Synapcores

The problem. Agents demo in an afternoon and die in production. To actually run one you assemble a stack — orchestration framework, queue, worker fleet, vector store, graph DB, memory layer, audit tooling — and then own the seams: data leaving your perimeter to be embedded, state scattered across four systems, no provable record of what the agent did or why. The complexity isn't in the model; it's in the plumbing around it. For whom. Teams putting agents against data they can't move — regulated or security-sensitive workloads where sovereignty and provable audit are non-negotiable. Concretely: security ops (SOAR), AML/compliance, claims, legal. The buyer is technical — CTO, VP Eng, platform/data lead — at organizations where "just send it to a hosted vector DB" ends the conversation. Two motions: builders pulling the free CE binary, and integrators/consultancies delivering it into enterprise accounts. What's different. Intelligence belongs where the data lives. Your agents run outside your data; SynapCores agents run inside it. That cashes out three ways: The database is the agent runtime. CREATE AGENT is DDL — a durable database object that fires on cron or on committed DML. No orchestrator, no queue, no worker fleet. AGENT_RUN(persona, task) is callable as a SQL function, so an agent invocation is just an expression in a query. One engine, one connection, one auth token. SQL, Cypher graph traversal, vector search (EMBED/COSINE_SIMILARITY), agent memory (MEMORY_*), LLM completion, NL→SQL, multimodal ingest and native in-process vision — same data, same transaction boundary. Not Pinecone + LangChain + Neo4j + Redis stitched together. Governance by construction. Every run hash-chained into a tamper-evident ledger. Provable, not just logged. And the structural one against Oracle et al.: they're bolting AI onto an engine designed decades before it. This is AI-native from the ground up — which is why the AI surfaces are inside the query language rather than beside it.