Now Available — March 2026

Read everything.
Miss nothing.

Awarity gives AI an unlimited context window — no RAG, no embeddings, no lossiness. 92% cheaper than sending data to a frontier model. Free to download.

Download for Mac Apple Silicon & Intel · Free
See the benchmarks →
92%
Cheaper via API
vs. GPT-5.4 on large catalogs using cloud map models. The bigger your data, the bigger the gap.
99%
Cheaper on-prem NEW
Run the map phase on Qwen or Llama locally. Cost drops to ~$0.04 per query — just the final infer call.
100%
Perfect score
ECW + Claude Opus nailed every contradiction detection case. Base model missed 4 of 14.
~3 pg
Break-even
ECW costs less than a native model call after just ~3 pages with GPT-5.4 or Claude Opus.
The problem

Three things conventional AI struggles with

01

Context window limits

Native LLMs like Claude or ChatGPT cap out at 500K–1M tokens. Drop in a large dataset and you're already over the limit — the model starts hallucinating or refusing. Awarity has tested beyond 400 million tokens.

02

RAG is lossy by design

Retrieval-Augmented Generation picks what to retrieve — and guesses wrong. Embeddings compress meaning, vector search misses edges, and the answer you get is only as good as what was fetched. Awarity reads everything, every time.

03

Data leaves your hands

Most AI tools require uploading documents to a third-party cloud. Awarity runs on-prem or fully offline. Your contracts, financials, and patient data never leave your environment.

Benchmark results

Equal or better quality. Radically lower cost.

90.1
GPT-4.1 + ECW synthesis score
Nearly matches GPT-5.4 alone (90.9) — at 92% lower cost. ECW lifts a cheaper model to flagship performance.
$0.31
Per synthesis case with ECW
GPT-5.4 alone costs $3.67 for the same task. Same quality. 92% cheaper. Every single time.
New
~$0.04
With an on-prem map model
Run the map phase on Qwen or Llama locally — cost is zero. Only the final infer call charges API fees. Trade-off: slower than cloud.
Full benchmark results — including on-prem model testing
Our solution

How the Elastic Context Window Works

Step 01 — Ingest

Build your catalog

Drop in any documents — PDFs, Word files, spreadsheets, CSVs. Awarity chunks and indexes them into a local catalog. No cloud upload required.

Step 02 — Map

Every chunk, in parallel

ECW sends every chunk through a fast, cheap model simultaneously. Each call is small — under the context limit — so no document is truncated. Notes are collected from all chunks.

Step 03 — Infer

One coherent answer

The notes — not the raw documents — are synthesized by your chosen frontier model. The infer context stays small regardless of catalog size. That's why ECW is 68–92% cheaper.

You know who else thinks context window size is important? Claude does.

Claude on context window size
Works with OpenAI Anthropic Gemini Llama Any LLM
Use it your way

Desktop app, CLI, or embedded in your stack

UI

Desktop app

Download Awarity for Mac and start querying your documents in minutes. Ingest files, build catalogs, run queries — no command line required. Free and self-contained.

CLI

Integrate into pipelines

The Awarity CLI drops into any existing workflow. Run awarity ask, awarity extract, or awarity for-each inside scripts, CI/CD jobs, or scheduled tasks. If you have a pipeline, Awarity plugs in.

API

Embed in your apps

Deploy Awarity as an Azure Function, AWS Lambda, or Docker container. First-class TypeScript and Node.js support. Call document reasoning from any application or service.

Windows Linux

Windows & Linux coming soon

We're building it. Drop your email and we'll let you know the moment it's ready — no spam, just one email when it ships.

Or email hello@awarity.ai with "Windows/Linux Waitlist" in the subject.

Download free.
Run in minutes.

Awarity runs entirely on your machine. Bring your own API key, ingest your documents, and start getting answers — no SaaS subscription, no data upload, no vendor lock-in.

Download for Mac Apple Silicon & Intel · Free
macOS 12+ required  ·  Windows & Linux coming soon