Skip to main content

Enterprise Knowledge, Made Actionable

Reduce costs, improve reliability, and govern agent behavior with structured enterprise knowledge.

Explore SIGN
View on GitHub
cut-ai-costs-with-sign

Cut knowledge-layer tokens by 50–60%

Give agents knowledge they can reason over, interpret systematically, and apply consistently.

SIGN™ – Sigil Intelligence Graph Notation – is an open standard for expressing enterprise knowledge in a form agents can read, reason over, and act on with governance.

JSON serializes data. SIGN™ governs how agents use it.

Read the Whitepaper

The Problem

Agents can access data. They can’t apply it.

Enterprise knowledge already exists—policies, data models, frameworks, and domain definitions.

But it is not structured in a way agents can use.

Data may be structured, but the relationships, constraints, and rules that define how it behaves are not codified. Documents describe intent but cannot be enforced. Prompts introduce flexibility but no consistency.

The result: Agents retrieve information. They do not apply rules, enforce constraints, or produce repeatable outcomes.

agents-cannot-access-data

SIGN™ Solves This

Make knowledge executable

SIGN™ encodes not just what exists, but how it behaves.
Instead of describing rules in text or embedding them in code, SIGN expresses them directly in the knowledge layer.

Example

@def internal-mobility

Redeploy qualified internal talent before hiring externally.

@rules

1. Surface internal candidates before external when matchScore >= threshold

2. External hiring requires justification when a qualified internal match exists

@constraints

forbids: external hire approved when internalMatch.score >= threshold and override = absent

enforced-by: workforce-governance-layer

@infer readiness

when: tenure >= 12mo and skillMatch >= threshold

then: candidateStatus = promotion-ready

confidence: derived

src: mobility-rule-007

This defines what the policy is, how it is applied, what cannot happen, and how conclusions are derived.

Agents do not interpret intent. They operate with governed enterprise knowledge.

THE STANDARD

SQL for Enterprise Knowledge

SIGN™ provides a standard way to express knowledge so it can be interpreted and applied consistently across systems.

Like SQL for data, it separates representation (what is defined) from execution (how it is used). This makes knowledge reusable, enforceable, portable, and standardized.

Use Cases

SIGN™ applies anywhere agents must interpret information, apply rules, and produce consistent outcomes.

skillxp-cost
career-highways-delivers

Workforce Intelligence & Career Pathing

Standardize roles, skills, and pathways to drive mobility and workforce decisions.

supply-chain-and-logistics-optimization

Supply Chain & Logistics Optimization

Apply constraints across sourcing, inventory, and distribution for consistent operational decisions.

advanced-manufacturing-and-operations

Advanced Manufacturing & Operations

Enforce process rules and quality thresholds to drive repeatable production outcomes.

regulatory-compliance-and-audit

Regulatory Compliance & Audit

Translate requirements into enforceable logic with fully traceable decisions.

financial-and-risk-decisioning

Financial & Risk Decisioning

Apply defined rules and thresholds to produce consistent, explainable outcomes.

LOWER COST

lower-cost

Reduce Overhead. Increase Performance.

AI systems scale with tokens. Every request carries cost, and most enterprise implementations rely on formats that were never designed for efficiency.

JSON introduces structural overhead. Documents require excessive context. Both consume tokens without improving decision quality.

SIGN™ replaces that overhead with compact, semantic structure.

Because relationships and logic are encoded directly, agents require less context to achieve the same outcome.

  • Fewer tokens per request.
  • Lower inference costs.
  • Faster execution.
  • More knowledge in every interaction.

At scale, this reduces the tokens agents spend on knowledge by 50–60%, lowering inference cost at scale.

Example:

JSON

Verbose structure. Repeated keys. More tokens.

SIGN™

Compact rules and constraints. Less context required.

Result

50–60% fewer tokens. Lower cost. Faster execution.

Same knowledge.
Half the tokens. Consistent, governed results.

GOVERNED REASONING

data-to-decision

From Data to Governed Decisions

Agents produce reliable outcomes only when facts, constraints, and inference logic are explicitly defined.

In most systems, those elements are left to the model to interpret. Because LLMs are probabilistic, reasoning is inconsistent—rules may be applied differently, constraints may be missed, and outcomes can vary.

SIGN™ moves that responsibility out of the model and into the knowledge layer.

It encodes what is true, what must be enforced, and how conclusions are derived—so agents operate against defined logic instead of inferring it.

This enables systematic interpretation and repeatable outcomes, rather than probabilistic responses.

SIGN™ does not perform reasoning.

It defines how reasoning is applied—consistently and governed.

Why Sign™?

Why SIGN™

Teams adopt SIGN to:

  • reduce AI costs by 50%+
  • standardize enterprise knowledge
  • eliminate inconsistent agent behavior
  • enable consistent, governed decisioning

Without SIGN™, agents guess. With SIGN™, they execute.

management-teams-adopt

Open Standard

open-standard

Open where adoption matters

A knowledge representation layer only works if it is shared.

If every organization defines its own format, knowledge cannot move across systems, agents cannot interoperate, and adoption stalls. A common language is required for agents to consistently interpret and apply knowledge at scale.

Career Highways is open sourcing SIGN™ to establish that standard.

  • Organizations can adopt without lock-in.
  • Developers can build on a shared model.
  • Knowledge can move across environments.

SIGN™ defines the language. Anyone can use it.

Career Highways + SIGN™

Optimized workforce intelligence. Extensible to any domain.

SIGN™ enhances how Career Highways structures and applies enterprise knowledge, reducing context, lowering AI costs, and enabling faster, more consistent decisioning.

In workforce, Career Highways has applied this model to roles, skills, policies, and career pathways, turning them into a governed system of record. SIGN™ strengthens that system by making knowledge more compact, consistent, and optimized for agent execution.

Because SIGN™ is a general representation layer, the same approach extends beyond workforce. Organizations can structure knowledge in SIGN™ and integrate it into the Career Highways platform to support governed decisioning across workforce, compliance, risk, operations, and beyond.

SIGN™ is the language.

Career Highways is the optimized, governed workforce intelligence system.

workforce-intelligence

AGENT EXECUTION

agent-execution

Make agent decisions consistent and governed

Structure enterprise knowledge so agents don’t guess—they execute.

SIGN™ encodes rules, constraints, and logic directly into the knowledge layer, producing outputs that are:

  • Consistent
  • Explainable
  • Governed
  • Repeatable

THE ROSETTA STONE FOR AGENTIC KNOWLEDGE.

The Rosetta Stone did not invent knowledge — it made a record that already existed, but could not be read, suddenly legible. Your organization’s rules, constraints, and logic already exist: in policies, data models, and the judgment of your experts. SIGN™ makes them legible to agents. It does not add knowledge. It unlocks what you already have.

AN OPEN STANDARD, LIKE SQL.

SQL standardized how anyone talks to a database — without standardizing the databases themselves. A multi-hundred-billion-dollar industry grew on top. SIGN™ standardizes how agents read enterprise knowledge, without exposing the knowledge itself. Anyone can write SIGN™; the canonical knowledge stays yours.