A. AI Product development team.
- Fact Sheet #1 — RLSF as the Off‑the‑Shelf Replacement for RLHF
- Fact Sheet #2 — Intellisophic Customer Awareness: Data Sources & Enterprise Fit
FACT SHEET #1
RLSF: The Off‑the‑Shelf Replacement for RLHF
Purpose
Position RLSF as a direct, drop‑in replacement for RLHF inside Microsoft’s internal AI stack—faster, cheaper, more consistent, and dramatically less compute‑intensive.
What RLHF Gets Wrong
Human Feedback = Bottleneck
- Requires large pools of human annotators
- Slow iteration cycles
- High cost per labeled unit
- Inconsistent judgments
- Vendor minimums often start around $1M+
- Multiple inference passes required for preference loops
RLHF was essential in 2020.
It is a liability in 2026.
What RLSF Fixes
Semantic Feedback = Machine‑Speed Quality Layer
RLSF replaces human preference loops with automated semantic scoring.
- Deterministic, ontology‑grounded evaluation
- No crowdsourcing
- No repeated sampling
- No human latency
- No subjective variance
Powered by iSAM‑1
- Billions of semantically indexed sentences
- Derived from large‑scale web sources
- Structured using a W3C‑compliant RDF ontology
- Ready for immediate internal deployment
Compute Reduction
RLHF compute cost comes from:
- Repeated inference cycles
- Multiple sampling passes
- Human‑in‑the‑loop delays
- Re‑evaluation after each training step
RLSF collapses the loop:
- One inference pass
- Immediate semantic scoring
- No human raters
- No repeated sampling
Fewer passes → fewer tokens → fewer GPU hours → lower internal cost.
Direct Comparison
Capability RLHF RLSF
Feedback Source Human raters Automated semantic scoring
Consistency Variable Deterministic
Throughput Human‑limited Machine‑speed
Cost Structure High, labor‑based Usage‑based
Scalability Constrained Unlimited
Ontology Support None Native W3C RDF
Compute Cost High Reduced
Deployment Custom Off‑the‑shelf
Executive Summary
RLSF is the first off‑the‑shelf system that fully replaces RLHF inside Microsoft’s AI stack.
It improves model quality, reduces compute consumption, and eliminates human‑based bottlenecks.
FACT SHEET #2
Intellisophic Customer Awareness: Purview Data Sources & Enterprise Fit
Purpose
Demonstrate that Intellisophic understands Microsoft’s enterprise customers and aligns naturally with the data governance ecosystem they already use—especially Microsoft Purview.
Why Purview Matters
Purview is the governance fabric for Microsoft’s enterprise customers.
It defines the universe of data sources customers trust for:
- Classification
- Compliance
- Discovery
- Risk management
- Data lineage
Intellisophic builds on this familiarity by aligning RLSF with the same data sources.
Common Purview Data Sources in Customer Environments
Azure‑Native
- Azure Blob Storage
- Azure Data Lake Storage
- Azure SQL Database
- Azure Synapse Analytics
- Azure Cosmos DB
Microsoft 365
- Exchange Online
- SharePoint Online
- OneDrive for Business
- Teams chat and file content
- Power BI semantic models
On‑Premises / Hybrid
- SQL Server
- Oracle
- SAP
- File shares
- Teradata
Multi‑Cloud / SaaS
- Amazon S3
- Google Cloud Storage
- Snowflake
- Salesforce
- ServiceNow
Why This Matters for Intellisophic
If Purview can see it, Intellisophic can use it.
This demonstrates:
- Deep customer awareness
- Seamless alignment with existing governance workflows
- No new ingestion pipelines
- No new compliance overhead
- Natural fit with Microsoft’s enterprise architecture
How RLSF Complements Purview
Purview = Data discovery & classification
RLSF = Semantic evaluation & AI quality scoring
Together, they create a unified pipeline:
- Purview identifies and classifies enterprise data
- RLSF uses that data to evaluate model correctness, grounding, and alignment
- Customers gain a governed, semantically aware AI quality loop
Sales Engineering Talking Point
“Your data is already governed through Purview. Intellisophic simply adds a semantic intelligence layer on top of the same sources—no new ingestion, no new pipelines, no new governance burden.”
Executive Summary
Intellisophic understands the real operational environment of Microsoft’s enterprise customers.
RLSF fits directly into the Purview‑governed data estate, making it a natural extension of Microsoft’s existing governance and AI quality infrastructure.
FACT SHEET #3
Intellisophic: Top‑Tier Semantic Engineering for RLSF Deployment
Overview
Intellisophic delivers Reinforcement Learning from Semantic Feedback (RLSF) on top of a semantic engineering foundation developed over more than two decades. The company’s engineering and dev‑ops organization includes over 50 specialized semantic engineers, enabling Microsoft‑grade support, reliability, and integration readiness.
- Engineering Capability at Microsoft Support Levels
Intellisophic’s engineering team is structured to meet the expectations of enterprise and government AI environments:
- High‑availability support readiness
- Formal escalation and incident‑response pathways
- Rigorous QA and release discipline
- Deep semantic modeling and ontology expertise
- Secure development lifecycle practices
- Proven readiness for regulated and mission‑critical workloads
This positions Intellisophic as a trusted engineering partner for internal Microsoft teams and enterprise AI programs.
- RLSF: Semantic Feedback Engineered for Enterprise and Government
RLSF replaces human‑driven feedback loops with automated semantic scoring derived from Intellisophic’s ontology‑based platform.
Core strengths:
- Deterministic semantic evaluation
- High‑throughput scoring at machine speed
- Reduced compute consumption
- Consistent correctness and grounding checks
- No dependency on human annotation pipelines
RLSF is engineered to integrate directly into Microsoft‑style training and evaluation architectures.
- Semantic Engineering Depth
Intellisophic’s platform is built on:
- Orthogonal Corpus Indexing (OCI)
- A semantic index spanning billions of sentences
- W3C‑compliant ontology structures
- Enterprise metadata alignment
- Support for hybrid and multi‑cloud data estates
This foundation enables RLSF to deliver explainable, auditable, and repeatable evaluation—critical for enterprise and national‑security environments.
- National‑Security‑Grade Validation
Intellisophic’s semantic technology has been validated in scenarios requiring:
- High assurance
- Precision under operational pressure
- Rapid analysis of large, complex datasets
- Strong governance and auditability
This history demonstrates the platform’s suitability for Microsoft’s internal AI quality, grounding, and compliance needs.
- A 50+ Engineer Dev‑Ops Organization
Intellisophic maintains a dev‑ops and engineering team of more than 50 semantic engineers, providing:
- Senior engineering capacity
- Enterprise‑grade software development
- Co‑development with customer engineering teams
- Integration with Azure AI and Microsoft 365 ecosystems
- Long‑term engineering partnership structures
This gives Intellisophic a top‑tier engineering arm capable of delivering Microsoft‑level support, implementation, and ongoing technical stewardship.
Executive Summary
Intellisophic combines a world‑class semantic AI platform with a 50+ engineer dev‑ops organization operating at Microsoft‑grade maturity. RLSF delivers deterministic semantic feedback that reduces compute, improves AI quality, and supports enterprise and government‑grade deployments. The engineering team is equipped to support Microsoft‑scale integration and internal AI workflows.
It’s concise, enterprise‑aligned, and tuned for internal Microsoft audiences who think in terms of grounding, safety, compliance, and multimodal reliability.
FACT SHEET #4
Intellisophic Image Ontology: Multimodal Grounding for Microsoft‑Grade AI
Overview
Intellisophic maintains a licensed, concept‑indexed image ontology that provides Microsoft with a governed visual foundation for multimodal AI. When paired with RLSF, it forms a complete semantic quality layer across text, image, and video—a capability frontier labs increasingly require and cannot build internally.
This image layer directly addresses Microsoft’s needs in Copilot, Azure AI, and regulated‑domain workloads.
- A Semantic Foundation, Not Just an Image Set
The image ontology transforms visual data from raw pixels into governed knowledge objects:
- Each image is tied to a concept
- Each concept sits in a W3C‑aligned ontology
- Each link carries license provenance
- Each concept aligns across text, speech, and image modalities
This elevates RLSF from a training‑loop optimization to a full semantic infrastructure layer.
- Solves Microsoft’s Known Frontier‑Lab Pain Points
Microsoft’s internal teams routinely encounter:
- Licensing uncertainty
- Dataset provenance gaps
- Multimodal hallucinations
- Medical/scientific image restrictions
- Safety and compliance review delays
The Intellisophic image ontology directly addresses these issues by providing:
- Clean, auditable image provenance
- Concept‑level grounding
- Cross‑modal consistency
- Safe coverage of regulated domains
This signals to Microsoft engineering teams:
“We understand the real problems you face at scale.”
- Positions Intellisophic as a Top‑Tier Semantic Engineering Partner
The image ontology showcases the depth of Intellisophic’s engineering capability:
- A dev‑ops organization of 50+ semantic engineers
- A licensed, concept‑indexed image base
- A W3C‑aligned semantic ontology
- A semantic pipeline validated in national‑security contexts
This moves Intellisophic out of the “vendor” category and into infrastructure partner territory.
- Provides Microsoft a Multimodal Safety & Grounding Story
Microsoft is under pressure to deliver:
- Lower hallucination rates
- Stronger grounding
- Clear licensing compliance
- Support for regulated domains
- Unified multimodal reasoning
RLSF + the image ontology gives Microsoft a single, coherent grounding layer across modalities.
This is especially relevant for:
- Copilot image understanding
- Azure AI vision models
- Medical/scientific/educational scenarios
- Government and enterprise compliance
- Differentiates Intellisophic in a Way No RLHF Vendor Can Match
Other vendors offer:
- Human raters
- Preference data
- Annotation labor
Intellisophic offers:
- Semantic correctness
- Licensed multimodal grounding
- Deterministic evaluation
- A governed knowledge base
This is a fundamentally different category—one aligned with Microsoft’s long‑term AI strategy.
- The Combined Value: RLSF + Image Ontology
RLSF alone:
Replace human feedback with semantic scoring.
RLSF + Image Ontology:
Provide Microsoft with a licensed, explainable, multimodal semantic foundation that reduces compute, eliminates legal risk, and unlocks regulated‑domain AI.
This combined story resonates strongly with Microsoft leadership, safety teams, and frontier‑lab engineering groups.
Executive Summary
The Intellisophic image ontology transforms RLSF into a full multimodal semantic infrastructure layer. It gives Microsoft a governed, licensed, concept‑indexed visual foundation that strengthens grounding, reduces legal risk, and supports enterprise and regulated‑domain AI. This capability is unique in the market and directly aligned with Microsoft’s internal AI roadmap.
Fact Sheet 5 – Enterprise Safety Architecture & Quality Assurance for Microsoft
Purpose:
Enable Intellisophic Sales Engineering to clearly articulate to Microsoft how SAM‑1, Semantic Ground Truth, and RLSF deliver enterprise‑grade quality assurance and safety that directly strengthens Microsoft’s AI ecosystem.
This version is optimized for SE-to-Microsoft conversations, partner alignment discussions, and technical validation talks with Microsoft engineering, product, and Copilot teams.
How Intellisophic’s Semantic Ground Truth + RLSF Strengthen Microsoft’s AI Quality, Reliability & Safety
1. Purpose (Sales Engineering Mission)
This fact sheet equips Intellisophic Sales Engineers to:
Explain how Intellisophic’s technology delivers verifiable, auditable, enterprise‑grade quality assurance.
Communicate why these assurances matter specifically to Microsoft AI, Copilot, Azure OpenAI, and enterprise customers.
Position Intellisophic as a strategic semantic partner that strengthens Microsoft’s Responsible AI and Enterprise Safety Architecture.
2. The Core Problem Microsoft Faces: RLHF Safety & QA Gaps
Microsoft’s large models—like all major foundation models—inherit limitations from human‑labeled RLHF:
RLHF is subjective (based on annotator “feel” – not fact).
RLHF is inconsistent (varies by geography, workforce quality, and cultural bias).
RLHF has no provenance (cannot show where a label’s truth came from).
RLHF fails long‑tail coverage (humans can’t label rare domain topics reliably).
RLHF breaks under stress (easy to jailbreak, spoof, or mislead).
RLHF creates unverifiable safety behaviors (human opinions cannot be audited).
These weaknesses impact Copilot reliability, industry‑specific workloads, regulated environments, and Microsoft’s commitments to safety, accuracy, and trustworthiness.
3. Intellisophic + RLSF: Enterprise Safety Architecture Alignment
Intellisophic’s technology plugs directly into Microsoft’s safety vision by providing semantic truth, verifiable facts, and correctness‑driven alignment.
Enterprise Safety Architecture = Three Reinforcing Layers
Layer 1 — Semantic Ground Truth (SAM‑1)
SAM‑1 provides a verifiable knowledge backbone, built from:
Trusted sources (textbooks, encyclopedias, vetted reference material)
Expert‑validated ontologies
Billions of subject–predicate–object factual triples
Domain-scale concept coverage (millions of concepts)
Semantic signatures for polysemy and context resolution
Value to Microsoft:
SAM‑1 gives Microsoft a ground‑truth database that can validate or reject LLM outputs in real time—ensuring accuracy and reducing hallucinations.
This directly strengthens Microsoft’s:
Content filtering
Safety classifiers
Copilot grounding stack
Responsible AI evaluation pipelines
Regulated industry positioning
Layer 2 — RLSF (Reinforcement Learning Semantic Feedback)
RLSF replaces human preference judgments with symbolic, semantic, correctness‑based feedback from SAM‑1.
RLSF uses:
Logical constraint solvers
Provers and validation engines
Fact‑checking mechanisms
Semantic scoring of correctness
Objective reward functions driven by truth
Value to Microsoft:
Produces objectively correct outputs, rather than “likely” outputs
Dramatically reduces jailbreak exposure
Improves model reliability in regulated domains
Creates predictable safety boundaries
Ensures deep accuracy across long‑tail, expert‑level knowledge
RLSF is the semantic equivalent of “unit testing for alignment”—and Microsoft deeply values this approach.
Layer 3 — Enterprise Controls & Governance
Intellisophic’s semantic stack complements Microsoft’s enterprise safety controls:
Purview DLP
Content Safety
Responsible AI tools
Copilot system messages
Entra ID & Zero Trust
SFI compliance
Industry‑specific regulatory mappings
Value to Microsoft:
These controls become stronger and more reliable when grounded in SAM‑1’s authoritative facts and RLSF’s correctness‑based alignment.
Intellisophic provides the semantic enforcement layer Microsoft currently lacks.
4. Quality Assurance: What Intellisophic Provides Microsoft
A. Independent Verification & Auditable QA
SAM‑1 ensures every concept, relation, and fact is:
Source‑anchored
Expert‑validated
Provenance‑tracked
Fully auditable
Microsoft gains alignment that is defensible and inspectable, not opinion-driven.
B. Automated Misinformation Suppression
By checking model outputs against verified facts, SAM‑1 prevents:
AI hallucinations
Fabricated claims
Incorrect domain reasoning
Subtle semantic inconsistencies
This is crucial for Microsoft as Copilot enters high‑trust workflows (legal, medical, finance).
C. Long‑Tail Domain Coverage
SAM‑1’s depth gives Microsoft:
Rich regulated‑industry terminology
Fine‑grained technical knowledge
Rare or specialized concept comprehension
Lower extension and customization costs
This directly enhances Microsoft’s vertical Copilot offerings.
D. Semantic Disambiguation & Precision
SAM‑1’s semantic signatures solve ambiguity natively:
Distinguishes homonyms (e.g., “Mercury” the element vs planet)
Prevents misclassification
Ensures correct contextual understanding
For Microsoft, this increases Copilot’s correctness rate—especially in complex domains.
E. Safety That Stands Up to Audit & Regulation
Because facts and semantic rules are explicit—not emergent—Microsoft can:
Show regulators how decisions were made
Validate outputs with evidence
Defend safety claims with traceability
This aligns with Microsoft’s compliance requirements across FSI, healthcare, energy, and government.
5. Competitive Advantage for Microsoft When Using Intellisophic
Capability
RLHF Vendors
Intellisophic + RLSF
Truth Verification
No
Yes
Provenance & Audit Trail
No
Yes
Long‑Tail Domain Knowledge
Weak
Strong
Safety Under Stress
Weak
Strong
Semantic Understanding
Limited
Deep
Jailbreak Resistance
Low
High
Regulatory Readiness
Moderate
Strong
This is exactly the differentiation Microsoft needs as Copilot expands into mission‑critical workloads.
6. SE Summary Pitch (for Microsoft Conversations)
“Intellisophic provides Microsoft with enterprise‑grade quality assurance by giving models a real semantic ground truth and reinforcement signals based on correctness, not crowd judgments.
This reduces hallucination, strengthens accuracy, improves safety, and makes alignment auditable—directly enhancing Microsoft Copilot and Responsible AI commitments.”
Intellisophic 