Enterprise Engineering · AI Engineering · Enterprise AI Platforms
We engineer intelligent enterprise systems.
SingleFabric combines deep engineering expertise with enterprise AI platforms to build, modernize, and operate complex digital systems — from foundational infrastructure to production-ready AI applications.
DATA · SOFTWARE · INFRASTRUCTURE · INTELLIGENCE
The proposition
From complex systems to operational outcomes.
Enterprises do not need another disconnected technology stack. They need engineering that connects data, software, infrastructure, and intelligence into systems that work in production.
Integrate — connect systems and data.
Engineer — build intelligent capabilities.
Operationalize — deploy, govern, and evolve.
What we do
Three capabilities. One engineering system.
Featured platform
Software engineering, redesigned for the AI era.
Mindaeon brings requirements, architecture, coordinated engineering agents, verification, and deployment into a governed software delivery platform.
- Multi-repository engineering workflows
- Policy-controlled execution and verification gates
- Any model — including models served locally
- Tamper-evident, hash-chained audit of every agent action
- Customer-controlled deployment, including air-gapped environments
$ mindaeon run --blueprint core-banking
▸ resolving requirements ......... 42 items
▸ architecture pass ............. complete
▸ agents: 6 coordinated ........ 3 repositories
▸ verification gates
tests ....................... 128 passed
policy ...................... allowed
evidence .................... recorded
✓ build verified — awaiting operator approval
The integration hub
Every system visible. No black spots.
Enterprise data and activity sit in dozens of systems — each one a black spot to everyone outside its team, and to every agent. Mindaeon's integration hub connects them once and makes them both visible and usable — built for the systems enterprises actually run: Jira and Confluence Data Center, GitHub Enterprise Server, self-managed GitLab, Jenkins, SonarQube, Artifactory — not just their cloud editions.
Context store with provenance
Connected systems are synced into one searchable store — every record carries its connector, source, fetch time, and sensitivity. Searchable by people in the console, and by agents through a single context-search tool.
Black-spot detection
Mindaeon reads your projects' own CI and build configuration, finds the systems your work depends on, and shows which are connected and which are still blind spots — with a coverage score and the right connector for each gap.
Permission mirroring
Source-system permissions are mirrored, so search results are filtered by what the asking person could see in the source system. Visibility in Mindaeon never exceeds visibility at the source.
Actions declared, classed, approved
Every action a connector can take is declared in advance — read, write, or delete — and write actions wait for a person's approval by default. Nothing an agent does in a connected system happens out of sight.
Enterprise AI, built for reality
From enterprise data to production intelligence.
Scroll and watch the stack assemble: connect existing systems, build the appropriate AI architecture, integrate models and agents, and deploy within your operating environment — with control at every layer.
Data foundations
Connect fragmented sources and applications — synced, searchable, permission-mirrored.
AI systems
Build workflows, agents, and applications under policy.
Infrastructure
Deploy across cloud and private environments — or entirely air-gapped.
Governance
Control access, execution, and auditability at every layer.
Solutions
Your sector. Your problem. The governed answer.
Eight sector playbooks: the problem, why current tools fail, the Mindaeon answer, and a worked scenario. When a target customer reads their playbook, the answer should be yes.
Engineering approach
Designed to work beyond the demo.
Architectural rigor, security, integration discipline, testing, and operational ownership — expressed as a delivery lifecycle.
- 01
Discover
Understand the system landscape, constraints, and the outcome worth engineering toward. No assumptions without evidence.
- 02
Architect
Design systems deliberately: boundaries, integration contracts, failure modes, and the governance model that keeps them honest.
- 03
Engineer
Build with discipline — typed contracts, tested changes, reviewed automation. Speed is a consequence of structure.
- 04
Verify
Every change passes verification gates: automated tests, policy checks, and evidence artifacts an auditor can follow.
- 05
Deploy
Ship into the environment the customer actually operates — cloud, private, or air-gapped — under their control model.
- 06
Evolve
Operate, measure, and improve. Systems earn trust through observed behavior, not slideware.
Evidence & trust
Proof over promises.
Tamper-evident audit
Every model call and agent action lands in a hash-chained record. Changing an entry, taking one out, or cutting the end off is found by walking the chain — and chain heads export to your own security monitoring.
Agents act as you, never as a shared account
Microsoft 365 and Google Workspace connections are per-person: each person signs in themselves and agents act with their permissions. Credentials are proved against the source, stored once, and never shown to an agent.
No licence server. No call home, ever.
Air-gapped installs run from a signed bundle with no telemetry, no update checks, no assets from a CDN — held by a test, not a promise. Policy is enforced where the user cannot bypass it, and we say exactly where that is.
Have a complex engineering challenge?
Let's design the right solution — from architecture and integration to AI platforms and production deployment.
Discuss your project