Marxen Data Labs

Compute is purchasable.
Reach is not.

Close to 500,000 verified contributors across India, Nepal and Sri Lanka. Speech, text, document, image, egocentric and multimodal sensor data, gathered by people who are physically there.

Survey tape marking a route across the bare concrete floor of an empty warehouse, seen at eye height, lamps hanging from the roof trusses
A route taped across a warehouse floor

01

Why this is the hard part

Everyone building a model needs the same thing: more data, from more of the world, collected and consented properly. The bottleneck is not annotation software. It is reach.

A clean hour of code-switched Tamil and English speech from a 52-year-old bus conductor needs someone standing in front of that bus conductor, explaining what the recording is for, taking real consent, capturing usable audio in a noisy depot, and paying him the same day.

Our network was built over years for ground activations and campus programmes. It was engineered to put trained people in front of strangers at scale, and we repointed it at data.

Contributors are identity-verified, briefed, trained and paid, which is what separates a corpus you can attest to from one assembled by anonymous accounts optimising for volume.

02

What we produce

  1. Speech and ASR corpora.

    Read, spontaneous and conversational speech. Code-switched Indian-language data, which is how India actually speaks and which most corpora quietly exclude. Field-condition audio with the traffic, machinery and crowds a deployed model will really meet.

    A shotgun microphone in a fur windshield, clamped to a carbon tripod on the leaf litter of a stream bank, its cable running down to a bag on the ground. The Veerse at Zahrensen, Germany, January 2025.
    Stream bank · shotgun microphone
    A field recorder, shotgun microphone and coiled cables on a steel table in a concrete hall
    Field recorder · steel table

  2. Transcription at volume.

    Multilingual, with speaker separation, timestamps and structured output.

    Three chrome faders standing at different heights on the black panel of an audio mixing console, each in its own slot with the scale marks ruled alongside.
    Mixing console · three faders

  3. Text and NLP corpora.

    Indic datasets for fine-tuning across education, healthcare, legal and government work. Annotated by native speakers, never machine-translated labels dressed up as ground truth.

    The keyboard of a grey mechanical typewriter, every key stamped with a Tamil character, the type bars visible behind it. A typewriting institute in Tiruppur, Tamil Nadu, August 2021.
    Typewriting institute · Tamil keyboard
    Archive shelving of bound registers, a desk lamp and one open volume
    Records room · registers, open volume

  4. Image, video and document annotation.

    Bounding boxes, segmentation, classification, OCR ground truth, and document-structure labelling for layouts Western training sets have never encountered, including handwritten registers and multi-script forms.

    A book held open and flat on the bed of a robotic book scanner, lit from the side by the machine strip lights, the steel cradle and rails around it. National Library of the Czech Republic, May 2024.
    National library · book scanner

  5. Egocentric data.

    First-person capture of people doing real tasks. The scarcest category in the market, and the one physical AI depends on most.

    A small black camera module clipped to the side of a clear spectacle frame, its wide-angle lens facing forward, photographed close up against a grey ground.
    Head-worn camera · spectacle frame
    A loading bay between block walls, its roller door half raised on a concrete yard, a pallet truck standing on the floor inside
    Loading bay · pallet truck

  6. Multimodal sensor data.

    Time-aligned audio, video, IMU and depth captured together, for models that have to work in a body rather than a browser tab.

    A spinning lidar drum on a white steel plinth, bolted to a black sensor box with camera apertures cut into its face, mounted on the roof bars of a car against a dark glazed facade. San Francisco, May 2025.
    Car roof · lidar and camera pod
    A stereo depth camera on a machined aluminium mount, its ribbon cable running across a steel bench
    Stereo depth camera on its mount

  7. Retrieval-ready knowledge bases.

    Ingestion, chunking and vector-store construction over an institution's own documents.

    A shelf of red cloth-bound volumes leaning against each other, each spine stamped with its own catalogue number on a paper label. The record library of Czech Radio Brno, April 2023.
    Record library · numbered volumes
    A server hall corridor between two rows of racks, lit from the far end
    Server hall · corridor

04

How we work

Human-led annotation with structured QA. Not a crowd marketplace, and not a model labelling its own training data. Every workflow leaves an audit trail: who labelled what, against which version of the guidelines, at what inter-annotator agreement.

Consent is explicit, recorded and revocable, with the terms written in the contributor's own language. Contributors are paid on schedule and told what the data is for. For a regulated buyer that is the difference between a corpus you can train on and one your counsel makes you delete.

05

Who buys this

Frontier and foundation-model labs adding multilingual capability. Indian AI companies fine-tuning on domain data. Enterprises building on their own historical material. Government language-infrastructure programmes. Robotics teams that need egocentric and sensor data from environments that look nothing like a California office.

sales@marxen.in

03

Coverage

verified contributors
Close to 500,000

  1. Indian languages.

    Tamil, Hindi, Telugu, Kannada, Malayalam, Bengali, Marathi, Gujarati, Punjabi, Odia, Assamese and Urdu, plus the code-switched registers that sit between them and English.

  2. Geographies.

    India across all major states, plus Nepal and Sri Lanka.

Built in India. For the people actually using it.

Tell us what you are trying to do. Bring the use case, the constraints and the users. We will tell you honestly whether Marxen is the right call, including when the answer is no.

Book a callinfo@marxen.in