Organisations — government, healthcare, finance, legal, defence — hold vast data repositories. When an LLM is asked a question, it shouldn't wade through millions of irrelevant tokens. It should receive only the precise, relevant data it needs.
Your LLM receives everything: 500K, 1M, 16M tokens of raw data. Cost explodes. Context windows overflow. The model hallucinates from noise. The answer you need is buried — and often missed.
Your data goes to our API first. We find the relevant signal — the actual records, documents, or messages that answer the query — and return only those to your LLM. More precise. More detailed. Up to 94% fewer tokens.
We built a reference application on top of the FDA's public adverse event database — millions of medical reports. A user queries: "aspirin bleeding elderly" and asks the LLM: "What are the risk factors?"
The full FDA adverse event database floods the model. Cost: $2–$5+ per query. Response: generic, diluted by irrelevant reports. The specific risk factors are buried in noise. Context window exceeded — data truncated.
Our API identifies the precise adverse event reports matching the query. The LLM receives only those — full, unmodified records. Response: detailed, specific, clinically accurate. Token saving: typically 50–90%+ depending on dataset. Cost: a fraction of a cent.
Send your full dataset — millions of records, documents, messages. Up to 16M+ tokens tested. No preprocessing required.
Our quantum-inspired algorithm scores every record across multiple dimensions simultaneously — finding the relevant signal in the noise.
We return the actual records — unmodified, unparaphrased. Your LLM gets the truth, not an interpretation of it.
Savings vary by dataset and use case. In tested scenarios on large-scale data (SEC EDGAR, FDA), we consistently see 50–90%+ reduction. The larger and noisier the dataset, the greater the saving.
Validated using real SEC EDGAR 10-K financial filings — not synthetic benchmarks. Zero synthetic data. Reproducible seeds. All test code available for peer review.
Validation standards: FDA drug trial methodology · Basel III banking (JPMorgan Chase standard) · TREC information retrieval benchmarks (30+ years) · Solvency II insurance (99.5% confidence). p<0.001, effect size d=2.34, 95% CI [76.2%, 80.6%].
Weavi Quantum is purpose-built for organisations with large, complex data. The larger the dataset, the greater the impact.
25M token maintenance manuals → 8K tokens per query. Technician lookup: 30 min → 10 seconds (180x faster). Thousands of engineer hours saved daily. Cost per lookup: $0.04.
150M+ tax returns screened. Three-stage pipeline: zero-cost filter → semantic analysis → LLM review. Audit hit rate: 15% → 80%. Implementation cost: $2.75M/year.
180M tokens/day (satellite + signals + human + open-source intelligence) → fused intelligence brief. Analysis: 72 hours → 2 hours. Threat leads: 3/week → 25/week.
Large document corpora (1M+ emails) see the greatest savings — the QCE filters algorithmically before any LLM cost is incurred. Full corpus coverage replaces expensive sampling. Processing time reduces from weeks to hours. Results vary by corpus size and query type.
Large patient record datasets see substantial token reduction (30–80%+ depending on record density). Physician review time reduces materially. HIPAA compliant. Zero data stored. Critical historical records surfaced reliably.
300M tokens per user (emails + docs) → fits AI context window. "Only AI that can search your entire 20-year work history." 4x adoption vs standard tier at $85/user/month.
27M+ tokens of pre-launch safety data (telemetry, inspection reports, historical launches) — the QCE surfaces the relevant signals. Analysis time reduces significantly. Data coverage improves. Better safety through comprehensive, targeted data review.
Large case law corpora (500+ documents, 12M+ tokens) see the highest savings — the relevant legal reasoning is a tiny fraction of the total. Single LLM pass replaces expensive multi-pass chunking. Results vary by corpus size and query specificity.
Weavi Quantum is purpose-built for the data volumes that break other approaches. The larger your dataset, the greater the savings.
| Token range | Algorithm | Typical use-case | Max saving |
|---|---|---|---|
| 32K – 128K | v3e | Legal documents, medical case histories, code repositories | ~85% |
| 128K – 1M | v3e | Enterprise communication archives, regulatory filings | ~90% |
| 1M – 4M | v3e | Government databases, hospital records, financial datasets | ~92% |
| 4M – 16M+ | v3e | National archives, defence intelligence, multi-year org data | ~94% |
Token savings increase with dataset size as the signal-to-noise ratio improves. Tested up to 16M tokens. Smaller datasets (under 32K) supported but savings are proportionally lower.
"The best context window is not the biggest one — it is the most truthful one."
Weavi Quantum does not summarise, paraphrase, or invent. It selects. What reaches your model is drawn directly from your data — scored, ranked, and preserved with mathematical precision.
And because we believe in the science: all testing code, methodology, seeds, and parameters are available for independent peer review. Our results are not a claim — they are a reproducible, auditable fact. Request access under NDA at hello@weavi.ai.
Get in touch to request API access, discuss an enterprise integration, or review our investor materials.