Open source · Apache-2.0

Living understanding for your AI.

Coalent turns your sources into decision-ready understanding — reused across every query and agent, and re-synthesized the instant a source changes. Not chunks. Not stale.

pip install coalent ·  sits on top of the stack you already run

Understanding · freshserved · cache hit

Customers can be refunded within 30 days; partial refunds are pro-rated.

Window
30 days
Partial
Pro-rated
Auto-approve
Under $500
Sourceszendesk:refund-guidenotion:billing-policy
Edit a source and watch it stay correct.

↑ live — pick a question, then change a source.

Works with the tools you already run

QQdrant
ChChroma
pgpgvector
GrGraphRAG
AiOpenAI
AnAnthropic
LgLangGraph
CwCrewAI
MMCP tools
CfConfluence
JJira
SqSQLite
QQdrant
ChChroma
pgpgvector
GrGraphRAG
AiOpenAI
AnAnthropic
LgLangGraph
CwCrewAI
MMCP tools
CfConfluence
JJira
SqSQLite
The shift

Stop feeding your AI chunks.

Give it understanding it can act on — and that stays correct.

Traditional RAG · top-k chunks
…clause 4.2 of the policy states that in the event…
…see appendix B for the 30 day window which may…
…unless otherwise approved by a manager per…
…deprecated: previously the window was 14 days…

Noisy, redundant, sometimes contradictory. Your LLM does the sifting — every time.

Coalent · understanding

Customers can be refunded within 30 days; partial refunds are pro-rated.

Window
30 days
Partial
Pro-rated
Approve
< $500

Distilled, structured, cited — and rebuilt the moment a source changes.

Why Coalent

Understanding, reused, and always fresh.

Real understanding

Coalent synthesizes your sources into a structured, decision-ready briefing — summary, claims, facts — not a pile of fragments.

Reused everywhere

Built once, matched by meaning, reused across every query, session, and agent. Less retrieval, less LLM spend, lower latency.

Always fresh

When a source changes, only the understanding that used it is rebuilt — surgically, the moment it changes. Never stale.

Measured, not asserted

Cheap and fresh — proven.

Coalent ships its own benchmark with an independent oracle and real token cost. The result against naive RAG and a provenance-less cache:

Naive RAG
Correct100%
Stale0%
Cost70
always fresh — but pays every read
Stale cache
Correct80%
Stale20%
Cost35
cheap — but goes stale
Coalent
Correct100%
Stale0%
Cost49
fresh AND ~30% cheaper

0% stale  ·  ~0% cheaper than always-fresh  ·  see the benchmark →

Sits on your stack

Bring any retriever. Keep your model.

Coalent is the layer above retrieval — not a replacement. Plug in your vector DB, GraphRAG, tools, or APIs, and your model of choice. It's one call from any agent framework.

QdrantChromapgvectorGraphRAGOpenAIAnthropicLangGraphCrewAIMCP toolsConfluenceJira
from coalent import SemanticCache, QdrantRetriever, LLMSynthesizer, OpenAIProvider

cache = SemanticCache(
    QdrantRetriever(client=qdrant, collection="docs", embed=embed),
    LLMSynthesizer(OpenAIProvider()),
)

ctx = cache.get("what is our refund policy?")   # decision-ready, fresh

Store understanding, not data.

Caches go stale because they store answers without knowing where they came from. Coalent keeps the lineage — so it always knows exactly what to forget.

The Coalent principle

Give your AI living understanding.

Open source, Apache-2.0. Sits on top of what you already run.