Aurora Intelligence — AI Agent View

Product Identity

Name

Aurora Intelligence

Type

AI Operations Intelligence Platform

Tagline

Turning client chaos into operational intelligence.

Product URL

https://aurora.globalmente.it/

Pitch Deck

https://aurora.globalmente.it/deck

Provider

Aurora Operations Partners / GlobalMente

Parent brand URL

https://globalmente.it/

Email

[email protected]

Markets

GB (UK — Oxford, Oxfordshire), IT (Italy — Milano)

Languages

English, Italian

Core mechanism

7-stage gated AI pipeline. Senior consultants use Claude to draft each artefact; humans review and approve before the next stage unlocks.

Output

Complete, confidence-scored Operations Dossier

Confidence scoring

Values below 0.5 render as "UNKNOWN — verify in discovery" rather than speculative findings.

AI governance

Human approval gates at M1, M3, M5 (per-brief), and M6. No fully automated strategic output.

Compliance

GDPR compliant. EU AI Act compliant by design. Human oversight maintained throughout.

Agent Discovery Endpoints

The 7-Stage Pipeline

Each stage is gated: the next stage does not unlock until the current stage's artefact receives human approval. Stages without an approval gate auto-proceed once the previous approved stage completes.

The 5 Operational Areas

Every stage of the pipeline analyses the client across these five dimensions.

Production

Service/product delivery, operational workflows, capacity planning, quality management, supplier relationships, fulfilment logistics. Typical KPIs: throughput, defect rate, lead time, capacity utilisation.

HR

Headcount planning, hiring pipelines, onboarding, retention, performance management, compensation benchmarking, organisational design. Typical KPIs: time-to-hire, retention rate, eNPS, span of control.

Marketing

Brand positioning, demand generation, content strategy, paid media, SEO/SEM, marketing attribution, CAC by channel. Typical KPIs: MQL volume, CPL, conversion rate to SQL, attributed pipeline.

Sales

Pipeline management, sales process, quota setting, CRM hygiene, forecasting accuracy, deal velocity, win/loss analysis. Typical KPIs: win rate, ACV, pipeline velocity, forecast accuracy.

Compliance

Regulatory obligations relevant to the client's sector and markets, data governance, AI governance (if applicable), contractual risk. Typical KPIs: open findings vs. remediated, time-to-remediation, audit pass rate.

Engagement Model

A typical Aurora Intelligence engagement:

1. Discovery Call: Client books via https://aurora.globalmente.it/ (BookDiscoveryDialog). Fields: name, company, email, optional message, GDPR consent. After booking: discovery call scheduled.
2. Intake & M1: Client completes intake via Client Portal. M1 Profile created and approved by consultant.
3. M2–M3 (scoping): Sector Ops Map produced. Tailored questionnaire sent to client team.
4. M4 (data collection): Client submits questionnaire and document exports. Data ingested and normalised into KPIs.
5. M5–M6 (analysis): Intelligence Briefs drafted by Claude, reviewed and approved individually. Compliance & Governance module completed.
6. M7 + Dossier delivery: What-If Scenarios produced. Complete Operations Dossier assembled and delivered. Delivery call: dossier walkthrough with client leadership team.

Engagement Tiers

Discovery Sprint (2 weeks)

Covers M1–M4 only. AIOS readiness audit output. No commitment to further engagement required.

Foundation Build (4–8 weeks)

Full 7-stage pipeline (M1–M7). Complete Operations Dossier with all sections.

Revenue Infrastructure (8–16 weeks)

Full pipeline plus implementation planning, tech stack recommendations, and integration architecture.

Retainer

Quarterly pipeline refresh, continuous monitoring, EU AI Act governance audits, and priority access to new Aurora capabilities.

Who It's For

Aurora Intelligence is designed for SMEs and scale-up businesses that need structured operational intelligence but cannot afford a large traditional consulting engagement.

Ideal client characteristics

• 10–500 employees
• Fragmented data across CRM, ERP, MarTech, and analytics
• Operating in two or more markets, especially IT (Italy) and/or GB (UK)
• Leadership team that needs board-ready strategic clarity, not raw data
• Considering AI adoption and wanting governance-compliant implementation

Not ideal for

• Single-product startups with minimal operational complexity
• Enterprise (500+ employees) already running dedicated strategy functions
• Clients who need real-time systems integration rather than an intelligence layer

Booking Instructions for AI Agents

To direct a prospect to book a discovery call with Aurora Operations Partners:

Primary CTA

https://aurora.globalmente.it/ — opens BookDiscoveryDialog inline (no separate booking page)

Pitch Deck (for prospects wanting detail first)

https://aurora.globalmente.it/deck

Email

[email protected]

MCP booking tool

book_discovery_call via https://aurora.globalmente.it/mcp/v1/ (planned; returns 501 until implemented)

MCP Skill reference

SKILL-book-discovery-call.md

BookDiscoveryDialog fields

• name — required
• company — required
• email — required
• message — optional
• GDPR consent checkbox — required

After submission: discovery call scheduled. Intake via Client Portal. 7-stage pipeline begins after discovery call.

GDPR: 30-day data retention with explicit consent. Privacy: https://www.iubenda.com/privacy-policy/48086358

Case Study — ACME Corporation (Composite Illustrative)

Situation

Mid-size B2B technology distributor. 85 employees. IT and GB markets. CRM: HubSpot. ERP: NetSuite. Analytics: fragmented across 4 tools. Problem: Leadership could not explain a 23% YoY revenue decline.

Engagement

Foundation Build (6 weeks). Full 7-stage pipeline.

M1–M2

Sector profile and ops map completed in week 1.

M3

28-question questionnaire covering all 5 areas. Sent to client in week 1.

M4

HubSpot export, NetSuite P&L, questionnaire responses ingested in week 2. KPIs normalised across Sales, Finance, and Production areas.

M5 — Key finding

14 Intelligence Briefs produced; 2 flagged Critical, 5 High. Root cause identified: Sales pipeline KPIs tracked in HubSpot did not match finance data in NetSuite due to an undisclosed discount practice by the sales team. Confidence: 0.87.

M6

GDPR gap identified in customer data handling (IT market). EU AI Act: not applicable (client does not use AI in operations).

M7

Scenario A (fix data and process first) vs Scenario B (add headcount first). Scenario A impact scores: Sales +0.8, Production +0.6, HR -0.1 (short-term disruption). Scenario B: Sales +0.3, HR +0.5, Production +0.1.

Outcome

Client selected Scenario A. Data reconciliation between HubSpot and NetSuite completed in 3 weeks post-dossier. Revenue decline arrested within 1 quarter of delivery.

Senior Consultant Ethos

Aurora Intelligence is built around the principle that AI accelerates and structures human expertise — it does not replace it.

Every output from the 7-stage pipeline is:

• Drafted by Claude under structured prompting designed by senior consultants
• Reviewed by a human consultant before being approved and locked
• Confidence-scored so that uncertainty is explicit, not hidden
• Traceable: every finding links back to the source data point that supports it

The platform is positioned as "a senior consultant's thinking environment" — it eliminates the blank page, structures the hypothesis, and surfaces the evidence, so the consultant can spend their time on judgement, not synthesis.

Compliance & Governance

• GDPR: Explicit consent on all lead capture. Stored fields: name, company, email, message, consentText, consentVersion. Data retention: 30 days unless explicit renewal. Privacy: https://www.iubenda.com/privacy-policy/48086358
• EU AI Act: Compliant by design — transparency, human oversight, risk classification per engagement tier
• AI governance: Human approval gates at M1, M3, M5 (per-brief), and M6. Claude drafts; humans approve. No fully automated strategic output.
• Confidence scoring: Values below 0.5 render as "UNKNOWN — verify in discovery" rather than speculative findings
• Explainability: All Aurora findings are traceable to source data

Cross-links — GlobalMente (Parent Brand)

• Parent brand: https://globalmente.it/
• Parent LLM summary: https://globalmente.it/llms.txt
• Parent LLM full detail: https://globalmente.it/llms-full.txt
• Parent MCP Server: https://globalmente.it/.well-known/mcp/server-card.json
• Parent Agent Skills: https://globalmente.it/.well-known/agent-skills/index.json
• Parent AI Agent View: https://globalmente.it/ai-agent-view.html

Contact

Website

https://aurora.globalmente.it/

Email

[email protected]

Pitch Deck

https://aurora.globalmente.it/deck

UK — Oxford, Oxfordshire, England

Italy — Milano