MORPHOS AIGet in touch

    Morphos AI

    AI can only go where its knowledge can fit.

    The next era of AI will not be confined to data centers. Green Vectors automatically organizes, consolidates, and continuously updates the information AI depends on, so systems have less to carry and search. That makes room for more capable intelligence on the hardware already in the world—from enterprise infrastructure to vehicles, robots, satellites, and the edge.

    See how Green Vectors works Get in touch

    For language systems today and autonomous-system research under active development.

    Prismatic folded Morphos M
    INFRASTRUCTUREREDUCED
    SYSTEMCAPABLE
    APPLIED AI RESEARCHLANGUAGE INTELLIGENCEAUTONOMOUS SYSTEMSCHANDLER, ARIZONA

    01 / Deployment

    The real world is not a data center.

    Data centers can add servers, memory, power, and cooling when a workload grows. A vehicle, autonomous platform, portable system, or embedded device cannot.

    The system also has to hold the model and the information it uses, move data quickly enough, stay within the available power, and shed the heat it produces. If the system exceeds those limits, the capability can't scale.

    Explore Autonomous Systems
    01Data center
    02Portable rack
    03Vehicle
    04Autonomous platform
    05Handheld and embedded

    Centralized compute remains part of the system. The focus is what has to keep working when access to it is limited.

    The deployment limit

    A capable model can still be impossible to make useful.

    Smaller and more efficient models solve only part of the problem. AI also has to store and find the information it uses.

    Better models help. Better hardware helps. Neither removes repeated meaning from the knowledge layer the system has to carry and search.

    The AI has to fit the hardware already in the field.

    02 / The hidden burden

    AI systems keep storing the same information in different forms.

    A policy can reappear across handbooks, support articles, training guides, and later revisions. A camera can capture hundreds of similar views of a scene. A sensor can report the same condition for hours. The words, pixels, and readings change. Much of the useful information does not.

    AI systems represent text, images, audio, and sensor data as vectors. Depending on the workload, each passage, frame, or reading can create another vector—even when it adds little new information. The system then has more to store, search, update, and move, increasing pressure on memory, compute, power, bandwidth, and response time.

    Compression makes each vector smaller and less accurate. Morphos asks a different question:

    How many vectors does the system need to carry at all?

    Morphos reduces repeated vector representations before storage, leaving less information to store, search, and keep current.

    CONVENTIONAL VECTOR ACCUMULATIONMore inputs. Less new information.
    Illustrative input patterns

    Language

    A policy repeated across four sources:
    Employee handbookFile travel expenses within 30 days.
    Support articleTravel claims are due within one month.
    Training guideEmployees have 30 days to submit expenses.
    Policy revisionThe travel-expense deadline remains 30 days.

    Edge and sensor systems

    A stream of similar observations:
    Camera inputThe scene remains substantially unchanged across consecutive frames.
    TelemetryReadings remain within the expected operating range.
    Meaningful changeA new object, event, or deviation appears.
    Related information identifiedOrganize what overlaps. Preserve what is distinct. Update the representation when something meaningful changes.

    Compact, current information layer

    SHARED MEANING

    Travel-expense deadline

    Submit within 30 days
    CURRENT OPERATING STATE

    Stable condition

    Meaningful changes retainedThe representation remains connected to the information that produced it.
    Source context preservedFewer repeated representationsMeaningful changes remain visible

    When information repeats, the system shouldn’t have to store and search it all over again.

    03 / The platform

    One principle across two fields of work.

    Morphos is applying one principle across both fields: preserve what is distinct, reduce what repeats, and keep the representation current.

    Documents, sensors, and perception streams create different kinds of data. They share a structural problem: information keeps arriving, much of it overlaps, and the hardware available to process it is finite.

    Green Vectors has measured results today in language retrieval. Morphos is also researching how the approach could apply to perception, sensor, telemetry, and autonomous-system workloads. Non-language development and evaluation are underway.

    01 / FIELD

    Language Intelligence

    Reduce repeated meaning across large, changing text collections so more distinct knowledge can be stored, searched, and kept current.

    Measured on enterprise-scale and public datasets.Explore Language Intelligence
    02 / FIELDUnder development and evaluation

    Autonomous Systems

    Evaluate how compact, continuously updated representations could help perception, sensor, and telemetry systems retain useful information close to the platform that needs it.

    Under active development and evaluation.Explore Autonomous Systems

    The shared foundation

    Green Vectors

    Reduce repetition before the system has to carry it.

    Green Vectors changes what enters the vector layer.

    It receives vectors created from incoming information, organizes related content, determines how strongly new input should affect what is already represented, keeps information connected to its larger context, and updates the representation as knowledge or conditions change.

    These actions work together to create a smaller, more current working layer. Green Vectors changes the vector representation built from the input; it does not rewrite the original source information.

    For language systems, Green Vectors can sit between the embedding model and the existing vector database. Teams can keep the database and retrieval tools already in place.

    For autonomous systems, Morphos is evaluating the same underlying approach with perception, sensor, telemetry, and local decision-support workloads on target hardware.

    The representation is organized and updated as information arrives, so it is ready for retrieval or local use.

    See the four connected actions
    Incoming informationEmbedding or feature modelGreen VectorsCompact, current vector layerRetrieval or local decision support

    04 / How it works

    Four connected actions. One compact, current information layer.

    Reducing vector count matters only if the system retains the information the task needs.


    Green Vectors balances those goals through four connected actions. These actions have measured application in language retrieval. Their application to perception, sensor, telemetry, and autonomous-system workloads remains under development and evaluation.

    1. 01

      Bring related information together.

      Green Vectors evaluates whether new input belongs with information already represented. A differently worded policy may reinforce an existing idea instead of automatically creating another isolated entry. In non-language research, similar observations may contribute to an existing representation of the current condition rather than being treated as unrelated by default.

    2. 02

      Let meaningful information matter more.

      Not every new input should have equal influence. A direct policy revision should matter more than a passing reference. In a sensor stream, a meaningful deviation may matter more than another reading showing the same steady condition. Green Vectors evaluates how strongly incoming information should affect what is already represented.

    3. 03

      Keep the larger context connected.

      Information has structure. Documents contain relationships between passages, sections, and the larger source. Perception and sensor systems contain relationships across time, space, and different input streams. Green Vectors preserves these connections so the system can work with the context the task requires without treating everything as one oversized block or a collection of disconnected fragments.

    4. 04

      Update what is already represented.

      When new information belongs with an existing representation, Green Vectors can update that representation instead of storing another independent vector by default. This allows the working information layer to change as knowledge or operating conditions change, without requiring every repeated input to become another permanent representation.

    Architecture

    Green Vectors changes vectors before they reach the database.

    Compression and quantization make each vector smaller, but less accurate. Hybrid Search and Reranking can restore some of the accuracy loss. But only Green Vectors reduces how many vectors the system has to store and search. Those other legacy methods can be used together with GV - but in many cases, they become optional and unnecessary altogether.

    Source materialEmbedding modelGreen VectorsExisting vector databaseRetrieval

    05 / The difference

    Green Vectors changes what the database has to store.

    Exact deduplication can find copies. Compression can make stored vectors smaller. Faster databases can search an existing index more efficiently. Green Vectors addresses an earlier question: how much repeated meaning should enter the index at all?

    The answer depends on a coordinated system for organizing related meaning, controlling how new information changes what is already represented, preserving context, and keeping those representations current. No single step produces the result by itself.

    01

    Smaller vectors

    Reduce the size of each representation after it is created.

    02

    "Faster" search

    Search the representations already stored more efficiently.

    03

    Larger hardware

    Add capacity where the deployment can support it.

    The Green Vectors Difference

    Fewer redundant representations

    Change what enters storage in the first place. Fewer Vectors = Less to store = Less to compute. Win-win-win.

    Project Gutenberg benchmark

    260GB1.3GB.

    The benchmark used approximately 50,000 public-domain books from Project Gutenberg. The conventional vectorized index occupied 260GB. The Green Vectors configuration for the same source material occupied 1.3GB.

    Source material
    Approximately 50,000 books
    Traditional vector count
    More than 15 million
    Green Vectors vector count
    Approximately 76,000
    Measured outcome
    Stored vector count and index size
    Language and date
    English · June 2025
    SCOPE OF RESULT

    This result comes from one public literary collection under one configuration. Results on other collections or on sensor, perception, autonomous-system, and constrained-hardware workloads require separate tests.

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    06 / Evaluation

    Test Green Vectors on your own system.

    The result depends on the source material, workload, success criteria, and hardware available. A useful evaluation compares the current approach with Green Vectors on the same work and under the same conditions.

    LANGUAGE AND RETRIEVAL

    Bring the collection and the current baseline.

    Bring a representative dataset, real queries, and the current baseline for storage, retrieval quality, latency, and updates.

    Discuss a language evaluation
    HARDWARE AND AUTONOMOUS SYSTEMS

    Bring the target platform and its real limits.

    Bring the target platform, a representative workload, the known system limits, and an integration owner. Non-language performance is established separately on each target platform and task.

    Discuss a platform evaluation
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    Tell us about the system, the workload, and the limit in the way.

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    Tell us where your AI needs to run.

    Tell us what the system must know, where it has to operate, and the memory, compute, power, or connectivity limit in the way.

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