Never let your AI Agents work on stale Knowledge again.
Your AI agent has a knowledge problem — your business moves on but its knowledge base remains static. Alchemyst fixes that.
Alchemyst AI is a context layer for AI agents that provides persistent memory and semantic retrieval through a single API — keeping an agent's knowledge current so it never works on stale knowledge. It delivers sub-50ms retrieval with a 99.9% uptime SLA and zero infrastructure to manage.
The Silent Problem
Enterprise AI doesn't fail because the model is bad.
It fails because the context rots.
GPT-4, Gemini, Claude — they're all capable enough. The gap between a capable model and a truly intelligent product is the layer that keeps its knowledge current, traceable, and semantically consistent across your entire organization.
Semantic Consensus breaks silently
Semantic Consensus"Revenue" means $500K to your CFO and $5M to your Sales team. Your AI agent doesn't know which one is right — and acts with false confidence on whichever it finds first.
Ontologies rot from day one
Context RotEvery knowledge graph starts accurate. The decay begins the moment you ship it. New pricing tiers, new segments, new teams — the schema never updates itself. Agents keep acting on a version of your business that no longer exists.
Tractability is the missing primitive
AuditabilityYou can't audit what you can't trace. Without knowing exactly what context an agent had when it made a decision, debugging failures is guesswork. Auditability across agentic tasks requires a traceable context layer — not just logs.
Manual FDE teams don't scale
ScalabilityPalantir solves this with entire teams of forward-deployed engineers embedded in every client. That works at $50M+ contracts. It doesn't work for the rest of the market. There has to be a better way.
How Semantic Drift propagates through your organization
“If structured data drift almost killed Zillow — imagine what semantic driftcan do to your AI-driven organization.”
— Anuran Roy, Semantic Consensus and Semantic Drift
What Alchemyst Does
A context layer that keeps your AI current, traceable, and semantically consistent.
One API call. Three memory layers. Full context arithmetic. Every decision traceable back to its source — without managing a single vector database or graph store.
Three-layer context architecture
Episodic memory (what happened), semantic memory (what it means), and procedural memory (how to act) — all queryable through a single API. Your agent always has the right layer for the right task.
const ctx = await alchemyst.context.get({
episodic: { session_id, last_n: 10 },
semantic: { query: userMessage },
procedural: { task: "outbound_call" }
});Context Arithmetic — not just RAG
Instead of naïve top-K similarity search, Alchemyst performs set operations on your data: union across sources, intersection across relevance criteria, and explicit subtraction of stale or irrelevant context.
// Union: merge user history + live CRM data
// Intersect: only what satisfies both criteria
// Subtract: exclude superseded decisions
const window = ctx.union(history, crm)
.intersect(relevantToQuery)
.subtract(outdatedPricing);Context Traces for full auditability
Every agent decision is traceable back to the exact context it had. Not a summary — the exact data points, ranked and filtered, that went into the model's context window. Debug in minutes, not days.
const trace = await alchemyst.trace.get(
session_id, turn_id
);
// Returns: sources[], scores[], rules_applied[]
// Pairs with Euphony for visual debuggingSemantic consensus enforcement
Define canonical term definitions at the org level. When "revenue" means different things to different teams, Alchemyst resolves the ambiguity before it reaches the model.
await alchemyst.ontology.define({
term: "revenue",
canonical: "ARR as reported to board",
aliases: ["sales", "bookings", "ARR"],
owner: "finance",
updated_at: new Date()
});Example Use Case
You can't debug what you can't see — Context Tracing with OpenAI Euphony
Pairing Alchemyst's Context Traces with Euphony — OpenAI's open-source conversation visualizer — creates an end-to-end debugging workflow. Every agent failure is now diagnosable in minutes: was it a retrieval problem, a configuration problem, or a model problem?
The Context Thesis
Why we're building the memory layer for AI.
Intelligence without memory is performance, not understanding.
A model that can answer any question but remembers nothing is a search engine, not an agent. True intelligence requires the ability to learn from experience — to carry forward what was said, decided, and discovered.
Context is the compound interest of AI interactions.
Every interaction is an investment. Without context, that investment expires at the end of the session. With context, each interaction builds on the last — the agent gets smarter, more personalized, and more valuable with every use.
The model is not the bottleneck. The infrastructure is.
GPT-4, Gemini, Claude — they're all capable enough. The gap between a capable model and a truly intelligent product is the layer that gives it memory, continuity, and awareness of the world it operates in.
Context should be a primitive, not an afterthought.
Developers shouldn't have to build context management from scratch for every AI product. It should be as simple as calling an API — ingest, retrieve, and let intelligence compound.
“The model is the engine. Context is the fuel.
Without it, you're not going anywhere.”
— Alchemyst AI, Context Thesis