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About 7lens

For CTOs, heads of platform and enterprise architects: how 7lens works, and what you gain.

  • One model of your company. An ontology with four axes that grows with you, and that you own.
  • Your data, connected in place. 7lens reads what your tools already know and connects it inside your environment. AI proposes; trusted data, fixed rules and an explicit check decide.
  • Onboarding in under 40 hours. From an empty model to Lenses your teams and agents can use.
What you gain

What engineering leaders gain

  • Speed, without a data lake

    7lens reads what your CMDB, cloud, security, finance and monitoring tools already know, and connects it. No data lake, no cleaning project, no second copy of your data.

  • Answers you can trust

    AI proposes. Trusted data, fixed rules and an explicit check decide what is true. Every fact keeps where it came from, and a defined rule settles each disagreement.

  • No lock-in

    You own your semantics. Define the company once and map any tool or framework to it. Swap a tool and only the mapping changes.

  • In your environment

    The data, the Context and the processing stay in your environment, with zero data egress. The 7lens management plane receives human configuration only.

The model

7lens Data Model

The enterprise model: four axes, how they connect, and the three Groups containers.

The core 7lens model is the Ontology: four axes — Groups, Organization, Technology, and Business. Organization, Technology, and Business describe what your enterprise is. Groups is one axis with three containers (Groups, Frameworks, Integrations) that classifies across the other three. Filled in for your enterprise, they become your Context: the shared picture people, AI agents and software read.

Ontology vs. Context

The Ontology is the model 7lens ships: the kinds of things that exist and how they are allowed to connect. You add your own Groups and attributes to it. The Context is that model filled in for your enterprise — your actual organizations, applications, services, and the links between them.

The 7lens Data Model. Groups is one axis with three containers: Groups, Frameworks and Integrations. It classifies across the three other axes. Organization: Organization, Region, Sub-region (optional), Country, Legal Entity. Technology: Stack, Service, then Offering and Asset Type side by side, with Platform and ICT Provider cross-cutting. Business: Domain, Application. Data Model Extensions add Inventory, where each Asset lives, and, in future, Contracts and Processes. Together they form one 7lens Context.

Groups

One axis, three containers

Defined by each enterprise. They classify elements across Organization, Technology and Business, and set strategic ownership and access boundaries through the Owner of each value.

Groups

Your own classification dimensions, with values and Owners.

  • Business Line
  • Criticality
  • Modernization Wave
  • Operating Model
Frameworks

Standards, architecture models and compliance regimes from the catalogue. Selecting one records intent, not certification.

  • TOGAF
  • TBM
  • ISO 27001
  • GDPR
  • DORA
Integrations

Tool and platform taxonomies from the catalogue. Not instance, tenant or Asset ids.

  • ServiceNow CSDM

Organization

Who the enterprise is and where it operates.

  1. Organization(global)
  2. Region
  3. Sub-region(optional)
  4. Country(ISO 3166-1 alpha-2)
  5. Legal Entity

Each node has an accountable person (Owner). Regions and Sub-regions follow UN M49.

Technology

The technology capabilities the enterprise provides and how they are organized.

  1. Stack(recursive)
  2. Service
  3. Offering(product or variant)
    Asset Type(recognition key for Assets)
Cross-cutting
Platform(in-house or third-party)
ICT Provider(third-party supplier)

Stack → Service → Offering / Asset Type. Platform and ICT Provider are cross-cutting, not levels.

Business

The business areas and the applications that support them.

  1. Domain(recursive)
  2. Application

A Domain belongs to exactly one Legal Entity. An Application belongs to a Domain and consumes the Technology Services it relies on. It stores no link to a Platform: Assets show that relation.

Data Model Extensions

They extend the core model without being axes of it.

Inventory

Every real thing the enterprise tracks. An Asset belongs to Applications and/or Platforms (it may also belong to a Domain) and maps to Technology through its Asset Type and one Offering. A Platform may produce an Asset, or not; Platform-produced Assets are the recommended target.

Asset(one real instance)

ContractsFuture

Commercial contracts with vendors and providers, tied to what they actually cover.

ProcessesFuture

Operational processes made machine-readable, so they can be automated and monitored.

7lens Context

One trusted Context: every element connected and classified the same way, read by people, AI agents and software.

Example: a payments app in Retail Banking

  1. Retail Banking is a Business Domain.
  2. That Domain belongs to a Legal Entity in Spain, which gives it its place in the Organization.
  3. Payments is an Application in the Retail Banking Domain. It stores no Legal Entity of its own; it sits in the Organization through its Domain.
  4. Payments consumes a Technology Service it relies on: Kubernetes, in the Compute Stack.
  5. The Kubernetes Platform supplies that Service and creates a cluster for Payments.
  6. The cluster is an Asset in Inventory. Its Asset Type, Kubernetes cluster, sits on the Kubernetes Service, and the Asset names one Offering of that Service.
  7. The cluster belongs to Payments; the Platform produced it. Payments and the Platform have no direct link: the Asset shows the relation.
The product

7lens Proprietary AI Powered Data Processing

What you configure, what 7lens does with it, and how people and software read the result.

7lens gives your enterprise one connected picture of its landscape. It reads what the tools you already use — CMDB, cloud, security, monitoring, finance, application catalogs, custom systems — already know, and connects it inside your own environment with zero data egress. It does not replace those tools, and it does not copy your data out.

Interpret your data. Don't migrate it.

No data lakes. No cleaning projects. 7lens reads the enterprise data you already have and gives each leader and AI agent their own view. All inside your environment.

Runs in your environment

7lens runs in your own cloud account. Early Access installs on AWS.

Zero data egress

Your data, the Context and the processing stay with you. The 7lens management plane receives human configuration only, never customer data.

Your data, your region

Data is kept in the region you require, inside your security boundary.

One enterprise. One ontology. Seven lenses.Same data. Different questions. Better decisions.

The 7lens architecture. Your existing systems (cloud, security, finance, CMDB, operations and other sources) send what they know to Collector. Everything else runs in your environment, with zero data egress: Collector records and checks each delivery and passes it to the Semantic Engine, which uses the Ontology to normalize, reconcile, validate and map it, keeps the trail and resolves disagreements, and produces your enterprise Context. A 7lens management plane (SaaS), outside your environment, receives human configuration only and no customer data. AI proposes; trusted data, fixed rules and an explicit check decide what is true. People read the Context through Lenses 1 to 6, AI agents through Lens 7 over MCP and the API, and software through the API.

Your existing systems

7lens reads what they know. It does not replace them.

  • CloudCloud platforms
  • SecuritySecurity tools
  • FinanceFinance systems
  • CMDBAsset and configuration records
  • OperationsMonitoring tools
  • Other sourcesApplication catalogs · Custom tools
7lens SaaS · outside your environment

Management plane

Human configuration only. No customer data.

Runs in your environment · zero data egress

Collector

The front door for what your systems already know.

  • Records who sent it
  • Checks the payload is usable
  • Accepts a full load or a change
  • Keeps a status for each delivery

Ontology

The model: what kinds of things exist and how they can connect.

  • Organization
  • Groups
  • Technology
  • Business
  • Extensions: Inventory, Contracts, Processes

Semantic Engine

Turns source information into one connected picture.

  1. Normalize
  2. Reconcile
  3. Validate
  4. Map
  5. Keep the trail
  6. Resolve disagreements

Your enterprise Context

The model filled in for your enterprise.

  • Organization
  • Groups
  • Technology
  • Business
  • Inventory
  • ContractsFuture
  • ProcessesFuture

AI proposes. Trusted data, fixed rules and an explicit check decide what is true.

Seven Lenses. One Context.

Each Lens asks its own question of the same picture.

People

Lenses 1 to 6, written for the people who lead and govern.

  • 01Security Lens
  • 02CTO Governance Lens
  • 03CFO Capital Lens
  • 04CIO Strategy Lens
  • 05Lock-In Free Lens
  • 06Automation & AIOps ROI Lens

AI agents

Lens 7, through MCP and the API, under the same permissions and audit trail as a person.

  • 07AI Lens

Software & systems

The same Context through the API: what exists, how it connects, and where each fact came from.

  • Custom Lens
  • Custom Lens
  • Custom Lens

Follow a fact: Example fact (illustrative)

  1. Your tools keep their facts and stay the authority for them. Here, a cloud platform and a CMDB each describe the same cluster. (Cloud platform: prod-eu-k8s-01; CMDB: Production K8s (EU))
  2. Collector records who sent each delivery, checks the payload is usable, accepts a full load or a change, passes it to the Semantic Engine and keeps a status. (2 deliveries received; Sender recorded; Payload checked; Status kept)
  3. The Semantic Engine does not treat incoming data as already true. First it normalizes names and formats into the 7lens model. (Names and formats aligned to the model)
  4. Reconcile: records that describe the same real thing become one canonical record. (2 records → 1 canonical record)
  5. Validate and map: rules mark anything that does not fit the model, and each fact is attached to the right place. AI may suggest the mapping; trusted data, fixed rules and an explicit check decide. (Checked against the Ontology; Asset Type: Kubernetes cluster; Belongs to a Platform; Suggested by AI, decided by rule and check)
  6. Keep the trail and resolve disagreements: the record keeps where each fact came from, when it was seen and how confident the match is. If two sources conflict, a defined rule picks the outcome. Nothing is chosen quietly. (Sources kept; When it was seen; Match confidence; Conflicts decided by a rule)
  7. The result is one connected fact in your enterprise Context, built and kept inside your environment. The source systems are unchanged. (One connected Asset; Source systems unchanged)
  8. The same Context is read through the seven Lenses: Lenses 1 to 6 by people, Lens 7 by AI agents through MCP and the API. Software reads the same Context through the API. (Lenses 1–6: people; Lens 7: AI agents (MCP, API); Software: API)
Getting started

Onboarding in under 40 hours

Three phases take you from an empty model to Lenses your people and agents can use.

  1. Configure the enterprise

    Fill in the four axes: Groups, Organization, Technology (starting from the open OSM catalog) and Business.

  2. Decide who and what can use it

    People, AI agents and systems, each with their own access and update permissions.

  3. Put Lenses to work

    Start with the use case that brings the most value, then combine and extend.

Register to testRead the step-by-step guide See the seven use cases