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GOAD by INFINIA Intelligence

Knowledge you can trace. Intelligence you can trust.

Connect research to the decisions that matter. GOAD brings knowledge graphs, explainable evidence, and human judgment into the agentic enterprise.

GOAD / Connected knowledge
How can we detect machine failure earlier?
Research inputs
Fault-diagnosis survey01
Vibration-processing survey02
Publication metadata03
GOAD
extract · relate · trace
Illustrative knowledge path. Explore a curated research example below.

01 / The knowledge foundation

Research becomes more useful when it connects.

GOAD organizes scientific literature into connected concepts, methods, and evidence. A knowledge graph makes the relationships visible — and gives AI context it can point back to.

  1. 01

    Scientific literature

    Papers · tables · references

  2. 02

    Structured knowledge

    Concepts · methods · findings

  3. 03

    Knowledge graph

    Relationships · provenance

  4. 04

    Enterprise context

    Questions · evidence · decisions

44M
DOI-mapped research documents
≈4,500
research topics

Source-reported scale · GOAD Overview, 19 March 2026. Counts describe the research corpus, not the examples on this page.

02 / Explainable by design

An answer is a starting point. Show me why.

Follow a claim back to its source. See what the research supports, where it stops, and what still needs a person’s judgment.

Evidence path / 01Curated research example

Follow a predictive-maintenance question from a claim to the research that informs it. Select a claim to inspect its source and limits.

Business questionWhat evidence would help us assess vibration-based predictive maintenance?
Select a claim or gap01 — 03
Connected sourceData fusion techniques for fault diagnosis of industrial machines: a surveyClaim → surveyed approach
Evidence inspector01 / 03

Claim → surveyed approach

Data fusion can bring multiple machinery signals into a fault-diagnosis assessment.

A survey examines data-fusion techniques at different levels for industrial machinery fault diagnosis and predictive maintenance. It also discusses challenges and opportunities.

Source record

Data fusion techniques for fault diagnosis of industrial machines: a survey

Amir Eshaghi Chaleshtori and Abdollah aghaie

17 November 2022 · Author-deposited preprint

DOI 10.48550/arXiv.2211.09551

Survey of approaches and open challenges across machinery fault diagnosis.

What this does not establish

A survey of techniques does not establish performance or readiness for a particular machine, dataset, or business setting.

This is a curated illustration of an inspectable evidence path. It is not a live GOAD query or a verified assessment of a specific system.

Keep the source in sight.

A useful answer carries its provenance: the publication, the date, and the evidence behind the claim.

Make uncertainty visible.

Missing evidence and limits belong next to the answer, where a decision maker can act on them.

Give people the final say.

Research informs the recommendation. Review, context, and approval determine what happens next.

03 / Explore the connections

One knowledge foundation. Many fields of possibility.

Start with a domain. See how a method, a research question, and a practical application can connect.

Choose a research topic
A question to explore

Which recovery method fits the reservoir?

MethodEnhanced oil recovery
assessed against
Evidence to examineReservoir conditions
informs
ApplicationMethod assessment

Illustrative topic relationships, not retrieved GOAD results.

04 / Built around a decision

Put knowledge to work.

From a technical question to a useful business deliverable. Explore three ways to apply a shared evidence foundation.

Choose a business use case
The business question

Does the technical promise hold up?

Open demo
Bring together

Research findings, comparable methods, and known constraints.

Prepare for review

A technical assessment with traceable claims and open questions.

Example workflows. The demo library uses labeled illustrative scenarios.

05 / The agentic enterprise

Let agents do the groundwork. Keep judgment in the loop.

A knowledge foundation can give enterprise agents shared context. The workflow still needs clear boundaries: what the agent prepares, what the evidence supports, and who approves the next step.

Explore the proposed workflow

Human review

A person checks the evidence, applies business context, and decides whether to proceed.

Proposed enterprise workflow · this illustration does not execute business actions.

InfiniaIntelligenceINFINIA INTELLIGENCE with GOAD

Build the next decision on better knowledge.

INFINIA Intelligence connects research intelligence with practical enterprise experiences. GOAD is the knowledge foundation. Our demos make the possibilities tangible.