I37: In Visor's deployment, the agent is tested end to end and validated by the customer's team in real conditions before launch. I40: A chat that needs a person moves to live chat and is routed to the right department with its history. I47: For healthcare, the page states as a rule that the agents do not give clinical advice and are set up to pass a clinical question to a person. I58: The chat agent answers from a knowledge base built from the customer's documents and kept in sync with them. I61: The security page lists the AI providers in the voice and chat path (Azure, Vertex AI Gemini, Deepgram, AssemblyAI, ElevenLabs) and what each processes. I66: In Visor's deployment, Visor trains the customer's people to run the platform. I67: The Visor success story states that Max started at 65% automation in 2021 and that the goal is 80% of questions and processes resolved without a person; only the 65% is a measured figure, and the page markup attributes the story to an archived copy of 2022-08-20. I68: Nexa reads real conversations on a schedule, spots patterns and proposes fixes. S1: Generali Tranquilidade is named as a customer; the page gives 80,000 conversations a month between brokers and the AI agent, an undated figure that the page markup attributes to an archived copy of 2022-08-20. S3: An llms.txt file resolves at the site root and indexes the product pages with markdown twins. Visor.ai Gonçalo Consiglieri Bruno Matias Afonso Fonseca Rita Anjo Generali Tranquilidade Generali Tranquilidade Millennium BCP Millennium BCP Farmácia Sá da Bandeira Visor.ai AI agents (voice and chat) Voice and chat agents A3 · R2 · C1 · O2 Chat AI Agent Chat AI Agent Nexa Nexa Quality & Insights AI Agent Quality & Insights AI Agent visor.ai/success-stories/generali visor.ai/voice-agent visor.ai/healthcare visor.ai/chat-agent visor.ai/security visor.ai/success-stories/millenni… visor.ai/llms.txt It is configured on a low-code or no-code builder and acts on real organisational data I03 It executes actions itself I08 It works across several domains or systems I10 It connects to external tools or services across a trust boundary I11 The agent handles routine tasks; the person is consulted on exceptions I17 Its purpose and permitted tasks are stated in writing I20 Its limits and known failure cases are documented for those who use it I22 The laws that apply to it are identified I36 New agents pass an approval or review before they run I37 There is a defined escalation path I40 A limit on it is a rule in prose I47 The data it uses is described I58 The models and third-party components it uses are listed I61 The people who work with it are trained I66 Its value is measured I67 It is improved on a regular cycle I68 It is in production: a public source names a customer or gives usage figures for what it does on its own S1 It is machine-readable for agents: it publishes an llms.txt, an AGENTS.md, an API description or an agent card S3 Salesforce 13 EAAMM 4 OWASP 7 MCP authorization 2 AAGMM 3 Microsoft 18 NIST AI RMF 17 EU AI Act 2 OWASP, AT3 Citizen Developer Agent Salesforce, Level 2: Simple Orchestration, Single Domain Salesforce, Level 1, how to advance from Level 1 to Level 2 Microsoft, 200: Repeatable, Business strategy Salesforce, Level 3: Complex Orchestration, Multiple Domain Salesforce, Level 2, how to advance from Level 2 to Level 3 Salesforce, Level 3, how to advance from Level 3 to Level 4 Microsoft, 400: Capable, Business strategy Microsoft, 200: Repeatable, Technology and data OWASP, AT6 Externally Extended Agent NIST AI RMF, GOVERN 6.1 NIST AI RMF, MAP 4.1 EAAMM, L3 Consultative (survey item Q9) NIST AI RMF, MAP 1.1 NIST AI RMF, MAP 2.1 NIST AI RMF, MAP 3.3 NIST AI RMF, MAP 1.3 EU AI Act, Article 13 NIST AI RMF, MAP 2.2 NIST AI RMF, MEASURE 2.5 NIST AI RMF, GOVERN 1.1 OWASP, Maturity Level 4 Adaptive, Self-Regulating Governance, key enterprise action Microsoft, 100: Initial, AI governance and security OWASP, Maturity Level 1 Experimentation without Guardrails, key enterprise action Microsoft, 400: Capable, AI governance and security NIST AI RMF, MANAGE 1.1 EAAMM, Prerequisite, L2 to L3 NIST AI RMF, GOVERN 3.2 AAGMM, GD-06 OWASP, Maturity Level 0 Unaware and Ad Hoc, key enterprise action AAGMM, GD-05 EAAMM, Readiness dimension 2 Salesforce, Level 0, how companies can advance from Level 0 to Level 1 Salesforce, Level 1, how to advance from Level 1 to Level 2 OWASP, Maturity Level 2 Policy-Defined, Human-in-the-Loop, key enterprise action NIST AI RMF, GOVERN 2.2 EU AI Act, Article 4 EAAMM, Readiness dimension 3 Microsoft, 100: Initial, Organization and culture Microsoft, 200: Repeatable, Organization and culture Microsoft, 300: Defined, Technology and data Microsoft, 300: Defined, Organization and culture NIST AI RMF, MAP 3.4 Salesforce, Level 0, how companies can advance from Level 0 to Level 1 Salesforce, Level 0, how companies can advance from Level 0 to Level 1 Salesforce, Level 1, how to advance from Level 1 to Level 2 Salesforce, Level 3, how to advance from Level 3 to Level 4 Salesforce, Level 4, how to maximize value Salesforce, Level 4, how to maximize value Microsoft, 200: Repeatable, Business strategy Microsoft, 300: Defined, AI strategy and experience Microsoft, 300: Defined, Business strategy Microsoft, 400: Capable, Business strategy Microsoft, 400: Capable, AI strategy and experience NIST AI RMF, MAP 1.4 NIST AI RMF, MAP 3.1 Microsoft, 100: Initial, Organization and culture AAGMM, GD-12 Microsoft, 400: Capable, AI governance and security Microsoft, 500: Efficient, Business strategy Microsoft, 500: Efficient, AI strategy and experience NIST AI RMF, MANAGE 4.2 OWASP, Maturity Level 1 Experimentation without Guardrails, key enterprise action NIST AI RMF, MEASURE 4.3 MCP authorization, Authorization (draft) MCP authorization, Authorization (draft) ## The evidence | Source | Read | SHA-256 (first 16) | |---|---|---| | [www.visor.ai/](https://www.visor.ai/) | 2026-11-10T10:41Z | `03497c0c550e4bc7` | | [www.visor.ai/chat-agent/](https://www.visor.ai/chat-agent/) | 2026-11-10T18:45Z | `25d94e3335f5dc6e` | | [www.visor.ai/company/](https://www.visor.ai/company/) | 2026-11-10T10:41Z | `f4592014a8d289e8` | | [www.visor.ai/healthcare/](https://www.visor.ai/healthcare/) | 2026-11-10T18:45Z | `bea305021fe6b3ab` | | [www.visor.ai/llms.txt](https://www.visor.ai/llms.txt) | 2026-11-10T18:45Z | `35a955f100a9cf45` | | [www.visor.ai/privacy-policy/](https://www.visor.ai/privacy-policy/) | 2026-11-10T10:41Z | `0055c5fcebc30d78` | | [www.visor.ai/pt/prr/](https://www.visor.ai/pt/prr/) | 2026-11-10T10:41Z | `52a248dec2398acc` | | [www.visor.ai/security/](https://www.visor.ai/security/) | 2026-11-10T18:45Z | `f337c67022110924` | | [www.visor.ai/success-stories/generali/](https://www.visor.ai/success-stories/generali/) | 2026-11-10T18:45Z | `5ad19f4d69ad64f9` | | [www.visor.ai/success-stories/millennium-bcp/](https://www.visor.ai/success-stories/millennium-bcp/) | 2026-11-10T18:45Z | `bf836623286299e0` | | [www.visor.ai/voice-agent/](https://www.visor.ai/voice-agent/) | 2026-11-10T18:45Z | `c641b9f6a84c7399` | ## How this report was made A researcher-assessor gathered and captured the sources and mapped the evidence to indicators, without assigning a level. A challenger argued against every level three and four and every doubtful credit. The [deterministic scorer](../../spectrum/pipeline/index.md) computed the levels and the gate checked the rules. A card appears here only after two rounds of review and a sign-off by agents that did not write it (the team's workflow); Visor.ai AI agents (voice and chat): reviewed in 4 rounds, signed off by gamma.approve@web-summit.dinis on 2026-11-10. A person's sign-off of the series is still to come. | Role | Who | |---|---| | researcher-assessor | a sub-agent of gamma.studio@web-summit.dinis | | challenger | a sub-agent | | scorer | build/spectrum.mjs | | analyst, designer, writer | gamma.studio@web-summit.dinis | | review | gamma.review@web-summit.dinis, 4 rounds | | sign-off | gamma.approve@web-summit.dinis, 2026-11-10 | ## The vault Every capture, the dossier, both assessors' mappings, the challenger's notes and this report's data are in a public sgit vault (`w4byv6dr`). Read key: sgit_public_read_90a76763f617c725a429b7b445125e43a01bf7f880c08904b7803018bc65935b:w4byv6dr With [sgit](https://sgit.ai/): `sgit clone` followed by the key. The key reads; it cannot write. ## Corrections None yet. A correction is made in place, dated here, and carried to the card, the vault and the article. ## The article [Visor.ai agents on the GAMMA-Ray Spectrum: A3 · R2 · C1 · O2](article.md), written from this report for PrompTugal.news.