docs: restructure nav — drop FAQ/Changelog tabs, add API Reference tab

Remove the standalone FAQ and Changelog top-level tabs. FAQ and
Community pages move into the Overview tab as their own groups
(still fully reachable, just relocated). Changelog was only an
external link to GitHub releases and had no pages of its own.

Split the API reference pages (reference/*) out of the Modules tab
into a new, dedicated API Reference tab, so Modules now holds only
the conceptual guides and API Reference holds every module's class
and function documentation.

docs/docs.json

@@ -2,7 +2,7 @@
   "$schema": "https://mintlify.com/docs.json",
   "theme": "mint",
   "name": "Semantica",
-  "description": "The Context and Semantic Layer for AI in High-Stakes Domains — Context Graphs · Decision Intelligence · Full Provenance",
+  "description": "The Accountability and Context Layer for AI — Context Graphs · Decision Intelligence · Full Provenance",
   "colors": {
     "primary": "#10B981",
     "light": "#10B981",
@@ -43,7 +43,7 @@
     "raiseIssue": true
   },
   "metadata": {
-    "og:title": "Semantica — Context & Semantic Layer for AI in High-Stakes Domains",
+    "og:title": "Semantica — Accountability & Context Layer for AI",
     "og:description": "Build explainable, auditable knowledge graphs with full provenance. Open source. MIT licensed.",
     "og:image": "/assets/img/semantica-logo.png",
     "twitter:card": "summary_large_image",

docs/index.md

@@ -1,87 +1,97 @@
 ---
 title: "Semantica"
-description: "The Context and Semantic Layer for AI in High-Stakes Domains: Context Graphs · Decision Intelligence · Full Provenance"
+description: "The Accountability and Context Layer for AI: Context Graphs · Decision Intelligence · Full Provenance"
 ---
 
 ```bash
 pip install semantica
 ```
 
-Most AI agents store embeddings, not meaning. They can't say why a fact was recalled, where it came from, or what led to a decision. In healthcare, finance, legal, and government, that lack of a traceable record blocks production deployment.
+Your AI agent just made a decision. Now someone needs to explain it.
 
-Semantica is the context and semantic layer for AI in high-stakes domains, sitting beneath your existing agent framework. It doesn't replace LangChain or LlamaIndex; it makes their outputs traceable.
+*What did it know at the time? Which facts shaped the outcome? Where did those facts come from? Has it made the same call before: and did that go well?*
+
+If your stack can't answer those questions with a traceable record, you have a gap. Not a capability gap: an **accountability gap**. It's the reason AI hasn't landed at scale in healthcare, finance, legal, and government. And it's why teams building for those markets keep rebuilding the same guardrails from scratch.
+
+**Semantica closes that gap.** It's the context and accountability layer that sits beneath your existing agent framework: not a replacement for LangChain or LlamaIndex, but the infrastructure that makes their outputs trustworthy.
 
 
-## What Most AI Stacks Are Missing
+## The Problem Every Production AI Team Hits
 
-**No memory structure.** Agents store embeddings, not meaning.
+Powerful agents aren't automatically trustworthy ones. Five structural blind spots make modern AI systems impossible to deploy in regulated environments:
+
+**No memory structure** — agents store embeddings, not meaning
 - No way to ask *why* a fact was recalled
 - No link from a recalled fact back to its source document
 - Context is a black box that resets on every run
 
-**No decision trail.** Agents act continuously but record nothing.
+**No decision trail** — agents act continuously but record nothing
 - No history to hand to a regulator or auditor
 - No way to replay or reproduce a past decision
 - Debugging means re-running, not reviewing
 
-**No provenance.** Outputs can't be traced to source facts.
-- A hard compliance blocker in healthcare, finance, and legal
+**No provenance** — outputs can't be traced to source facts
+- In healthcare, finance, and legal: this is a hard compliance blocker
 - No lineage from inference back to the original document
-- No way to demonstrate what the agent actually relied on
+- Impossible to demonstrate what the agent actually relied on
 
-**No reasoning transparency.** Black-box answers with no explanation.
-- No way to validate the reasoning path
-- No way to contest a specific conclusion
+**No reasoning transparency** — black-box answers with no explanation
+- Impossible to validate the reasoning path
+- Impossible to contest a specific conclusion
 - No basis for improving or correcting future behavior
 
-**No conflict detection.** Contradictory facts silently coexist in vector stores.
+**No conflict detection** — contradictory facts silently coexist in vector stores
 - No detection when two sources disagree
 - Outputs become inconsistent and unpredictable over time
 - Silent failures compound as the knowledge base grows
 
+<Note>
+  These aren't edge cases. They're why enterprise AI pilots stall: and why your compliance team keeps saying *not yet*.
+</Note>
+
 
 ## What Semantica Adds to Your Stack
 
-Semantica gives every agent the infrastructure it needs to be accountable, and it drops into an existing setup in minutes.
+Semantica gives every agent the infrastructure it needs to be accountable. Drop it into your existing setup in minutes:
 
-**Context Graphs.** A structured, queryable graph of everything your agent knows, decides, and reasons about.
-- Persistent across agent runs, with no context loss between sessions
+**Context Graphs** — a structured, queryable graph of everything your agent knows, decides, and reasons about
+- Persistent across agent runs: no context loss between sessions
 - Queryable with SPARQL and full graph algorithms
 - Temporal model with `valid_from` / `valid_until` on nodes and edges
 - Point-in-time snapshots of the full knowledge state
 
-**Decision Intelligence.** Every decision is a first-class object in your system.
+**Decision Intelligence** — every decision is a first-class object in your system
 - `record_decision()` captures full lifecycle and causal chain
 - Hybrid precedent search over past decisions for consistency
 - `analyze_decision_impact()` shows downstream consequences
 - Causal chain visualization from trigger to outcome
 
-**Full Provenance.** Every fact links to its source document and ingestion event.
+**Full Provenance** — every fact links to its source document and ingestion event
 - W3C PROV-O compliant lineage across all modules
 - Full traceability from raw input to final inference
 - `recorded_at` stamping with OWL-Time export
 - Audit-ready for HIPAA, SOX, GDPR, FDA 21 CFR Part 11
 
-**Reasoning Engines.** Explainable reasoning paths, not black boxes.
+**Reasoning Engines** — explainable reasoning paths, not black boxes
 - Forward chaining, Rete, deductive, abductive
 - SPARQL query-based inference over RDF graphs
 - Datalog with recursive Horn clause rules
 - Every conclusion backed by a traceable derivation path
 
-**Temporal Intelligence.** Your graph knows not just *what*, but *when*.
-- Allen interval algebra covering all 13 temporal relations
+**Temporal Intelligence** — your graph knows not just *what*, but *when*
+- Allen interval algebra: all 13 temporal relations
 - Point-in-time queries over historical graph states
 - Temporal provenance stamping on every fact
 - OWL-Time export for standards-compliant archiving
 
-**Ontology Hub.** Full ontology lifecycle in the browser.
+**Ontology Hub** — full ontology lifecycle in the browser
 - Visual editor for schema design and editing
 - SHACL Studio for constraint authoring and validation
 - Alignment authoring across multiple ontologies
 - Health dashboard and version control built in
 
 <Tip>
-  Works alongside any LLM provider and any agent framework. Add it to an existing stack without changing your architecture.
+  Works alongside any LLM provider and any agent framework: add it to an existing stack without changing your architecture.
 </Tip>
 
 <img src="/assets/img/diagrams/architecture-overview.svg" alt="Semantica four-layer architecture: Ingestion → Processing → Intelligence → Application" style={{ width: '100%', borderRadius: '12px', margin: '24px 0' }} />
@@ -175,17 +185,17 @@ decision_id = context.record_decision(
 
 </CodeGroup>
 
-- [Full Quickstart](/quickstart): step-by-step pipeline walkthrough
-- [Cookbook](/cookbook): 40+ real-world Jupyter notebooks
-- [Join Discord](https://discord.gg/sV34vps5hH): community chat and support
+- [Full Quickstart](/quickstart) — Step-by-step pipeline walkthrough
+- [Cookbook](/cookbook) — 40+ real-world Jupyter notebooks
+- [Join Discord](https://discord.gg/sV34vps5hH) — Community chat and support
 
 
-## Industry Use Cases
+## Built for Where Mistakes Have Consequences
 
-Semantica is used in domains where every decision must be explainable and every fact must be traceable.
+Semantica was designed for domains where every decision must be explainable and every fact must be traceable.
 
 <Warning>
-  **This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model. Its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. See [Core Concepts](/concepts) for the full scope note.
+  **This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. See [Core Concepts](/concepts) for the full scope note.
 </Warning>
 
 **Healthcare & Life Sciences**
@@ -246,7 +256,7 @@ Semantica is used in domains where every decision must be explainable and every
     - Knowledge graphs vs. vector stores: when to use each
     - What GraphRAG is and how Semantica implements it
     - How provenance and decision tracking work together
-    - The context and semantic layer architecture
+    - The accountability layer architecture
   </Step>
   <Step title="Go deep on any module">
     Every module has a dedicated [reference page](/reference/context) with:
@@ -256,12 +266,12 @@ Semantica is used in domains where every decision must be explainable and every
   </Step>
 </Steps>
 
-- [Installation](/installation): get Semantica installed in under a minute
-- [Quickstart](/quickstart): build a complete knowledge graph pipeline in 5 minutes
-- [Core Concepts](/concepts): the mental model behind the API
-- [API Reference](/reference/context): exact module, class, and method details
-- [Cookbook](/cookbook): domain notebooks for real-world use cases
-- [Changelog](https://github.com/semantica-agi/semantica/releases): release history
+- [Installation](/installation) — Get Semantica installed in under a minute
+- [Quickstart](/quickstart) — Build a complete knowledge graph pipeline in 5 minutes
+- [Core Concepts](/concepts) — The mental model behind the API
+- [API Reference](/reference/context) — Exact module, class, and method details
+- [Cookbook](/cookbook) — Domain notebooks for real-world use cases
+- [Changelog](https://github.com/semantica-agi/semantica/releases) — Release history
 
 
 ## Full Capabilities
@@ -384,20 +394,20 @@ Semantica is used in domains where every decision must be explainable and every
 
 ## Why Semantica?
 
-**Open Source, MIT.** No vendor lock-in, no paywalled features.
+**Open Source, MIT** — No vendor lock-in. No paywalled features.
 - Full source available on GitHub
 - Every line auditable by your security team
 - Fork, extend, and self-host with no restrictions
 - No telemetry, no usage reporting
 
-**Production Ready.** Built for teams that can't afford surprises.
+**Production Ready** — Built for teams that can't afford surprises.
 - 1,000+ passing tests with full regression coverage
 - `PipelineValidator` catches configuration errors at startup
 - `FailureHandler` with exponential backoff and dead-letter queues
-- Ongoing security hardening, with fixes shipped in every release ([CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md))
+- Ongoing security hardening: fixes shipped in every release ([CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md))
 
-**Modular by Design.** Import only what you need.
+**Modular by Design** — Import only what you need.
 - Use `NERExtractor` without a graph store
 - Use `ContextGraph` without vector storage
 - Every component independently swappable and testable
-- No framework lock-in, and works with any agent stack
+- No framework lock-in: works with any agent stack