diff --git a/docs/features/ai_verktoy.md b/docs/features/ai_verktoy.md
index 9aaf9e6..ac43abd 100644
--- a/docs/features/ai_verktoy.md
+++ b/docs/features/ai_verktoy.md
@@ -225,7 +225,7 @@ Flere AI-verktøy i serie: dra output fra "Oversett" videre til
 ### Fase A: Grunnleggende verktøy-panel
 - [x] AI-preset node-type (`node_kind: 'ai_preset'`) + metadata-skjema
 - [x] Standard-presets som seed-data (rens tekst, korrektør, oppsummering osv.)
-- [ ] `POST /intentions/ai_process` endepunkt i maskinrommet
+- [x] `POST /intentions/ai_process` endepunkt i maskinrommet
 - [ ] Verktøy-panel UI med prompt-velger og modell-indikator
 - [ ] Jobbkø-integrasjon med AI Gateway
 
diff --git a/maskinrommet/src/ai_process.rs b/maskinrommet/src/ai_process.rs
new file mode 100644
index 0000000..d038231
--- /dev/null
+++ b/maskinrommet/src/ai_process.rs
@@ -0,0 +1,314 @@
+// AI-prosessering — hent kilde-content + preset-prompt, kall AI Gateway.
+//
+// Jobbtype: "ai_process"
+// Payload: {
+//   "source_node_id": "<uuid>",
+//   "ai_preset_id": "<uuid>",
+//   "direction": "node_to_tool" | "tool_to_node",
+//   "requested_by": "<uuid>"
+// }
+//
+// Flyten:
+// 1. Hent kilde-node content fra PG
+// 2. Hent AI-preset prompt + modellprofil fra PG
+// 3. Map modellprofil → LiteLLM-alias (flash → sidelinja/rutine, standard → sidelinja/resonering)
+// 4. Send til AI Gateway (LiteLLM)
+// 5. Logg forbruk i ai_usage_log
+// 6. Returner AI-output (direction-logikk implementeres i oppgave 18.3)
+//
+// Ref: docs/features/ai_verktoy.md, docs/infra/ai_gateway.md
+
+use serde::{Deserialize, Serialize};
+use sqlx::PgPool;
+use uuid::Uuid;
+
+use crate::jobs::JobRow;
+use crate::resource_usage;
+
+#[derive(sqlx::FromRow)]
+struct SourceNodeRow {
+    content: Option<String>,
+    #[allow(dead_code)] // Brukes i oppgave 18.3 (direction-logikk)
+    title: Option<String>,
+    #[allow(dead_code)] // Brukes i oppgave 18.3 (direction-logikk)
+    node_kind: String,
+}
+
+#[derive(sqlx::FromRow)]
+struct PresetRow {
+    title: Option<String>,
+    metadata: Option<serde_json::Value>,
+}
+
+/// OpenAI-kompatibel chat completion request.
+#[derive(Serialize)]
+struct ChatRequest {
+    model: String,
+    messages: Vec<ChatMessage>,
+    temperature: f32,
+}
+
+#[derive(Serialize)]
+struct ChatMessage {
+    role: String,
+    content: String,
+}
+
+/// OpenAI-kompatibel chat completion response.
+#[derive(Deserialize)]
+struct ChatResponse {
+    choices: Vec<Choice>,
+    #[serde(default)]
+    usage: Option<UsageInfo>,
+    #[serde(default)]
+    model: Option<String>,
+}
+
+#[derive(Deserialize, Clone)]
+struct UsageInfo {
+    #[serde(default)]
+    prompt_tokens: i64,
+    #[serde(default)]
+    completion_tokens: i64,
+}
+
+#[derive(Deserialize)]
+struct Choice {
+    message: MessageContent,
+}
+
+#[derive(Deserialize)]
+struct MessageContent {
+    content: Option<String>,
+}
+
+/// Mapper modellprofil til LiteLLM-alias.
+/// Ref: docs/features/ai_verktoy.md § 4, docs/infra/ai_gateway.md § 3.4
+fn model_profile_to_alias(profile: &str) -> &'static str {
+    match profile {
+        "flash" => "sidelinja/rutine",
+        "standard" => "sidelinja/resonering",
+        _ => "sidelinja/rutine", // fallback til billigste
+    }
+}
+
+/// Håndterer ai_process-jobb.
+pub async fn handle_ai_process(
+    job: &JobRow,
+    db: &PgPool,
+) -> Result<serde_json::Value, String> {
+    let source_node_id: Uuid = job
+        .payload
+        .get("source_node_id")
+        .and_then(|v| v.as_str())
+        .and_then(|s| s.parse().ok())
+        .ok_or("Mangler source_node_id i payload")?;
+
+    let ai_preset_id: Uuid = job
+        .payload
+        .get("ai_preset_id")
+        .and_then(|v| v.as_str())
+        .and_then(|s| s.parse().ok())
+        .ok_or("Mangler ai_preset_id i payload")?;
+
+    let direction = job
+        .payload
+        .get("direction")
+        .and_then(|v| v.as_str())
+        .ok_or("Mangler direction i payload")?;
+
+    let requested_by: Uuid = job
+        .payload
+        .get("requested_by")
+        .and_then(|v| v.as_str())
+        .and_then(|s| s.parse().ok())
+        .ok_or("Mangler requested_by i payload")?;
+
+    // 1. Hent kilde-node
+    let source = sqlx::query_as::<_, SourceNodeRow>(
+        "SELECT content, title, node_kind FROM nodes WHERE id = $1",
+    )
+    .bind(source_node_id)
+    .fetch_optional(db)
+    .await
+    .map_err(|e| format!("PG-feil ved henting av kilde-node: {e}"))?
+    .ok_or("Kilde-node finnes ikke")?;
+
+    let source_content = source
+        .content
+        .filter(|c| !c.is_empty())
+        .ok_or("Kilde-noden har ikke innhold å behandle")?;
+
+    // 2. Hent AI-preset
+    let preset = sqlx::query_as::<_, PresetRow>(
+        "SELECT title, metadata FROM nodes WHERE id = $1 AND node_kind = 'ai_preset'",
+    )
+    .bind(ai_preset_id)
+    .fetch_optional(db)
+    .await
+    .map_err(|e| format!("PG-feil ved henting av AI-preset: {e}"))?
+    .ok_or("AI-preset finnes ikke")?;
+
+    let metadata = preset
+        .metadata
+        .ok_or("AI-preset mangler metadata")?;
+
+    let prompt = metadata
+        .get("prompt")
+        .and_then(|v| v.as_str())
+        .ok_or("AI-preset mangler prompt i metadata")?;
+
+    let model_profile = metadata
+        .get("model_profile")
+        .and_then(|v| v.as_str())
+        .unwrap_or("flash");
+
+    // 3. Map modellprofil → LiteLLM-alias
+    let model_alias = model_profile_to_alias(model_profile);
+
+    tracing::info!(
+        source_node_id = %source_node_id,
+        ai_preset_id = %ai_preset_id,
+        direction = %direction,
+        model_alias = %model_alias,
+        preset_title = ?preset.title,
+        source_content_len = source_content.len(),
+        "Starter AI-prosessering"
+    );
+
+    // 4. Kall AI Gateway
+    let (ai_output, usage, actual_model) =
+        call_ai_gateway(model_alias, prompt, &source_content).await?;
+
+    tracing::info!(
+        source_node_id = %source_node_id,
+        output_len = ai_output.len(),
+        actual_model = ?actual_model,
+        "AI-prosessering fullført"
+    );
+
+    // 5. Logg forbruk i ai_usage_log
+    let collection_id = resource_usage::find_collection_for_node(db, source_node_id).await;
+    let (tokens_in, tokens_out) = usage
+        .as_ref()
+        .map(|u| (u.prompt_tokens, u.completion_tokens))
+        .unwrap_or((0, 0));
+    let total_tokens = tokens_in + tokens_out;
+
+    // ai_usage_log — detaljert AI-forbrukslogg
+    if let Err(e) = sqlx::query(
+        r#"
+        INSERT INTO ai_usage_log
+            (collection_node_id, job_id, model_alias, model_actual,
+             prompt_tokens, completion_tokens, total_tokens, job_type)
+        VALUES ($1, $2, $3, $4, $5, $6, $7, 'ai_process')
+        "#,
+    )
+    .bind(collection_id)
+    .bind(job.id)
+    .bind(model_alias)
+    .bind(actual_model.as_deref())
+    .bind(tokens_in as i32)
+    .bind(tokens_out as i32)
+    .bind(total_tokens as i32)
+    .execute(db)
+    .await
+    {
+        tracing::warn!(error = %e, "Kunne ikke logge AI-forbruk i ai_usage_log");
+    }
+
+    // resource_usage_log — generell ressurslogging
+    if let Err(e) = resource_usage::log(
+        db,
+        source_node_id,
+        Some(requested_by),
+        collection_id,
+        "ai",
+        serde_json::json!({
+            "model_level": model_profile,
+            "model_id": actual_model.unwrap_or_else(|| "unknown".to_string()),
+            "model_alias": model_alias,
+            "tokens_in": tokens_in,
+            "tokens_out": tokens_out,
+            "job_type": "ai_process",
+            "preset_id": ai_preset_id.to_string(),
+            "direction": direction
+        }),
+    )
+    .await
+    {
+        tracing::warn!(error = %e, "Kunne ikke logge AI-ressursforbruk");
+    }
+
+    // 6. Returner resultat
+    // Direction-logikk (opprett ny node / oppdater eksisterende) implementeres i oppgave 18.3
+    Ok(serde_json::json!({
+        "status": "completed",
+        "source_node_id": source_node_id.to_string(),
+        "ai_preset_id": ai_preset_id.to_string(),
+        "direction": direction,
+        "ai_output": ai_output,
+        "tokens_in": tokens_in,
+        "tokens_out": tokens_out,
+        "total_tokens": total_tokens
+    }))
+}
+
+/// Kall AI Gateway (LiteLLM) for tekstbehandling.
+/// Returnerer (output_text, usage, actual_model_name).
+async fn call_ai_gateway(
+    model_alias: &str,
+    system_prompt: &str,
+    user_content: &str,
+) -> Result<(String, Option<UsageInfo>, Option<String>), String> {
+    let gateway_url = std::env::var("AI_GATEWAY_URL")
+        .unwrap_or_else(|_| "http://localhost:4000".to_string());
+    let api_key = std::env::var("LITELLM_MASTER_KEY").unwrap_or_default();
+
+    let request = ChatRequest {
+        model: model_alias.to_string(),
+        messages: vec![
+            ChatMessage {
+                role: "system".to_string(),
+                content: system_prompt.to_string(),
+            },
+            ChatMessage {
+                role: "user".to_string(),
+                content: user_content.to_string(),
+            },
+        ],
+        temperature: 0.3,
+    };
+
+    let client = reqwest::Client::new();
+    let url = format!("{gateway_url}/v1/chat/completions");
+
+    let resp = client
+        .post(&url)
+        .header("Authorization", format!("Bearer {api_key}"))
+        .header("Content-Type", "application/json")
+        .json(&request)
+        .timeout(std::time::Duration::from_secs(120))
+        .send()
+        .await
+        .map_err(|e| format!("AI Gateway-kall feilet: {e}"))?;
+
+    if !resp.status().is_success() {
+        let status = resp.status();
+        let body = resp.text().await.unwrap_or_default();
+        return Err(format!("AI Gateway returnerte {status}: {body}"));
+    }
+
+    let chat_resp: ChatResponse = resp
+        .json()
+        .await
+        .map_err(|e| format!("Kunne ikke parse AI Gateway-respons: {e}"))?;
+
+    let content = chat_resp
+        .choices
+        .first()
+        .and_then(|c| c.message.content.as_deref())
+        .ok_or("AI Gateway returnerte ingen content")?;
+
+    Ok((content.to_string(), chat_resp.usage, chat_resp.model))
+}
diff --git a/maskinrommet/src/intentions.rs b/maskinrommet/src/intentions.rs
index 3e1d3f7..fe94b33 100644
--- a/maskinrommet/src/intentions.rs
+++ b/maskinrommet/src/intentions.rs
@@ -3228,6 +3228,134 @@ pub async fn summarize(
     Ok(Json(SummarizeResponse { job_id }))
 }
 
+// =============================================================================
+// POST /intentions/ai_process — AI-prosessering via AI Gateway
+// =============================================================================
+
+#[derive(Deserialize)]
+pub struct AiProcessRequest {
+    /// Kilde-noden som skal prosesseres.
+    pub source_node_id: Uuid,
+    /// AI-preset som definerer prompt og modellprofil.
+    pub ai_preset_id: Uuid,
+    /// Retning: "node_to_tool" (opprett ny node) eller "tool_to_node" (modifiser in-place).
+    pub direction: String,
+}
+
+#[derive(Serialize)]
+pub struct AiProcessResponse {
+    pub job_id: Uuid,
+}
+
+/// POST /intentions/ai_process
+///
+/// Legger en `ai_process`-jobb i køen.
+/// AI-prosesseringen skjer asynkront — kilde-content sendes til AI Gateway
+/// med preset-prompt, og forbruk logges i ai_usage_log.
+///
+/// Direction-logikk (opprett ny node vs. oppdater eksisterende) implementeres
+/// i oppgave 18.3.
+///
+/// Ref: docs/features/ai_verktoy.md § 6.1
+pub async fn ai_process(
+    State(state): State<AppState>,
+    user: AuthUser,
+    Json(req): Json<AiProcessRequest>,
+) -> Result<Json<AiProcessResponse>, (StatusCode, Json<ErrorResponse>)> {
+    // Valider direction
+    if req.direction != "node_to_tool" && req.direction != "tool_to_node" {
+        return Err(bad_request(
+            "direction må være 'node_to_tool' eller 'tool_to_node'",
+        ));
+    }
+
+    // Sjekk at kilde-noden finnes
+    let source_exists: bool = sqlx::query_scalar::<_, bool>(
+        "SELECT EXISTS(SELECT 1 FROM nodes WHERE id = $1)",
+    )
+    .bind(req.source_node_id)
+    .fetch_one(&state.db)
+    .await
+    .map_err(|e| {
+        tracing::error!(error = %e, "PG-feil ved kilde-node-sjekk");
+        internal_error("Databasefeil")
+    })?;
+
+    if !source_exists {
+        return Err(bad_request("Kilde-node finnes ikke"));
+    }
+
+    // Sjekk at AI-preset finnes
+    let preset_exists: bool = sqlx::query_scalar::<_, bool>(
+        "SELECT EXISTS(SELECT 1 FROM nodes WHERE id = $1 AND node_kind = 'ai_preset')",
+    )
+    .bind(req.ai_preset_id)
+    .fetch_one(&state.db)
+    .await
+    .map_err(|e| {
+        tracing::error!(error = %e, "PG-feil ved preset-sjekk");
+        internal_error("Databasefeil")
+    })?;
+
+    if !preset_exists {
+        return Err(bad_request("AI-preset finnes ikke"));
+    }
+
+    // For tool_to_node-retning trengs skrivetilgang til kilde-noden
+    if req.direction == "tool_to_node" {
+        let can_modify = user_can_modify_node(&state.db, user.node_id, req.source_node_id)
+            .await
+            .map_err(|e| {
+                tracing::error!(error = %e, "PG-feil ved tilgangssjekk");
+                internal_error("Databasefeil")
+            })?;
+
+        if !can_modify {
+            return Err(forbidden(
+                "Ingen tilgang til å endre kilde-noden (tool_to_node krever skrivetilgang)",
+            ));
+        }
+    }
+
+    // Finn samlings-ID for kilde-noden (for prioritering)
+    let collection_id = crate::resource_usage::find_collection_for_node(
+        &state.db,
+        req.source_node_id,
+    )
+    .await;
+
+    let payload = serde_json::json!({
+        "source_node_id": req.source_node_id.to_string(),
+        "ai_preset_id": req.ai_preset_id.to_string(),
+        "direction": req.direction,
+        "requested_by": user.node_id.to_string()
+    });
+
+    let job_id = crate::jobs::enqueue(
+        &state.db,
+        "ai_process",
+        payload,
+        collection_id,
+        5, // Medium prioritet
+    )
+    .await
+    .map_err(|e| {
+        tracing::error!(error = %e, "Kunne ikke legge ai_process-jobb i kø");
+        internal_error("Kunne ikke starte AI-prosessering")
+    })?;
+
+    tracing::info!(
+        job_id = %job_id,
+        source_node_id = %req.source_node_id,
+        ai_preset_id = %req.ai_preset_id,
+        direction = %req.direction,
+        user = %user.node_id,
+        "ai_process-jobb lagt i kø"
+    );
+
+    Ok(Json(AiProcessResponse { job_id }))
+}
+
 // =============================================================================
 // POST /intentions/generate_tts — tekst-til-tale via ElevenLabs
 // =============================================================================
diff --git a/maskinrommet/src/jobs.rs b/maskinrommet/src/jobs.rs
index 7624762..8e13cda 100644
--- a/maskinrommet/src/jobs.rs
+++ b/maskinrommet/src/jobs.rs
@@ -16,6 +16,7 @@ use uuid::Uuid;
 
 use crate::agent;
 use crate::ai_edges;
+use crate::ai_process;
 use crate::audio;
 use crate::cas::CasStore;
 use crate::maintenance::MaintenanceState;
@@ -184,6 +185,9 @@ async fn dispatch(
         "audio_process" => {
             audio::handle_audio_process_job(job, db, stdb, cas).await
         }
+        "ai_process" => {
+            ai_process::handle_ai_process(job, db).await
+        }
         "render_article" => {
             handle_render_article(job, db, cas).await
         }
diff --git a/maskinrommet/src/main.rs b/maskinrommet/src/main.rs
index 9c9c789..e1f302a 100644
--- a/maskinrommet/src/main.rs
+++ b/maskinrommet/src/main.rs
@@ -1,6 +1,7 @@
 pub mod agent;
 pub mod ai_admin;
 pub mod ai_edges;
+pub mod ai_process;
 pub mod audio;
 pub mod bandwidth;
 mod auth;
@@ -202,6 +203,7 @@ async fn main() {
         .route("/intentions/retranscribe", post(intentions::retranscribe))
         .route("/intentions/resolve_retranscription", post(intentions::resolve_retranscription))
         .route("/intentions/summarize", post(intentions::summarize))
+        .route("/intentions/ai_process", post(intentions::ai_process))
         .route("/intentions/generate_tts", post(intentions::generate_tts))
         .route("/intentions/join_communication", post(intentions::join_communication))
         .route("/intentions/leave_communication", post(intentions::leave_communication))
diff --git a/tasks.md b/tasks.md
index e4f35b5..48fcfa8 100644
--- a/tasks.md
+++ b/tasks.md
@@ -202,8 +202,7 @@ Ref: Kodegjennomgang av `b4c4bb8` (Lydstudio: lydredigering via FFmpeg).
 Ref: `docs/features/ai_verktoy.md`, `docs/retninger/arbeidsflaten.md`
 
 - [x] 18.1 AI-preset node-type: `node_kind: 'ai_preset'` med metadata (prompt, model_profile, category, icon, color). Maskinrommet validerer ved opprettelse. Seed standardprompter (rens tekst, korrektør, oppsummering, oversett, skriv om, trekk ut fakta, forenkle, endre tone).
-- [~] 18.2 AI-prosessering endepunkt: `POST /intentions/ai_process` med source_node_id, ai_preset_id, direction (node_to_tool / tool_to_node). Maskinrommet henter kilde-content og preset-prompt, mapper modellprofil → LiteLLM-alias, sender til AI Gateway. Logg forbruk i ai_usage_log.
-  > Påbegynt: 2026-03-18T06:14
+- [x] 18.2 AI-prosessering endepunkt: `POST /intentions/ai_process` med source_node_id, ai_preset_id, direction (node_to_tool / tool_to_node). Maskinrommet henter kilde-content og preset-prompt, mapper modellprofil → LiteLLM-alias, sender til AI Gateway. Logg forbruk i ai_usage_log.
 - [ ] 18.3 Direction-logikk: `tool_to_node` → lagre original som revisjon, oppdater node content. `node_to_tool` → opprett ny node med AI-output, opprett `derived_from`-edge til kilde + `processed_by`-edge til AI-preset.
 - [ ] 18.4 AI-verktøy panel (frontend): Svelte-komponent for arbeidsflaten. Prompt-velger med standardprompter, fritekst-felt for egendefinert prompt, modell-indikator (readonly). Drag-and-drop mottak for tekstnoder.
 - [ ] 18.5 Drag-and-drop integrasjon: node → verktøy (ny node), verktøy → node (in-place revisjon). Drop-sone feedback med verktøyets farge. Inkompatibilitet for lyd/bilde-noder med forklaring.