AI assistants and search decision
SHANNON LAB LLC
Keep paying — Shannon's proprietary models, curated adversarial benchmark (DarkEval), and compliance posture are durable advantages that a solo developer cannot realistically replicate.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.
What a replacement has to do
- Provide a chat UI that proxies user messages to an LLM API, store short-term memory, surface web-search citations, and support function-calling/tool hooks for agentic workflows.
What it still won’t have
- Shannon 2 proprietary model and performance
- DarkEval proprietary adversarial benchmark and exploit coverage
- Enterprise compliance guarantees (SOC 2 Type II, custom compliance mapping)
- Built-in web search with integrated source citations and their token-quota model
- Any gated access to constraints-relaxed red-team models and audit tooling
What remains hard
- Proprietary models
Our new flagship, built on the Kimi K2.7 trillion-parameter foundation — frontier-distilled on 30K curated reasoning examples and served in FP8 for professional red-team and security work.
- Proprietary data
Post-trained on 30,000 curated frontier-grade reasoning and instruction examples for sharper, more reliable answers.
- Compliance and regulation
We're SOC 2 Type II compliant.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 18 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal Shannon-like chat+API service using Next.js for the frontend, Node/Express (or Next API) backend, Postgres for memory, Redis for rate-limits/queues, and the OpenAI API (or a hosted open-model) as the LLM. Scope in-scope: streaming chat UI, authenticated API key proxy, token accounting, persistent short-term memory (lookup + store), web-search integration (call a search API and attach top-3 citations), basic function-calling adapter to invoke HTTP tools, and server-side logging. Out of scope: reimplementing Shannon proprietary models, DarkEval benchmark, enterprise SOC2 attestation, and a multi-tenant billing portal. Include error handling, retries, unit tests for API adapters, and end-to-end test for the chat flow.
How we checked
How the score was reached
- Pay verdict base20
- An open-source build was found+5
- 5 cited sources+3
- Price verified on pricing page+3
- Hard moats found in the evidence-9
- Evidence score22
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productShannon AI - Expert-Level Uncensored AI Assistant
- official pricingPricing | Shannon AI - Red-Team Intelligence Plans
- official docsShannon AI API Documentation
- open sourceopenclaw/openclaw
- open sourceleon-ai/leon
Integrity checks
What held up, and what did not.
