Sketch · not shipped · not in `/demo`

Tailor ecosystem — decision fabric preview

Tailor's ecosystem exists to make agreement work observable, governable and reusable.

Diagnosis

Drafting is solved. Agreement is the bottleneck.

Tailor compresses regulated agreement work by turning scattered review activity into a governed decision fabric: what was proposed, who reviewed it, what evidence was cited, which rules applied, what changed, and how agreement was reached.

The ecosystem below shows how that fabric is built: sovereign infrastructure, captured work signals, organisational constraints, agentic orchestration, model routing, and vertical products.

01

Sovereignty boundary

AloomU

The physical and logical boundary for sovereign workloads. AloomU defines what can stay in-country, what must remain in-substrate, and which AI tasks can safely route to external providers.

02

Decision capture

Tailor captures the work that usually disappears between meetings, documents, comments and approvals.

Audio

Conversations, calls, classrooms and meetings become transcripts, segments and proposed actions.

Visual

Walkthroughs, builds, environments and physical context become annotated scene intelligence.

Operational

Review events, comments, approvals, ownership changes, document versions and decision states become indexed decision memory.

03

Constraint layer

Axioms

Axioms are the rules Tailor reasons against: policies, procedures, delegations, legislation, contract requirements and approval rubrics.

They constrain topology proposals, guide GoalNode evaluation, and make every AI-assisted recommendation auditable against the organisation's own operating rules.

04

Tailor Fabric

Tailor Fabric is the governed orchestration layer. It turns captured context and organisational constraints into human-controlled, agent-run workflows.

Fabric lets teams observe, plan, execute, evaluate and adapt review work while preserving an audit trail of how each decision was reached.

Core capabilities

Human-authored workflow graph

Open Topology

Humans control the macro-DAG through nodes, edges and layout, with deterministic validators and optimistic concurrency control.

Bounded agentic workspaces

GoalNodes

Cyclic ReAct loops can run inside without breaking the acyclic macro-graph.

Safe rollback of graph and memory

Timeline

High-confidence actions auto-commit; the timeline allows safe rollback of graph topology and semantic memory.

Pre-flight validation of AI proposals

Inner Monologue

The LLM tests and corrects topology proposals against validators before surfacing them to the user.

Autonomous recovery suggestions

Self-Healing Pipelines

Failed nodes trigger structural patch proposals that bypass outages or repair execution paths.

Unaffected review streams keep moving

Parallel Branches

Independent execution branches continue while only the affected branch waits for human clarification.

Interfaces & protocols

  • REST API — declarative graph mutations for nodes, edges and layout.
  • SignalR — real-time graph mutation + agent telemetry.
  • MCP — model context protocol integration.
  • CLI — operator and developer access.
  • WOPI — document workflow integration.
  • PACT — proposal and contract layer.
  • BAINK — sovereign billing layer.
05

Model routing

Every AI task has a sovereignty footprint.

Tailor routes reasoning, vision, document generation, GraphDiff proposals, embeddings and GoalNode evaluation to the right model based on sensitivity, latency, context length, modality and tenant policy.

Anthropic Claude

US-hosted

Deep reasoning · long-context orchestration · complex GoalNode work · cleanup passes

Azure OpenAI

Azure tenant — region configurable

Vision · structured GraphDiff output · fast correction loops · embeddings · tenant-configurable regional hosting

Sovereign LLM on AloomU

In-substrate (TBD)

Sensitive workloads · PII sanitisation · high-classification Axiom evaluation · self-healing context that must remain in-substrate

Routing policy · key design decision

Default reasoning · vision · long-context · low-latency · per-tenant overrides · sovereignty-only execution all need explicit rules.

06

Vertical products

The same decision fabric can be packaged into vertical products. Each one inherits Capture + Axioms + Fabric + Routing on the same sovereign substrate.

Live / emerging

Vantage

vertical

Capital Programme Intelligence

Logan City CouncilQueensland GovernmentFoxleigh MineHUB24ColliersFortescueNyrstarEnergy Queensland

Traide

vertical

Trade Quoting Intelligence

McNab

TailorVision

platform

Visual Intelligence

TheGoodSortAnduril

BAINK

platform

Sovereign Billing

HMAN

Future / reserved

Praxis

vertical

Healthcare Intelligence

no public reference yet

Shield

vertical

Law Enforcement Intelligence

no public reference yet

Spark

vertical

Education Intelligence

no public reference yet

Host

vertical

Hospitality Intelligence

no public reference yet

Sentinel

vertical

Managed Sovereign Security

no public reference yet

Decision register

The decisions still to make. Separated from the product story so they read as architectural calls, not uncertainty embedded in the substrate.

  1. Is AloomU the same entity as Loom, or a lower-level substrate?
  2. Are Axioms purely constraints, or also a capture modality?
  3. Should Threads, Stitches and Tailors become formal Fabric concepts?
  4. Which workloads must remain inside AloomU?
  5. Does TailorVision conflict with "Visual" as a capture-layer term?
  6. Is Spatial a platform module, a product, or only an internal capability?
  7. What is the final routing policy across Claude, Azure OpenAI and sovereign models?

Source data: src/frontend/src/data/ecosystem.json (#1176) · 4 live · 5 reserved · Anduril inline. Architecture overlay pending taxonomy lock.

Tailor — AI Document Review & Consensus Platform | Tailor