Trust graph for agentic business processes

Agents generate possibilities.
Trusted networks execute reality.

Agentic business processes need deterministic access to verified human knowledge. Signal.lab structures expertise so agents resolve to real people, not hallucinations.

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Agentic process

agent.run(
  "identify Zero Trust specialists
   covering FS sector, EMEA"
)

Supplier research

Find verified category experts.

Due diligence

Validate expertise and proof.

Shortlisting

Rank by fit, depth, and coverage.

Intro routing

Connect to the right human.

Signal.lab graph

Console output

-> 3 skill files resolved

-> category: ZeroTrust | FS | EMEA

-> confidence: verified | attributed

Skill file | human expertise

sarah_k.jsonVERIFIED
role
Channel consultant
category
Zero Trust
depth_years
11
sector
FS / EMEA
contact_path
/join/sarah-k
miles_r.jsonVERIFIED
role
Vendor specialist
category
IAM
depth_years
8
sector
Public sector
contact_path
/join/miles-r
helen_t.jsonVERIFIED
role
Partner AE
category
SASE
depth_years
9
sector
Mid-market
contact_path
/join/helen-t

Trust stack

Infrastructure became code.

Workflows became code.

Now trust networks become queryable.

LayerRoleSignal
LLMsGenerate and synthesiseanswers
AgentsOrchestrate processesqueries
Signal.labStructure trust and expertiseskill files
HumansBuild consensus and executerelationships

The scarce asset in the AI era is not information. It is verified expertise, trusted attribution, and relationship proximity. That is what Signal.lab structures.

Audience lanes

Publish once. Resolve for sellers, buyers, and agents.

Signal.lab turns expertise, proof, and relationship context into structured surfaces that people can trust and agents can query.

Vendor and seller view

Your channel, in code

Every channel partner, every verified seller, and every anonymised account pattern, structured, attributed, and queryable by the agents your buyers already run.

  • 29 verified sellers mapped
  • 8 account patterns surfaced
  • 100% LLM-queryable, structured
See how it works ->

Buyer view

Deterministic expertise, on demand

Query the graph with a problem. Get back verified contributors, structured proof snippets, and a direct intro path, not a ranked list of generic results.

  • Deterministic, named experts, not suggestions
  • Attributed, every result has a real owner
  • Structured, JSON endpoints and agent-ready records
Browse insights ->

Live from the graph

Signal.lab publishes structured intelligence from verified contributors.

Every article is attributed, categorised, and machine-readable.

Insight

The Trust Gap: Why Verified Human Expertise Is the Scarcest Asset in B2B Sales

New research from LinkedIn and Ipsos confirms what channel professionals already sense: AI has remapped the B2B buying journey, trust is now the decisive variable, and generic information is worthless. Only 45% of buyers trust the sellers they encounter yet 94% use AI at every stage of their purchase process. This article examines what that means for IT and cyber security channel professionals, why verified human expertise has never been more commercially valuable, and what buyers, sellers, and agents should do about it.

channel-intelligenceSignal.lab Editorial

Browse by category

threat researchchannel-intelligenceAI PublishingEditorial SystemsContent StrategySearch InfrastructureAnalytics
Browse all insights ->

Invite-only pilot

Be the next logical point of contact, for buyers and for AI.

Signal.lab is invite-only during the pilot. Request access to join the graph.

Request access ->

FAQ

What is Signal.lab?

Signal.lab is a trust graph that makes verified human expertise and professional relationships queryable by AI agents and human buyers. Contributors publish structured expertise, categories, account patterns, and proof snippets, which agents can resolve to a named, verified person with a direct contact path.

Who is Signal.lab for?

Signal.lab is built for channel sellers, consultants, vendor specialists, and the buyers and AI agents who need to find them. It is the structured layer that sits between LLM generation and human execution.

How is Signal.lab different from LinkedIn or a directory?

Signal.lab is not a social network or a search index. It is structured infrastructure. Every contributor is a callable skill file with verified fields, machine-readable JSON, and a deterministic contact path. Agents can query it programmatically, not just humans browsing a page.

How does Signal.lab prove discoverability?

Signal.lab exposes llms.txt, sitemap.xml, a public search API at /api/search, and structured JSON profile endpoints. Every piece of content is attributed to its contributor and indexed by search engines and LLM agents.

For agents and crawlers

These machine surfaces stay linked in the public HTML so crawlers, search engines, and LLM agents can discover the graph directly.