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Inseed

AI for mental health apps

Mental health apps, made smarter without a rewrite.

We add the AI features that matter in mental health products, reflective journaling analysis, voice-based check-ins, private session memory, and safety guardrails, to the app you already ship.

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You have a working app. Now it needs to feel like it listens.

Your app is live and people trust it with things they do not tell anyone else. Now you want AI inside it: a companion that responds when someone opens the app at 2am, a journal that turns a raw entry into something a therapist can read, a check-in that works by voice on a hard day. The pull is real. The way most shops respond is not.

The constraint in mental health is not the model, it is the data. Entries are the most sensitive text a person will ever write, and the moment you send them somewhere careless you have broken the one promise the product is built on. So we do not send them somewhere careless. We add AI as a layer beside your app, keep the raw text private by default, and design every prompt so nothing sensitive leaves the boundary you already set. What passed review stays where it is.

What we build

Four AI features that earn their place in a mental health app.

These are the patterns we see land in reflection, therapy-support, and wellbeing products. Each one sits beside your existing flow and treats the raw entry as something to protect, not something to ship to a third party.

Reflective journaling analysis

Turning a free-text entry into structured output: an emotional summary, a trend over time, and recurring themes the person can share with a therapist without spelling out the raw incident.

What it changes
Someone writes the way they already write, and the app gives back a picture they can actually use, instead of a blank box and a save button.
How it is built
A staged pipeline that extracts emotional texture and links entries over time, with the raw text kept end-to-end encrypted so the structured read is derived without the entry itself leaving the device.

Voice-based check-ins

A real-time voice companion that lets someone talk through what is going on, with live transcription, rather than typing when typing is the last thing they want to do.

What it changes
On a bad day the barrier to opening up drops from a paragraph to a sentence spoken out loud, and the app meets the person where they are.
How it is built
A streaming voice model behind a thin proxy, with a non-directive persona defined in the prompt and transcripts handled under the same privacy rules as text.

Private session memory

Continuity across sessions: the app remembers what someone talked about last time and picks the thread back up, instead of starting cold every open.

What it changes
The product stops feeling like a stranger every visit. It feels like it was paying attention, which is most of what people want from it.
How it is built
Past messages embedded and stored in a vector index, with the most relevant prior context retrieved and injected into the prompt. Only what is needed is recalled, and the raw store stays private.

Safety guardrails and crisis handoff

Guardrails that keep the assistant from giving clinical or harmful advice, plus detection of crisis language that routes a person to real resources or a human.

What it changes
The feature can support someone without pretending to be a clinician, and the moment a conversation crosses a line the app does the responsible thing instead of improvising.
How it is built
A refusal and scope policy in the system prompt, a lightweight classifier watching for crisis signals, and a defined handoff to human support or emergency resources.

Proof

What we have actually shipped in this space.

We name only work we can stand behind, and we are precise about who delivered it.

Epiphra

Delivered by Inseed

A live AI reflection companion for mental wellness. People talk through what is going on by text or real-time voice, and it responds with brief, non-directive support while remembering past conversations through long-term semantic memory. Our founder led the architecture and technical direction, with the team building the production app.

  • Real-time native-audio voice via the Gemini Live API
  • Long-term memory using text embeddings and a pgvector index injected into the prompt
  • Live on the App Store and Play Store, UI in six languages
How we build voice AI

Afterlight

Built by our founder

An AI self-reflection app that turns each journal entry into a visual reflection, emotional vectors, and a text summary, abstracting the raw emotion into something a person can share with a therapist without exposing the exact incident. Our founder built the entire agentic AI pipeline.

  • Multi-agent pipeline on Supabase functions, agents passing events to each other
  • OpenAI for text output plus an image model for the visual reflection
  • Raw entries stay end-to-end encrypted, never stored, never shown to the practitioner
Keeping AI private

YuMe

Built by our founder

A recovery app for people dealing with gambling addiction, combining an anonymous peer community, chat, and coaching on an evidence-based harm-reduction model. Our founder led the architecture and built the mobile app end to end, including an anonymous-by-default identity with progressive disclosure.

  • 5,000+ downloads in the first six weeks, 2,000+ community posts in the same window
  • End-to-end encrypted community feed (Virgil) over the Stream SDK
  • Anonymous user-to-coach matching that works without exposing identity
Read a full case study

Straight answer on the gap: we have shipped AI reflection features (agentic emotion pipelines, voice companions, semantic memory) and we have shipped privacy-first mental health products with end-to-end encryption. Afterlight is still pre-launch on TestFlight, so its AI pipeline is proven in testing rather than at scale. The audit is where we prove the specific feature on your app, in a throwaway prototype, before you commit to a build.

Borrowed proof, labeled honestly

React Native holds up under consumer scale.

React Native runs in production at Discord, Coinbase, and Shopify, and in health tracking at Gyroscope. These are not our clients. They are proof that the framework we build on carries real load in front of millions of people, so the question for your product is the feature, not the foundation.

How it starts

It starts with an audit, not a rebuild.

We do not open with a six-figure proposal. We open with one week of work that tells you exactly where AI fits in your app, what it costs to run, and whether it is worth building at all.

Step 1

Mobile App Audit

$5K to $8K

1 week

A written assessment of where AI fits in your app, a working proof-of-concept of the top feature, and a cost model you can take to your board. The fee credits toward a Sprint signed within 30 days, or is refunded if the report is not useful.

Step 2

AI Integration Sprint

$15K to $30K

2 to 4 weeks

One AI feature shipped into your existing app, as a thin integration layer with fallbacks, caching, and guardrails. Your architecture stays yours.

Step 3

AI-Native Mobile MVP

$25K to $75K

6 to 12 weeks

A production-ready AI-native mobile MVP when the answer is a new product rather than a feature. Fixed price, fixed milestones, both stores.

See full pricing

FAQ

Mental health questions, answered straight.

Where does a private journal entry go when AI reads it?

By default, nowhere it should not. The pattern we use keeps the raw entry end-to-end encrypted and derives the structured output (emotional summary, themes, trends) without persisting the raw text or exposing it to a practitioner. In Afterlight the entries are never stored in the database and never shown to the therapist. Where a model call is unavoidable, we send the minimum needed and log every field that leaves the app so the data path is auditable.

Can an AI companion be safe if it is not a clinician?

It can, as long as it never pretends to be one. We build the assistant to be supportive and non-directive, with a scope policy that refuses clinical advice, and a crisis-detection step that hands off to human support or emergency resources when a conversation crosses a line. Epiphra ships with a static safety disclaimer today; we can add active detection and handoff as a defined feature.

Do you have to send everything to OpenAI or Google?

No. There are three levers: send only de-identified or abstracted signal rather than raw text, use a provider endpoint with training disabled, and keep as much on-device as the feature allows. For emotional summaries you often do not need the raw incident at all, only the extracted texture, which is exactly how Afterlight is designed.

How does the app remember past sessions without storing raw entries?

Memory does not require keeping the plaintext. We embed messages into a vector index and retrieve the most relevant prior context to inject into the next prompt. In Epiphra that is text embeddings in a pgvector store. You recall what is relevant, not the entire history in the clear.

Can you add voice to an app that is text-only today?

Yes. Real-time voice needs a thin proxy backend because React Native cannot talk to the streaming voice APIs directly, which is exactly the piece we built for Epiphra with the Gemini Live API. The voice companion sits beside your existing text flow rather than replacing it.

Is React Native robust enough for a sensitive product like this?

Yes, and we can point at live evidence. YuMe runs an end-to-end encrypted anonymous community in production. Epiphra runs real-time voice and semantic memory on it. Beyond our work, Discord, Coinbase, and Shopify run React Native at consumer scale. The framework is not the risk.

Ready to add AI to your mental health app?

30 minutes, free. We will look at your app and tell you honestly where AI fits, what it costs, and what we would build first. Shuhel will be on the call.

Book a 30-minute call