MongoDB Software / SaaS United States

Mentra

Enterprise Knowledge Search

Mentra needed a backend that would let a small team iterate rapidly on AI-native smart glasses while storing fast-changing app data, multimodal notes, and retrieval-ready embeddings for user context.

For me, building smart glasses started around six years ago.
The goal is to fit around 80 to 90% of faces with one frame.
We recently launched Mentra Live, which is our take on the Meta Ray-Bans.

Reported impact

Business outcome

Mentra can ship new smart-glasses experiences quickly, support accessible captioning and note retrieval, and give third-party developers a backend that extends the platform beyond a single device app.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Mentra built its AI-native smart-glasses platform on MongoDB Atlas from day one so the team could move from prototype changes to production features without fighting database migrations. Atlas stores user settings, operating-system state, and backend data for built-in and third-party apps, while BSON keeps the persistence model close to the way data is handled at runtime in the application. The Mentra Notes app ingests what the wearer sees and hears, stores that multimodal history in MongoDB, and creates vector embeddings for each conversation. Using MongoDB Voyage AI, Mentra can retrieve relevant conversations later from a natural-language query and return summaries or context to the wearer. Atlas profiling and management tooling also helps engineers debug slow queries quickly, which matters for a startup building open-source hardware and software that must scale into manufacturing, accessibility, and always-on contextual AI use cases.

Data flow

Audio, visual, and settings data from Mentra's smart glasses apps is stored in MongoDB Atlas; Mentra Notes creates embeddings for conversations and MongoDB Voyage AI retrieves matching context later so users can search, summarize, and revisit what they saw or heard.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores Mentra's user settings, OS state, app data, and multimodal note history while supporting fast schema evolution.
  2. Serving
    • MongoDB Voyage AI Provides vector-based recall so Mentra Notes can retrieve relevant conversations from natural-language queries.

Architecture clues

  • Mentra Notes stores what users see and hear, then queries vector embeddings for later recall.
  • MongoDB has been used since day one because its BSON model is close to Mentra's runtime objects.
  • Third-party developers get a MongoDB-backed persistence layer when they build on the platform.

Evidence from the source

The number one way is the MongoDB Atlas platform. It is a huge time saver.
We have vector embeddings for every single conversation, and we use the MongoDB Voyage AI tool to recall those conversations based on the user's query.
We store all user settings and OS data in MongoDB.
We've been using MongoDB since day one.
Transparency Evidence & methodology 86/100 reference value · 92% confidence · 3 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value86/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    3 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    Mentra customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/mentra
    Source type
    Vendor-published customer story
    Published
    Jul 30, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Prospeo

    Mentra Overview, Address & Contact

    Independent secondary
    https://prospeo.io/c/mentra

    The embedded Organization data lists streetAddress "Charlotte, North Carolina, United States" and addressCountry "US".

    Source type
    Structured profile
    Supports
    • Region
  3. Mentra

    Privacy Policy

    First-party
    https://www.mentra.com/privacy-policy

    The policy lists the address as "Mentra, Inc., 9805 Statesville Road, Suite 6169, Charlotte, NC 28269," in the United States.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Jul 30, 2026 · MongoDB · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.