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.
The problem
AI systems keep storing the same idea in different forms.
Everything an AI system looks up is stored as a vector.
Text, images, audio, and sensor readings are turned into vectors so the system can store them and find them again. The collection of vectors a system searches is its index. The bigger the index, the more memory it needs, the slower each search, and the harder it is to keep current.
Much of it repeats what is already there.
A policy reappears across handbooks, articles, and revisions. A camera captures hundreds of near-identical frames. Each one becomes another vector, so the index grows with every repeat, and the system has to store, search, and move all of it.
Green Vectors reduces the repetition before storage.
It sits between the model that creates vectors and the database that stores them, so fewer vectors ever reach the index. What remains is kept current, and the source material is left intact. That is a different question from making each vector smaller.
Every repeated vector is paid for in four places.
At scale, the answer has been brute force: more memory, more servers, more power. That is part of why AI infrastructure is short of both, and why so little of it can leave the data center.
Measured result
So we changed what enters the index, and put it to the test on 50,000 books.
260 GBbecame1.3 GB.
The same books, turned into an index twice: once the conventional way, once through Green Vectors. A 99.5% smaller index, queries up to 4x faster, and search results that stayed at least as close to the question.
Green Vectors: 1.3 GB and about 76,000 stored vectors for the same 50,000 books.
English-language Project Gutenberg collection, June 2025, one configuration. Index footprint, not total system memory. Other collections, and sensor, perception, and autonomous-system workloads, need their own tests.
Fit
The real world is not a data center.
A data center adds servers, memory, power, and cooling when a workload grows. A vehicle, an autonomous platform, a portable system, or an embedded device cannot. The model is only part of what it carries. Its knowledge layer needs room too.
Better models help. Better hardware helps. Neither removes repeated meaning from the knowledge layer the system has to store and search.
This is memory, not disk. To search at speed, an index has to be resident in memory, and memory on a vehicle or a handheld is the scarcest thing on board. Fitting there is what lets a capable workload run on the hardware already in the field.
At 50,000 books, the conventional index needs 264 GB and fits only a data center. After 1-bit quantization it needs 12.1 GB and fits down to an autonomous platform, with accuracy given up to get there. Green Vectors needs 5.3 GB and fits all five, down to a handheld.
Footprints are the measured index sizes from the 50,000-book study (260 GB conventional, 8.1 GB after 1-bit quantization, 1.3 GB Green Vectors) plus a 4 GB allowance for the model and runtime. Larger collections scale the measured sizes proportionally, which is an illustrative assumption, not a measured projection. Memory budgets are selected examples, not device specifications. Source-document storage is not included. This shows what fits in memory, not demonstrated deployment, power, or thermal performance.
How it works
Four connected actions. One compact, current knowledge layer.
Reducing vector count only matters if the system keeps the information the task needs. Green Vectors balances the two through four actions that work as one. Explore a familiar example: one travel policy appearing across several documents.
Bring related information together.
Green Vectors checks whether new input belongs with what is already represented. A differently worded policy reinforces an existing idea rather than creating another isolated entry.
What this makes possibleAn index that grows with new ideas, not with every repeat. In a sensor stream, the same action could let similar frames contribute to one representation of the current condition.
Travel policy, illustrative example1 of 4Different wording. Related meaning.
Employee handbookFile travel expenses within 30 days.Support articleTravel claims are due within 30 days.Training guideEmployees have 30 days to submit expenses.Shared representationTravel expense deadlineSubmit within 30 days.
Every source stays linked.Three sources say one thing. It joins what is already known instead of becoming another isolated entry.
Let meaningful information matter more.
A direct policy revision should count for more than a passing mention. Green Vectors decides how strongly incoming information changes what already exists.
What this makes possibleThe current revision, or a real deviation in a sensor stream, is not drowned out by a thousand repeats of the old state.
Travel policy, illustrative example2 of 4Not every input should carry the same weight.
Direct policy revisionStrong influenceDedicated policy sectionModeratePassing referenceLightGeneric repetitionMinimalInfluence follows relevance, not repetition. No numeric score is implied.
Keep the larger context connected.
Documents hold relationships between passages, sections, and the whole. Green Vectors preserves those links so the system can work at the level the task requires.
What this makes possibleAnswers at the right level of detail without stuffing a context window. Readings that stay tied to their time, place, and source.
Travel policy, illustrative example3 of 4The detail stays attached to its context.
DocumentEmployee handbookSectionTravelBenefitsParagraphDeadlineApprovalsReceiptsA question can use as much context as it needs, without one oversized block or a pile of disconnected fragments.
Update what is already represented.
When new information belongs with an existing representation, Green Vectors updates it instead of storing another independent vector. The working layer changes as knowledge changes.
What this makes possibleKnowledge that changes in place, on the platform, without reprocessing the whole collection.
Travel policy, illustrative example4 of 4New information updates what exists.
Existing representationTravel expense deadline: 30 daysPolicy revision, this weekManager approval now requiredCurrent representationTravel expense deadlineSubmit within 30 days. Manager approval required.The vector representation changes. The source documents do not.
Different wording. Related meaning.
Every source stays linked.
Three sources say one thing. It joins what is already known instead of becoming another isolated entry.
What you get
A smaller layer. A simpler stack.
Most retrieval stacks grew by adding steps to compensate for an index that was too big and too noisy. Green Vectors changes the index itself, so those steps have less to do, and several become optional. None of them is wrong; the question is how many you still need. Hover, tap, or focus a dashed step to see why.
Stores each vector with fewer bits so the index takes less memory.
Indexes outgrew the memory available. The cost is precision: every vector loses detail, and retrieval quality can drop with it.
Fewer vectors, so the pressure that drove quantization is mostly gone. In the benchmark, 1-bit quantization still needed 8.1 GB; Green Vectors needed 1.3 GB. It can still be applied on top if the deployment wants it.
Two fields, one foundation
Useful today. Built to go further.
Documents, sensors, and perception streams produce different data with the same structural problem: information keeps arriving, much of it overlaps, and the hardware that has to process it is finite.
Language intelligence
Reduce repeated meaning across large, changing text collections so more distinct knowledge can be stored, searched, and kept current, alongside the models and database you already run.
Measured on enterprise-scale and public datasets.
Explore language intelligenceAutonomous systems
Evaluate how compact, continuously updated representations can help perception, sensor, and telemetry systems keep useful information close to the platform that needs it, inside the memory, power, and thermal limits of hardware in the field.
Non-language performance is established separately on each target platform and task.
Explore autonomous systemsTell us where your AI needs to run.
The workload. The hardware. The limit in the way. A useful evaluation compares your current approach and Green Vectors on the same work under the same conditions.