Loom AI Labs GitHub

Loom AI Labs

AI enablement engineering

We weave intelligence into real software.

Open-source foundations, managed deployment infrastructure and live engineering proof for dependable AI-enabled applications.

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Products from the lab

Managed product layer

Managed preview

LoomAI Platform

Compose templates, plugins and provider profiles into verified, self-contained AI deployments built on AI Fabric Framework and Chat UI.

  • Marketplace composition
  • Deployment lifecycle
  • Release verification
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Built on two open-source foundations

Application enablement layer

AI Fabric Framework mark

AI Fabric Framework

Live application data, evidence-grounded retrieval and governed actions for Java and Spring Boot.

  • Live data sync
  • Governed RAG
  • Controlled actions
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User experience layer

AI Fabric Chat UI

A reusable, framework-neutral chat interface for AI Fabric-enabled applications.

  • Web Component
  • React
  • Arabic and RTL
  • Four layouts
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AI Fabric Chat UI showing evidence and a confirmed action
Live Experiments Try practical AI experiments and see how ideas evolve into working systems. Applied Research Deep dives at the intersection of AI, software engineering, and real-world impact.

Runnable engineering proof

See the product stack at work.

Each experiment exposes a bounded scenario, its application boundary and observable evidence.

All experiments
live Adaptive experience

AI Shopping Experience

A shopper explores synthetic products, asks grounded questions and inspects the evidence behind the response.

  • RAG
  • Evidence
  • Chat sessions
  • Data sync

The health surface reports AI Fabric 0.4.0 and enabled chat, retrieval and data-sync capabilities.

About Launch
live Governed actions

Account Resolver

A user asks for account help, reviews the proposed operation and confirms only after the application exposes validation evidence.

  • Actions
  • Validation
  • Confirmation
  • Audit

Validation warnings can be shown without silently discarding user-confirmed values.

About Launch
live Data and retrieval

Live Data Sync

An operator creates, updates and removes synthetic application entities while watching the AI index converge.

  • Data sync
  • Index lifecycle
  • Retrieval proof
  • Operations

Create, update and delete operations have visible indexing state.

About Launch

Implementation-linked

Research that ends in inspectable software.

Engineering investigations with public implementation artifacts, explicit evidence levels and stated limitations.

All research
Data consistency Reproducible demo

Keeping Application Data and AI Evidence Aligned

This investigation treats AI indexing as an application data lifecycle rather than a one-time ingestion task. It links source mutations, queued operations, vector state and retrieval proof so an operator can inspect where alignment failed.

Artifact
Reproducible evaluation
Maturity
Demo verified
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Context and grounding Implemented prototype

Explicit Application Context Instead of Model Guessing

This study separates trusted application context from model-extracted language. It focuses on tenant identity, allowed vector spaces and resource scope as explicit inputs rather than values inferred from a prompt.

Artifact
Architecture study
Maturity
Implemented prototype
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Actions and governance Reproducible demo

A Governed Lifecycle for AI-Proposed Actions

This investigation follows an action from model interpretation through validation, warning, confirmation and application execution. It distinguishes advisory validation from trusted target checks.

Artifact
Reference implementation
Maturity
Demo verified
Read investigation

Built in the open

Inspect the source. Run the proof. Question the boundary.

Products and experiments link directly to their implementation. Claims stop where public evidence stops.

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Connect

Bring a real application problem.

Discuss framework adoption, UI integration, a reproducible experiment or an engineering research question.

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