AI in getbased — what’s powered, what isn’t

This page is the canonical map of where AI runs in the app. Three sections:

Light & Sun verdicts — ten per-state and per-event surfaces inside the Light & Sun lens. Some run automatically; individual Sun and device-session interpretations are deliberately on-demand.
AI elsewhere in the app — the chat panel, Interpretive Lens, context-card dots, PDF + photo import, EMF interpretation, and others.
What is NOT AI — the deterministic-math layer (channel doses, %MED, IU yields, trend alerts, PhenoAge, calculated markers). Big section because the math is auditable and reproducible — those numbers are NOT generated by an LLM.

Every live generative-health surface below requires a configured provider in Settings → AI. Demo profiles are the exception for context-card display: bundled summaries, dots, and tips can render without inference. The chat panel + Interpretive Lens read your enabled assembled lab context; per-row verdicts read just their own data slice. Agent Access uses a separate external-sharing permission and context path. Voice transcription and speech synthesis are a separate subsystem configured in Settings → Voice. They can use downloaded browser models, a compatible server, a supported OpenRouter/PPQ/Venice AI connection, xAI, or ElevenLabs. Dictation inserts text into the composer; the normal chat provider and context path begin only when the user sends that text.

This page maps where AI runs. The system prompt catalog documents the exact or parameterized instruction contract, source, parser, and review concerns for each surface.

Light & Sun verdict shape

Every Light & Sun verdict has the same shape:

A colored dot — green / yellow / red / gray (more on these below)
A tip — one sentence (≤14–18 words) summarizing the verdict
A detail — 1–4 sentences of context citing your specific numbers and the biology behind them
A refresh button (↻) — re-runs the analysis with a fresh API call

Dots:

Dot	Meaning
🟢 green	The feature-specific prompt found no supplied warning or clear concern; it does not certify safety, sufficiency, or protocol compliance
🟡 yellow	A prompt-defined caution, uncertainty, or data-backed gap is present
🔴 red	A strong entered pattern or supplied deterministic warning meets that prompt’s red rule; camera proxies alone cannot create red where the prompt forbids it
⚪ gray	Insufficient evidence, or an intentionally neutral pattern review such as the weekly channel synthesis

Where verdicts appear

Automatic surfaces use input fingerprints so unchanged data does not repeatedly spend tokens. Individual Sun-session and light-device-session interpretations are manual because the automatic Today and weekly summaries already cover those records.

Automatic event and setup verdicts

Light Tool measurement — fires for a saved Lux Meter, Flicker Detector, CCT Meter, Spectrum Classifier, Sleep Darkness, or Glass Transmission reading. Aggregate audit rows and brightness-proxy records are excluded. The verdict lives below the reading row in the room panel.
Audit verdict — fires when you save a Light Audit (a frozen snapshot of your environment). Appears at the top of the audit detail. A small colored dot also appears in the audit card header for at-a-glance status across multiple audits.
Onboarding plan — fires when you complete the Light & Sun setup card (skin type, eyewear, home lighting, Ott burden audit). Generates a personalized starting plan with three concrete first-week actions. Appears below the saved-setup chips.

On-demand session verdicts

Sun session row — runs only when the user requests an interpretation in the completed session detail. Today and weekly synthesis already cover the broader pattern.
Light-device-session row — runs only when requested for a completed panel, SAD lamp, dawn simulator, UVB/UVA, or targeted PBM session.

Per-state verdicts (fire on render when you have enough data)

These surfaces don’t have a clean “I’m done” trigger — the user is editing chip-pickers, adding rooms, the daily rollup shifts as new sessions land. Pre-2026-05-08 they had a manual Analyze button to avoid burning API calls during edit sessions; the per-render auto-fire now uses a fingerprint of the underlying state plus a per-tab-session guard so it fires once per meaningful state shift, not on every keystroke.

Light Today daily hero (+ dashboard chip) — fires the first time you visit the Light & Sun page each day, and re-fires when the cache is stale. Synthesizes your day’s full picture (sun + devices + tools + environment + recent biomarker context) into a single verdict at the top of the page. The same verdict appears as a compact chip on the dashboard’s Light Today strip.
Light Environment room — auto-fires when a room has a primary source set OR at least one measurement. Empty rooms skip auto-fire so a freshly-added blank room doesn’t burn an API call before you finish editing. Lives inside the room’s expanded body.
Per-screen — auto-fires when a device type is set (default ‘phone’). Lives inside the expanded screen card.
Indoor-burden summary — auto-fires when you have at least one room or screen mapped. Lives at the top of the Light Environment block (above the audit list).
Channel-mix synthesis — auto-fires when at least one channel has non-zero exposure in the rolling 7 days. Brand-new users with no logs skip until they have data worth interpreting. Lives in the “Your light, by what it does” section.

Caching and force-refresh

Every verdict is cached against a fingerprint of the underlying data. Ordinary render paths reuse an unchanged valid verdict without a fresh API call. The refresh button (↻) deliberately bypasses that cache and requests a new verdict even when the fingerprint is unchanged. When the data has changed (you edited a room, logged a new session, completed a measurement), the fingerprint mismatch invalidates the cache. The next render shows a “refresh AI verdict — your setup changed” CTA.

Cross-device sync

Verdicts live on the same row as the data they describe — sun-session verdicts on the session row, room verdicts on the room row, and so on. They sync to your other devices via the same per-row CRDT path the rest of your data uses. Latency is typically sub-10 seconds (the engine pushes immediately after writing, skipping the usual debounce).

What if the verdict seems wrong?

The AI is reasoning over your inputs, including measurement context. A few common failure modes worth knowing about:

Camera placement and source: camera-derived lux, CCT, RGB/spectrum, darkness, flicker, and glass-transmission results are screening proxies, not calibrated dose measurements. A one-point lux calibration stays approximate and source-specific. Follow each tool’s aiming guide and use the appropriate dedicated meter before a strong conclusion.
Cached vs current: if you edited a room recently and the verdict still references old numbers, the fingerprint should have invalidated and the CTA should say “your setup changed”. Click ↻ to regenerate.
Brand-name endorsement: the AI is instructed to never name specific brand products, only categories (“DC-dimmable LED”, “incandescent or halogen”). If a verdict mentions a specific product brand, that’s a regression — please open an issue.

Hardware advice

Seven verdict prompts share a load-bearing hardware block. A camera-banding result cannot identify flicker frequency, modulation depth, or health risk. Advice starts with comparisons—full versus reduced brightness, bypassed versus engaged dimming, or a different known fixture—and recommends product specifications or a purpose-built flicker meter before a strong conclusion. The prompt does not assume that any LED, smart bulb, dimmer, incandescent, or halogen source is flicker-free, does not name brands, and explicitly forbids open-flame recommendations.

Provider requirements

Verdicts require an AI provider configured in Settings → AI. Supported providers: OpenRouter, PPQ, Routstr, Venice, Local AI (Ollama / LM Studio / Jan / Unsloth Studio / compatible endpoints), and Custom. Provider pricing and actual token use vary by prompt, context, model, and retry behavior. A downloaded model running on the user’s own hardware has no provider fee; remote or cloud-tagged Local AI models may charge.

Disabling verdicts

If you want to keep your AI provider configured for the chat panel and Interpretive Lens but pause the per-row verdicts (e.g., during a budget-sensitive month):

window.DISABLE_AI_VERDICTS = true

Run that in DevTools Console. All ten surfaces will short-circuit until you remove the flag (or reload — the flag doesn’t persist).

AI elsewhere in the app

The ten Light & Sun verdicts above are one corner of the app’s AI surface. Below are every place AI runs in the rest of the app. All require a configured provider in Settings → AI.

Text-generation surfaces

Surface	Where	What it does
AI chat panel	Slide-out at bottom-right, every page	Free-form streaming conversation grounded in enabled profile context, with per-thread drafts, search, bounded rendering, latest-turn edit/retry, linked forks, custom personas, and sequential multi-persona discussions.
Interpretive Lens	Dashboard top, “Lens” pill	Routes a focused query through your knowledge base (research papers, notes, books). Uses the on-device transformers.js RAG by default, or an external server you configure.
Focus Card	Dashboard, top of the page	A 3–5 sentence “what to focus on right now” rollup synthesized across your goals + recent biomarker drift + active light/wearable signals. Auto-fires when its fingerprint changes.
Context-card AI assessment	9 health-context cards	A reported-facts summary, health dot, and short marker-linked tip per stale card. Demo profiles use bundled results; Local AI can refresh edited demos automatically, while cloud/paid demo refresh requires session-only consent.
Guided chat setup	First-time visitors, before any data	A chat-shaped setup flow for profile basics, optional provider choice, extra context, context cards, and import routing. The deterministic setup UI does not itself spend tokens.
Custom persona draft	Chat personality picker → New Personality	Generates a 400–500 word editable persona draft from a person, archetype, or communication style. It receives no lab context.
Custom-marker description	Marker detail modal, when AI is configured	One-sentence (≤30 words) explanation of what the marker measures + why it matters clinically. Cached per-marker so it only fires the first time.
EMF assessment interpretation	EMF assessment editor → Interpret	Synthesizes a room-by-room read of your EMF measurements with severity, source attribution, and mitigation recommendations. Manual trigger.
EMF comparison interpretation	EMF editor → Compare two snapshots	Side-by-side delta read between two saved assessments — what got better, what got worse. Manual trigger.
Supplement mitochondrial context	Current supplements and medications	ask AI for context opens a constrained question based on matched claim-level primary studies and the user’s mitochondrial/energy biomarkers. Manual trigger.
Nutrition History Ask AI	Body → Meals & Nutrition → History	Builds an editable 30-day, 3-month, 6-month, 1-year, or all-time aggregate from reviewed meals. The request receipt prevents the normal nutrition context block from being included twice.

Vision-AI surfaces (image-in)

Surface	Where	What it does
PDF lab import	Drop a PDF on the dashboard or sidebar	Extracts text → obfuscates PII (regex + optional local-AI streaming sanitizer) → AI parses values + maps to schema markers. Specialty labs then pass through deterministic product-aware normalization before custom-marker persistence.
Built-in model test	Settings → Data → Test models on lab reports	Runs the production PDF parser against a synthetic 68-result reference report, preserving raw model output so deterministic scoring can compare it with the verified answer key.
Photo lab import	Same drop-zone, accepts JPG / PNG / HEIC	Vision-extract path — same downstream as PDF, but starts from a phone snap of a printed lab report.
Custom-marker suggestion (from PDF)	Inside the PDF import flow	When a parsed value can’t be matched to a known marker, AI suggests a key + name + unit + reference range based on the surrounding PDF context.
Supplement/medication product review	Supplements & Meds → Review photos	Reviews up to four product, Facts, directions, or COA images. Separates active ingredients, excipients/capsule materials, laboratory results, and warnings into a draft that the user must review and then save.
Meal photo analysis	Body → Meals & Nutrition → Log meal	Sends up to four selected meal photos through the optional dedicated nutrition model route and returns an editable nutrient/component estimate. Running analysis may continue in the background or be canceled.
Nutrition-label analysis	Body → Meals & Nutrition → Log meal	Extracts serving and nutrient facts from selected label images into the same review-first meal draft.
Meal benchmark	Body → Meals & Nutrition → Meal benchmarks	Runs selected configured models independently against benchmark photos. Each model run can be canceled, retried, or replaced without interrupting the others; deterministic code scores reviewed estimates against known values.
Spectrum Classifier	Light Tools → “What is this light?”	Camera-based light-source classifier (LED cool / LED warm / fluorescent / incandescent / daylight). Uses RGB ratios + flicker variance — the deterministic part is the classifier itself; the AI only kicks in for the verdict on the saved measurement (covered in Light & Sun above).

What is NOT AI (so you can trust the numbers)

A lot of the app is deterministic math, not AI inference. If you see a number on a chart or a tier on a pill, it came from one of these:

Surface	Source
Channel pills (Vitamin D / Body clock / Cell energy & repair / Blood vessels / Skin & mood / Outdoor eye light)	Browser-side spectrum reconstruction → action-spectrum convolution → logged source/rhythm signal. Deterministic model, not a measured body response or target-progress score.
% MED burn estimate	Modeled erythemal action spectrum × Fitzpatrick reference threshold. It is a caution estimate, not a personal safe-time guarantee.
Vitamin-D-effective estimate	Modeled UVB reaching logged uncovered skin. It is not measured synthesis, serum status, or an oral-dose equivalent.
Reference ranges (`refMin` / `refMax`) on lab charts	Whatever the lab printed on the PDF, OR your own override. Never AI-generated.
Trend alerts (“dropped 25%” / “trending up”)	Linear regression slope + R² thresholds in `data.js`. Algorithmic, not LLM.
PhenoAge biological age	Levine 2018 formula over 9 biomarkers + age. Closed-form math.
Calculated markers (Free Water Deficit, BUN/Creatinine ratio, HOMA-IR)	Published formulas with conversion math for unit systems.
Channel doses on dashboard / per-session	Same Bird-Riordan reconstruction; no AI.
Trend sparklines on context cards	Direct plot of stored values.
Light-environment screening tiers	Deterministic heuristic from disclosed room/screen patterns and source-aware sensor, meter, or camera observations. It is not measured dose or diagnosis. The following narrative is AI-generated.
Custom marker creation, sidebar edits, chip-picker changes, manual-value entry	Local data writes. No AI call fires; the AI sees them on the next chat turn.
Supplement/medication lifecycle, daily quantity math, contaminant grouping, and mitochondrial evidence matching	Period/status rules, compatible-unit arithmetic, and claim-catalog lookup are deterministic. AI is used only for reviewed product extraction, requested context, or impact summaries.
Genome import, association/evidence/relevance labels, APOE, and mtDNA resolution	Raw/report/manual parsing and catalog lookup are deterministic and local. Ask AI on a finding is an explicit editable prompt that supplies the scoped catalog baseline; it is not part of import or classification.
Nutrition totals, targets, timing, fasting windows, Fuel Mix, and response trends	Computed from reviewed meal records in browser code. Target progress colors and meal-history charts do not come from a model.
Meal component arithmetic and benchmark scoring	Component totals, known-value comparison, error percentages, and winner ranking are deterministic after the model estimates have been normalized.

What runs on-device vs cloud

CLI-backed features use ai-feature-routing.js and the capability-aware execution route instead of bypassing the selected assistant through a direct-provider call. Local CLI agents receive the same enabled context plus scoped getbased tools; personal gateways receive context but are text-chat only. Image support does not imply audio support, and automatic voice remains on-device for CLI chat. See CLI agent providers for the dispatch and approval contract.

Path	Where it runs
Knowledge Base (Interpretive Lens default)	On-device transformers.js + OPFS — your knowledge documents never leave the browser.
Local AI provider (Ollama / LM Studio / Jan / Unsloth Studio / compatible endpoints, configured in Settings → AI → Local AI)	Direct from the browser to the configured endpoint. Loopback runs stay on the device; LAN, remote, and cloud-tagged setups leave it.
PII obfuscation (compatible streaming sanitizer)	Optional. It stays on-device only with a loopback endpoint and downloaded model, then passes the prepared content to the active provider.
Voice — On this device	Downloaded Whisper and Kokoro models run in browser workers. Raw recordings are transient and model files are rebuildable caches.
Voice — compatible server / OpenRouter / PPQ / Venice / xAI / ElevenLabs	The selected voice service receives only the recording being transcribed or assistant reply being synthesized. All are called directly from the browser; remote recipients require cloud-processing consent where applicable. Same as chat resolves supported AI accounts or falls back on-device. Private chat transports do not automatically cover ordinary voice endpoints.
Cloud providers (PPQ / Routstr / OpenRouter / Venice / Custom)	The browser calls the selected endpoint directly after recipient-scoped consent. Venice Encrypted TEE Mode encrypts message content with ECDH + AES-GCM and requires Intel TDX DCAP plus signed NVIDIA NRAS checks, but Venice still sees request metadata. The checks do not prove TDX/GPU co-location, approved code measurements, or the source of every response.
Camera frames and selected images (light tools, lab import, meal photo, nutrition label)	Prepared in the browser, then either kept on-device for light-tool metric extraction or sent to the configured AI provider for the requested vision call. Meals accept up to four images. Full-size meal images are request-only and are not saved; small reviewed thumbnails may be stored with the meal.

Caching + privacy notes

Every per-row verdict caches against a fingerprint of the underlying data. If nothing’s changed, the cached verdict is returned without a fresh API call.
Import model-test records stay device-local and are stripped from sync payloads because timing, loaded state, and hardware metadata do not transfer meaningfully. See AI model testing internals.
The chat panel pulls from buildLabContext() — the assembled section block filtered by Manage → Context → Data sources (contextSourceSettings). Those switches gate Insight Context Cards, Supplements & Medications, Blood marker results, per-specialty lab groups, Genome tiers, Light & Sun, Meals & Nutrition, and Wearable recovery context for in-app AI, score context, and source-driven warnings.
Interpretive Lens and Knowledge Base live in the Answer grounding panel, separate from data-source gates. Agent Access is separate again: the browser pushes an encrypted gateway summary only after Settings → Agent Access is enabled, and that push calls buildLabContext({ skipGroupFilter: true, ignoreContextToggles: true }) so external agents do not inherit the in-app Data sources switches.
Custom-marker creation, manual-value entry, sidebar edits, and chip-picker changes do NOT trigger AI calls — they update local data, AI sees them on the next chat turn or the next per-state verdict render.

Sun Sessions — how outdoor light sessions work
Light Environment — rooms + screens + audits
Light Tools — the measurement tool ecosystem
AI Providers — how to configure a provider
AI Chat — the chat panel that complements the per-row verdicts
Meals & Nutrition — meal analysis, reviewed storage, trends, benchmarks, and AI context boundaries
Supplements and medications — therapy lifecycle, reviewed product import, quality evidence, and AI boundaries

Core project

Data model & sync

AI & agents

Prompt catalog

Feature internals

Security architecture

Operations & release

AI surfaces map

AI in getbased — what’s powered, what isn’t

Light & Sun verdict shape

Where verdicts appear

Automatic event and setup verdicts

On-demand session verdicts

Per-state verdicts (fire on render when you have enough data)

Caching and force-refresh

Cross-device sync

What if the verdict seems wrong?

Hardware advice

Provider requirements

Disabling verdicts

AI elsewhere in the app

Text-generation surfaces

Vision-AI surfaces (image-in)

What is NOT AI (so you can trust the numbers)

What runs on-device vs cloud

Caching + privacy notes

​AI in getbased — what’s powered, what isn’t

​Light & Sun verdict shape

​Where verdicts appear

​Automatic event and setup verdicts

​On-demand session verdicts

​Per-state verdicts (fire on render when you have enough data)

​Caching and force-refresh

​Cross-device sync

​What if the verdict seems wrong?

​Hardware advice

​Provider requirements

​Disabling verdicts

​AI elsewhere in the app

​Text-generation surfaces

​Vision-AI surfaces (image-in)

​What is NOT AI (so you can trust the numbers)

​What runs on-device vs cloud

​Caching + privacy notes

​Related guides

AI in getbased — what’s powered, what isn’t

Light & Sun verdict shape

Where verdicts appear

Automatic event and setup verdicts

On-demand session verdicts

Per-state verdicts (fire on render when you have enough data)

Caching and force-refresh

Cross-device sync

What if the verdict seems wrong?

Hardware advice

Provider requirements

Disabling verdicts

AI elsewhere in the app

Text-generation surfaces

Vision-AI surfaces (image-in)

What is NOT AI (so you can trust the numbers)

What runs on-device vs cloud

Caching + privacy notes

Related guides