Source-backed before model-backed
Ground governance answers in source registers, review status, and known limitations before using model output.
Governance Intelligence
Governance Intelligence for Enterprise SLMs
Governance knowledge prepared for reviewed retrieval, traceability, and future SLM evaluation.
Scaled Agents Governance Intelligence is an incubation product direction designed to organize AI governance source material, product decisions, architecture context, framework mappings, retrieval approval, training boundaries, and evidence boundaries before that knowledge is considered for drafting, future model adaptation, or product integration.
SLM Context
Enterprise SLMs need governed knowledge before model adaptation.
Before an enterprise builds, fine-tunes, or adapts a small language model, it needs to know what knowledge is approved, where it came from, who reviewed it, how sensitive it is, whether it is allowed for retrieval, and whether it is separately approved for training. Governance Intelligence is designed to help prepare that source-backed foundation for reviewed retrieval or future model-adaptation decisions.
Review-ready before retrieval
Separate raw intake from reviewed knowledge so retrieval paths can stay inside approved source boundaries.
Training boundaries before fine-tuning
Classify what may be retrieved, drafted against, adapted later, or excluded from model training. RAG approval is not training approval.
Evidence before authority
Keep citations, source posture, and human review visible before generated answers influence governance work.
Applied SLM Lane
Forward-Deployed AI gets its own SLM-readiness lane.
The FDE SLM Readiness Pack applies the Governance Intelligence pattern to forward-deployed AI delivery: source registers, corpus boundaries, golden eval cases, refusal rules, and output-safety checks before any FDE model, retrieval workflow, or customer-managed SLM support is selected or trained.
Delivery-specific corpus
Keep FDE operating-model pages, GPT support drafts, playbook material, and market-signal notes separate from raw or customer-specific content.
Evaluation before adaptation
Test whether an SLM can reason about workflow discovery, Passport-ready scope, Toll Gate checkpoints, evidence gaps, reusable playbooks, and provider-neutral boundaries.
No model launch claim
The FDE lane is readiness and evaluation planning. It is not a trained model, fine-tuned adapter, live retrieval system, customer-facing assistant, or production Control Plane integration.
See the Forward-Deployed AI operating model for the public delivery context. RAG approval, training approval, model selection, and commercial packaging remain separate gates.
Problem
AI governance knowledge gets scattered before teams can reuse it safely.
Architecture decisions, PRDs, source-of-truth records, standards mappings, market signals, and review notes often live in different places. Without a governed corpus, teams can lose provenance, apply the same concept inconsistently, or ask AI systems to reason over material that has not been reviewed for public use, retrieval, or training.
Fragmented decisions
Important product, architecture, governance, and standards decisions become hard to find after the conversation moves on.
Unclear source posture
Draft notes, public-safe content, internal review records, and deprecated material need separate status before they are retrieved or reused.
Weak answer traceability
Governance answers should cite the source record, review status, sensitivity, and limitation instead of sounding authoritative without evidence.
Evidence And Traceability
Traceability should remain inspectable before generated material is relied upon.
A customer-managed implementation should preserve source references, review posture, allowed-use boundaries, citations, and known limitations without exposing private content or internal decision logic on the public site.
Source context
Identify the source, owner, review posture, currency, sensitivity, and allowed use for retrieved knowledge.
Answer context
Keep citations and limitations visible so generated material can be checked against the approved source.
Customer control
Customers retain responsibility for their corpus, access controls, retrieval configuration, model selection, evidence, and production decisions.
Public examples demonstrate the product direction only. They do not enable live retrieval, vector persistence, customer-data processing, connectors, runtime enforcement, or production use.
Capability
A governed corpus before a generative assistant.
Governance Intelligence starts with controlled knowledge objects, source registers, metadata, review status, sensitivity flags, public/private content separation, training boundaries, retrieval refusal rules, and evaluation questions. Retrieval and assistant workflows come after the corpus is structured enough to support consistent, source-backed answers.
Source Register
Track each source document, standard, decision, research note, market signal, and approved knowledge object with provenance and review status.
Metadata Schema
Classify knowledge by type, owner, sensitivity, product area, source date, related framework, allowed use, retrieval boundary, and training boundary.
Approved Corpus
Separate reviewed knowledge from raw intake and archived material so retrieval stays bounded to the right source set.
Evaluation Harness
Use golden questions, expected answers, citation checks, hallucination tests, leakage tests, and refusal checks before relying on generated responses.
Path
The Governance Intelligence path
The model adaptation decision comes later, after corpus quality, retrieval boundaries, review posture, and evaluation maturity support it. A future SLM path should clear corpus-quality targets, hallucination-resistance tests, zero critical leakage failures, source-citation checks, training-source approval, adapter ownership, and retirement or rollback planning before fine-tuning begins.
FirstCorpus foundation
NextSource-backed retrieval rules
ThenEvaluation harness
LaterModel adaptation decision
Use Cases
Designed for governance teams that need consistent answers without losing review control.
The first use cases are review-preparation workflows: finding past decisions, understanding governance rationale, mapping a topic to framework-informed controls, preparing Passport draft inputs, and identifying evidence requirements. Any generated output should remain claim-free, public-safe, source-bounded placeholder context for human review. Human owners and qualified reviewers still make formal decisions.
Decision retrieval
Find the relevant source-of-truth record, product decision, architecture note, or governance workflow that explains why a boundary exists.
Passport draft support
Prepare draft owner, purpose, scope, risk, tool, data, review, and evidence fields for a Passport record without treating the draft as approval.
Framework mapping support
Map governance concepts to framework-informed readiness themes with citations and limitations, not certification or compliance conclusions.
Evidence requirement support
Suggest candidate evidence records, review paths, and missing-input questions for owner review before action or publication.
License Boundary
Preparation use is allowed; commercial implementation requires a paid license.
This public Governance Intelligence package may be used to evaluate the concept, prepare internal review materials, discuss governance readiness, and plan a source-backed knowledge layer. Commercial implementation, production deployment, customer-facing use, resale, white-labeling, derivative service delivery, or operational use requires a separate paid Scaled Agents license or written agreement.
For licensing, implementation, or commercial-use details, contact Scaled Agents at [email protected]. Public page access, package download, or review use does not grant production rights, customer deployment rights, partner rights, support obligations, or rights to use Scaled Agents materials to build a competing product.
Boundaries
What this product direction does not claim yet.
Governance Intelligence describes governed knowledge-layer planning for future SLM evaluation. It should not be confused with an approved fine-tuned model, automated legal interpreter, external compliance authority, public knowledge base, or customer-facing production retrieval service.
No fine-tuned SLM claim
Fine-tuning should wait until the approved corpus, evaluation harness, training-source approvals, adapter ownership, and rollback plan are mature enough to justify model adaptation.
No generated-output claims
SLM-generated or SLM-assisted outputs should remain placeholder context for review, not legal, compliance, audit, security, production, implementation, customer-readiness, or model-release claims.
No legal or compliance conclusion
Framework mapping support can help prepare review materials. It does not provide legal advice, compliance approval, audit opinion, or certification.
No Control Plane change
Future Control Plane or Passport Studio integration remains a later gated path after the standalone knowledge layer is proven.
No customer data training
Customer-specific data should remain isolated, reviewed, and excluded from training unless separately governed by approved terms and controls.
Public materials are provided for education, readiness planning, review preparation, and product-context discussion only. They do not make formal decisions, approve production use, authorize AI workers, certify compliance, validate security posture, provide legal advice, create compliance determinations, create audit opinions, or claim a model release.