Data isolation

Your data is stored in a logically isolated Alfred environment.

Your campaign history, performance signals, customer data, decisions, outcomes, and derived intelligence are separated from every other customer’s data. No other customer can access, query, train on, benchmark against, or benefit from your data

Your organization’s data belongs to your instance alone.

Private pattern library

Alfred builds a private pattern library for your organization, a living memory of your marketing history, decisions, signals, and outcomes.

The pattern library captures how your creatives perform, which channels scale, which audiences respond, how seasonality affects demand, and how budget decisions translate into growth.

This intelligence is used only to improve analysis, recommendations, and decision support for your team. It is not shared across customers, exposed to competitors, or converted into generic playbooks.

No cross-customer training

Your data does not train shared models, shared recommendation systems, shared benchmarks, or shared playbooks.

Insights, patterns, signals, or outcomes derived from your data are never used to improve Alfred’s performance for any other customer.

Alfred becomes smarter for your business. It does not become smarter for your competitors.

Foundational model usage

We use API access to foundational AI models as an infrastructure for inference.

Data processed through providers such as Open source LLMs, Anthropic or other enterprise-grade model providers is not used to train their models under their enterprise terms.

The more important point is this: your organizational pattern library, the intelligence that makes Alfred valuable to your business — stays isolated to your Alfred instance. It does not leave your environment and it is never used to improve recommendations, benchmarks, or intelligence for another customer.

No third-party data sharing for training

Your data is not sold, licensed, shared, or made accessible to third parties for training, benchmarking, resale, or commercial intelligence purposes.

This includes foundational model providers, analytics vendors, and external partners.

Where sub processors are required to operate the product, they are contractually bound to process data only for the services they provide and not for their own model training, resale, or commercial use.

No commercialization of customer intelligence

Your data is not our product. Your subscription is our product.

We do not sell, publish, license, or commercially monetize your raw data, derived intelligence, campaign patterns, performance signals, or private pattern library.

We will not use your data in benchmarks, case studies, sales material, third-party reports, or category insights unless you explicitly approve it in writing.

Human access controls

Human access to customer data is restricted, role-based, logged, and limited to approved support, security, or operational needs.

Alfred employees do not browse, inspect, or reuse your data for sales, benchmarking, product learning, or competitive intelligence.

Where support access is required, it is limited to the specific issue being resolved.

Data deletion on request

Upon termination of the agreement, your data can be deleted within a defined contractual timeframe.

This includes raw data, derived intelligence, embeddings, stored outputs, and your private pattern library, subject to agreed retention and backup policies.

Written confirmation of deletion can be provided once the deletion process is complete.

Contractual protection

These commitments can be reflected in your agreement, including:

• Data ownership

• Data isolation

• No cross-customer training

• No third-party training use

• Subprocessor controls

• Human access restrictions

• Retention and deletion timelines

• Written deletion confirmation

• Restrictions on benchmarking, resale, and commercial use

FAQs

1. Will you read our data?

   Alfred does not use your data to train shared models or improve intelligence for other customers.

   Instead, Alfred builds a private pattern library for your organization — a living memory of your marketing history, decisions, signals, and outcomes. It learns what works for your brand: which creatives perform, which channels scale, which audiences respond, how seasonality affects demand, and how budget decisions translate into growth.

   This pattern library belongs to your instance alone. It is not shared across customers, exposed to foundational model providers, used for benchmarks, or converted into recommendations for competitors.

   Alfred becomes smarter for your business. It does not become smarter for anyone else’s.

   Human access is restricted, role-based, logged, and limited to approved support, security, or operational needs. Your data, your derived intelligence, and your pattern library remain yours.

2. Will our patterns train a model that our competitors benefit from?

No.

Your patterns do not train a shared model, shared recommendation layer, or shared playbook that competitors can benefit from.

Alfred may use foundational AI models as infrastructure, but your organizational intelligence does not flow into those models for training. Your campaign history, performance signals, decisions, outcomes, and derived patterns stay isolated to your Alfred instance.

That means the intelligence Alfred builds from your data is used only to improve analysis and recommendations for your team.

A competitor cannot benefit from your learnings, your patterns, your outcomes, or your decisions.

3. Will you sell insights derived from our data to others?

No.

We do not sell, license, share, publish, or commercially monetize your data or derived intelligence.

Your data is not our product. Your subscription is our product.

We will not use your data in benchmarks, case studies, sales material, third-party reports, or category insights unless you explicitly approve it in writing.

Your raw data, derived intelligence, and private pattern library remain yours. This will be reflected in the contract, including ownership, retention, deletion, subprocessor, and data-use terms.