One Language for People, Data & Machines
AI Knowledge Standardization is how we make all of your content—docs, tickets, policies, code, schemas, emails, chats—speak the same language so AI can retrieve accurately, reason consistently, and answer with evidence.
SolveForce builds a language-first pipeline (ontology → taxonomy → labels → links → embeddings → guarded RAG) so your LLMs stop guessing and start citing.
Where this fits in the SolveForce model:
🧠 Intelligence → Unified Intelligence • 🤖 Decision Layer → SolveForce AI
🏛️ Foundation → Primacy of Language • 🔎 Linguistic map → Language of Code Ontology • 📚 Index → SolveForce Codex
🗄️ Data fabric → Data Warehouse / Lakes • ETL / ELT • Data Governance / Lineage • Master Data Management • Vector Databases & RAG
🎯 Outcomes (Why standardize first—then scale AI)
- Answer precision ↑ — definitions resolve to one canonical term, synonyms map cleanly.
- Hallucinations ↓ — retrieval is label- and evidence-constrained; unknowns trigger honest refusal.
- Latency ↓ — smaller, better-curated indices per domain/label cut retrieval time.
- Trust ↑ — every response includes citations and provenance.
- Operating cost ↓ — fewer retries, less prompt glue, lower compute for larger corpora.
🧭 Scope (What we normalize)
- Sources: policies/SOPs, architecture docs, schemas, runbooks, tickets, chat threads, emails, code repos, API specs, logs, meeting notes, media transcripts.
- Objects: terms, acronyms, entities, roles, systems, products, controls, controls-to-evidence, data classes (PII/PHI/PAN), jurisdictions, SLAs/SLOs.
- Audiences: engineering, SecOps, IT, finance, legal, support, field ops—one glossary, many views.
🧱 Building Blocks (Spelled out)
- Ontology (what exists): entities, attributes, and relations (e.g., Service → uses → Key, guarded by → Policy). → Language of Code Ontology
- Taxonomy (how it’s grouped): SolveForce Codex (Grammar/Syntax/Semantics/Pragmatics) with domain facets. → SolveForce Codex
- Controlled Vocabulary: canonical names, synonyms, acronym expansions, disambiguation rules.
- Entity Resolution & Fingerprinting: de-dupe duplicates across systems; persistent IDs and doc hashes.
- Labels: sensitivity (Public/Internal/Confidential/Restricted), domain, product, lifecycle, jurisdiction, evidence class.
- Provenance: source URLs/paths, commit IDs, authors, timestamps, retention.
- Guardrails: DLP/PII filters, access scopes, denial reasons, refusal templates. → DLP • IAM / SSO / MFA
🏗️ Architecture (Ingest → Normalize → Label → Link → Embed → Retrieve → Cite)
- Ingest: connectors (docs, code, tickets, email, chat, wiki); OCR for scans.
- Normalize: clean HTML/markdown; split into semantic chunks (headings, sections, fields).
- Classify & Label: apply vocabulary, sensitivity, domain, jurisdiction; detect PII/PHI/PAN. → Data Governance / Lineage • DLP
- Link & Resolve: map terms to canonical entries; build cross-refs to Codex items and entity IDs.
- Embed & Index: generate domain-specific embeddings; shard indices per label/domain in a Vector DB. → Vector Databases & RAG
- Guarded Retrieval (RAG): query → filter by label/scope → retrieve K chunks with provenance → rerank with ontology signals.
- Generate & Cite: compose a grounded answer with inline citations; if insufficient, refuse with reasons.
- Observe & Tune: store Q/A, votes, drift metrics; update glossary; republish embeddings.
Guarded RAG = smaller, safer search space + ontology hints + hard access filters → reproducible answers with citations.
🔒 Policy & Controls (Zero-Trust Retrieval)
- Access-first: retrieval filters by user role, group, region, sensitivity before embeddings. → IAM / SSO / MFA
- PII/PHI gating: redact or mask on retrieval; restrict generation to read-only or refuse. → DLP
- Jurisdictional split: region-bound indices; cross-region queries by policy only.
- Provenance-required: no source → no claim; enforce “cite or refuse”.
- Refusal templates: standardized, honest “not enough evidence” responses.
📐 SLO Guardrails (Make quality measurable)
| SLO / KPI | Target (Recommended) | Why it matters |
|---|---|---|
| Definition Coverage (first-use terms linked) | ≥ 95% | Fewer ambiguous answers |
| Term Resolution Accuracy (human eval) | ≥ 97% | Canonical mapping confidence |
| Answer Precision@K (gold Q/A) | ≥ 92–95% | Less guesswork |
| Citation Coverage (answers with sources) | = 100% | Trust & auditability |
| Hallucination Rate (no-source claims) | ≤ 1–2% | Safety bar |
| Ingest→Label Latency (p95) | ≤ 5–15 min per doc | Freshness |
| Refusal Correctness | ≥ 98% | Honest “don’t know” when needed |
🛠️ Implementation Blueprint (No-surprise rollout)
- Inventory & Prioritize: pick 3–5 high-value domains (e.g., cloud, security, product, support).
- Glossary Sprint: extract synonyms/acronyms; define canonical names and disambiguations; commit to Codex. → SolveForce Codex
- Labeling Policy: sensitivity, lifecycle, jurisdiction; DLP rules and access scopes. → DLP • IAM / SSO / MFA
- Pipelines: build ingest/normalize; chunking rules; term linker; labeler; provenance capture. → ETL / ELT
- Indices: stand up per-domain, per-label vector indices + keyword fallback; configure rerankers. → Vector Databases & RAG
- Guarded RAG: implement filter→retrieve→rerank→cite with refusal logic.
- Benchmarks: create gold Q/A; measure precision@K, refusal correctness, latency; set SLO alerts.
- Ops & Drift: weekly glossary updates, synonym additions, dead-link fixes, retrain/rerank where drift > threshold.
- Publish & Train: quick style guide for SMEs; “how to write definitional first” to improve future content.
📊 Metrics That Matter
- Precision/Recall@K by domain and label
- Hallucination & Refusal rates (should move opposite directions)
- Definition Coverage (first-use term links)
- Time-to-freshness (ingest→label→index)
- Reproducibility (same answer/cites over time)
- User votes / CSAT on answers & citations
- Escalation rate to humans (goal: steady ↓)
Dashboards live alongside SIEM/SOAR and analytics for one view of quality & safety. → SIEM / SOAR
🧩 Integrations (Make it part of the system)
- Data Fabric: warehouse/lake, ETL/ELT, catalog, lineage, PII scanners. → Data Warehouse / Lakes • ETL / ELT • Data Governance / Lineage
- Access & Safety: IAM/SSO/MFA, DLP, ZTNA; role- and region-aware retrieval. → IAM / SSO / MFA • DLP • ZTNA
- AI Runtime: embeddings/image/text models, rerankers, vector DBs, caching, prompt macros. → SolveForce AI • Vector Databases & RAG
- Evidence: provenance logs, citation store, refusal ledger for audits. → Knowledge Hub
🏭 Industry Examples
- Healthcare — unify clinical vocabularies (ICD/CPT/HL7) and local terms; reduce PHI exposure; force cite/consent.
- Finance — map tickers/symbols/GL accounts/regulatory terms; jurisdiction-bound retrieval; redact PII/PAN by policy.
- Government — align to NIST/FIPS/FedRAMP glossaries; FOIA-safe retrieval with provenance; regional data indices.
- Enterprise IT — collapse vendor synonyms; link runbooks → assets; “definitional first” style in wiki -> fewer tickets.
🔄 Where AI Knowledge Standardization Fits (Recursive View)
1) Grammar — content rides Connectivity & the Networks & Data Centers fabric.
2) Syntax — pipelines & storage in Cloud (warehouse, lake, vector DB).
3) Semantics — Cybersecurity enforces access, DLP, and jurisdiction.
4) Pragmatics — SolveForce AI retrieves with citations, refuses when unknown, and learns from feedback.
5) Foundation — Primacy of Language + Language of Code Ontology keep terms coherent.
6) Map — indexed through the SolveForce Codex & Knowledge Hub.
📞 Launch AI That Knows Your Words (and Proves It)
Related pages:
SolveForce AI • Unified Intelligence • Language of Code Ontology • SolveForce Codex • Vector Databases & RAG • Data Governance / Lineage • Master Data Management • Data Warehouse / Lakes • ETL / ELT • DLP • IAM / SSO / MFA • Knowledge Hub