May 2026

Applied AI, governed, secure and measurable.

We turn AI into useful, controlled work systems. Functional pilots in 6–8 weeks, with governance from day one.

IA · hilo

●Anthropic lanza Claude for Small Business con conectores QuickBooks, HubSpot, CanvaAnthropic/●KPMG integra Claude en el corazón de su negocio global de consultoría y auditoríaAnthropic/●PwC expande su alianza con Anthropic — Claude entra en deals y funciones enterpriseAnthropic/●Code with Claude: managed agents, Remote Agents y CI auto-fix shippingInfoQ/●Anthropic dona MCP a la Agentic AI Foundation bajo la Linux FoundationAnthropic/●Google lanza Gemini 3.5, agent Spark y world model Omni — Ultra baja a $200/mesCNBC/●OpenAI lanza GPT-Realtime-2: voz en vivo con reasoning GPT-5 y 70+ idiomasTechCrunch/●Microsoft 365 Copilot: agentes autónomos en Outlook y human agency en M365Microsoft/●Meta debuta Muse Spark con Alexandr Wang al frente de Superintelligence LabsCNBC/●DeepSeek V4 open-source: 1.6T parámetros, 1M context, 1/20 del coste de Opus 4.7CNBC/●Comisión Europea publica draft de transparencia bajo Artículo 50 del AI ActEU Commission/●Gobierno híbrido de IA, crítico en la banca europea bajo el AI ActSiliconANGLE/●Anthropic lanza Claude for Small Business con conectores QuickBooks, HubSpot, CanvaAnthropic/●KPMG integra Claude en el corazón de su negocio global de consultoría y auditoríaAnthropic/●PwC expande su alianza con Anthropic — Claude entra en deals y funciones enterpriseAnthropic/●Code with Claude: managed agents, Remote Agents y CI auto-fix shippingInfoQ/●Anthropic dona MCP a la Agentic AI Foundation bajo la Linux FoundationAnthropic/●Google lanza Gemini 3.5, agent Spark y world model Omni — Ultra baja a $200/mesCNBC/●OpenAI lanza GPT-Realtime-2: voz en vivo con reasoning GPT-5 y 70+ idiomasTechCrunch/●Microsoft 365 Copilot: agentes autónomos en Outlook y human agency en M365Microsoft/●Meta debuta Muse Spark con Alexandr Wang al frente de Superintelligence LabsCNBC/●DeepSeek V4 open-source: 1.6T parámetros, 1M context, 1/20 del coste de Opus 4.7CNBC/●Comisión Europea publica draft de transparencia bajo Artículo 50 del AI ActEU Commission/●Gobierno híbrido de IA, crítico en la banca europea bajo el AI ActSiliconANGLE/

6–8

weeks to pilot

governance layers

deployment models

active sectors

## WHAT_WE_DO

Four layers to put AI into production with control.

layer.01

AI Ecosystem

Architectures adapted to data criticality, privacy and technology stack.

→ adapt(data, stack, speed)

layer.02

Governed foundation

Authorised sources, documents, APIs, embeddings, permissions and versioning.

→ govern(sources, perms)

layer.03

Agents and workflows

Functional assistants integrated into real processes with human escalation.

→ deploy(agent, process)

layer.04

aiBoost and measurement

Continuous improvement, retraining, fine-tuning, training and non-obsolescence.

→ measure(kpis).iterate()

## DEPLOYMENT

One criterion: deploy AI where the data can be governed.

Three ways to deploy AI, based on the level of control each process requires. We don't sell a single stack: we choose the environment based on data sensitivity, traceability, integration and speed.

Controlled cloud AI

For fast validation with non-sensitive or anonymised data, with scoped sources, permissions and measurement from the start.

Traceability: Medium
Example stack: Claude API, OpenAI, Gemini
Data isolation: Shared
Startup speed: High
Initial cost: Low
Proprietary audit: —
Corporate integration: Limited

Managed private AI

For critical processes with confidential data, isolated or dedicated infrastructure, proprietary audit and controlled operation.

Traceability: Full
Example stack: Dedicated Azure OpenAI, Bedrock, vLLM on VPC
Data isolation: Dedicated
Startup speed: Medium
Initial cost: Medium
Proprietary audit: ✓
Corporate integration: Optional

AI in the client's environment

For organisations that already govern M365, Azure, VPC or on-premise and want to integrate AI into their security, identity and compliance stack.

Traceability: Full
Example stack: M365 Copilot, client Azure tenant, on-prem
Data isolation: Own
Startup speed: Medium
Initial cost: Variable
Proprietary audit: ✓
Corporate integration: Native to client stack

Controlled cloud AI

For fast validation with non-sensitive or anonymised data, with scoped sources, permissions and measurement from the start.

Managed private AI

For critical processes with confidential data, isolated or dedicated infrastructure, proprietary audit and controlled operation.

AI in the client's environment

For organisations that already govern M365, Azure, VPC or on-premise and want to integrate AI into their security, identity and compliance stack.

Traceability

Medium

Full

Example stack

Claude API, OpenAI, Gemini

Dedicated Azure OpenAI, Bedrock, vLLM on VPC

M365 Copilot, client Azure tenant, on-prem

Data isolation

Shared

Dedicated

Own

Startup speed

High

Medium

Initial cost

Low

Medium

Variable

Proprietary audit

—

✓

Corporate integration

Limited

Optional

Native to client stack

## PROCESS

Start small, analyse results and grow with intent.

Listen

Map the process, users, friction points and available sources.

Week 1

Map the process, users, friction points and available sources. · Week 1

Prioritise

Assess value, risk, data sensitivity and pilot feasibility.

Week 1–2

Assess value, risk, data sensitivity and pilot feasibility. · Week 1–2

Build

Controlled pilot with defined sources, permissions and metrics.

Week 2–5

Controlled pilot with defined sources, permissions and metrics. · Week 2–5

Validate

Test with real users and measure quality, efficiency and adoption.

Week 5–7

Test with real users and measure quality, efficiency and adoption. · Week 5–7

Decide

Scale, adjust or discard with evidence.

Week 7–8

Scale, adjust or discard with evidence. · Week 7–8

## CASES

Where there is operational risk, critical documentation and a need for evidence.

Front-Back Office Reconciliation

● BankingMiddle/Back Officeproduction

Context: Trading and accounting don't close with the same amounts — duplicates or completeness issues.
AI: Compares transactions, detects inconsistencies and generates review alerts.
Impact: Less manual review, better daily close, discrepancy traceability.

STP Front → Iberclear

● BankingPost-Tradeproduction

Context: Transactions arrive from Murex, Excel or heterogeneous flat files.
AI: Interprets the source, normalises fields and generates ARCO XML ready to load.
Impact: Less manual handling, fewer errors, greater traceability.

Valuation and technical sheets

● Real EstateValuationMVP

Context: Preliminary assessment based on photos, attributes and internal documentation.
AI: Computer-vision agent that analyses images and produces standardised sheets.
Impact: Shorter analysis time, greater consistency, traceable documentation.

Emotional support

● HealthConversationalproduction

Context: Accessible, empathetic channel for 24/7 emotional containment.
AI: Conversational assistant as a digital emotional companion.
Impact: MVP in 6 weeks, high acceptance, foundation for clinical integration.

Support & Coding SuperAgent

● IT & Capital MarketsSuperAgentproduction

Context: Scale support and impact analysis without adding headcount.
AI: Specialised assistants on IPaaS with RAG and audit.
Impact: Reduced support times, better quality, foundation for new agents.

aiTalento · selection and assessment

● HR & TalentaiTalentoproduction

Context: High-volume CV selection with inconsistent criteria.
AI: Normalises CVs, identifies competencies and prioritises candidates.
Impact: Shorter screening time, more focus on high-value interviews.

## WHY_AIROSS

Functional pilots in 6–8 weeks, with governance from day one.

01 — Governance

Full traceability

Sources, permissions, roles, ownership and decision log from day zero.

02 — Security

Right environment

Deployment model by data sensitivity. Explicit human control.

03 — Measurement

Defined KPIs

Efficiency, quality, adoption, impact and risk — measured before and after.

04 — Delivery

Decide with evidence

Concrete pilots with real users. No metrics theatre.

## STAY_UP_TO_DATE

Notes and articles on applied AI.

→ see all

Banca18 may 20261 min read

Gobernanza de IA en banca: cinco preguntas que un comité debería responder antes de aprobar un piloto

Antes de mover una sola operación a producción, un comité de IA en banca tiene que dejar respuestas escritas a cinco preguntas. No son técnicas: son de responsabilidad.

→ read

Medición12 may 20261 min read

Cómo medir el ROI real de un agente corporativo (sin caer en el teatro de las métricas)

El 80% de los pilotos de IA que vemos miden tiempo ahorrado. Es la métrica menos útil. Hay tres dimensiones que sí explican si un agente paga su despliegue.

→ read

Arquitectura5 may 20262 min read

On-premise, privado o cloud fundacional: el criterio no es técnico, es de confianza

Tres modelos de despliegue, un mismo criterio: sensibilidad del dato, necesidad de trazabilidad, stack corporativo y velocidad de adopción. Cómo elegimos en cada caso.

→ read

## LET'S_TALK

Let's talk about your case — or ask airosso directly.

## Discovery session

30 minutes to map a secure, measurable pilot.

We listen, identify the candidate case, assess data sensitivity and put metrics, scope and deployment model in writing — before touching a single line of code.

No commitment, in English or Spanish
Output: 1 candidate pilot + measurement criteria
Attended by an aiRoss partner, not a salesperson

→ discovery@aiross.ai ↗ LinkedIn

airosso● Live demo

● airosso

Hi, I'm airosso — the aiRoss assistant.

How can I help? You can ask me about our active openings, cases, sectors, or how to start a pilot.

If you want to apply for a position, drag your CV (PDF or Word) into the chat and I'll guide you.