What people actually build with SynapCores
Honest shape-of-problem stories, each tied to a runnable recipe you can verify on your own machine in five minutes. No invented stats. No fake testimonials.
We don't have customer logos yet. We're early. So every use case here links to a recipe in our library that you can run yourself in five minutes. That's the demo. If you want help wiring it into your own workflow, the Agent Memory JumpStart is how.
Your support bot, internal copilot, or developer agent holds context across sessions — without stitching together a vector DB, graph DB, cache, and LLM API.
Common pain
- Stateless sessions: the agent forgets the user the moment the tab closes.
- Vector-only memory: similarity search retrieves rows but loses relationships (this ticket → this customer → this past resolution).
- Five-service stack: Pinecone + Postgres + Redis + LLM API + LangChain memory becomes fragile and impossible to debug.
SynapCores shape
- One engine for semantic recall, graph relationships, and durable state.
- Native MCP support so Claude Code / Cursor / VS Code can read memory tables directly.
- Inspectable retrieval traces — see exactly which memory the agent used to answer.
Vector retrieval alone can’t answer "what other accounts has this customer touched, and what did we say about them?" Graph traversal can. SynapCores does both in one query.
Common pain
- Vector-only RAG returns adjacent snippets but misses multi-hop reasoning.
- Standalone graph databases miss semantic similarity.
- Keeping a graph DB and vector DB in sync is a synchronization nightmare.
SynapCores shape
- Cypher graph traversal + vector similarity in the same SQL statement.
- Single index lifecycle — no double-writes, no cross-system staleness.
- Cite the path: every answer traces back through the graph nodes that produced it.
A fraud ring is structural (shared addresses, devices, payment methods) AND statistical (behavior anomalies). Most teams need two systems. We do it in one.
Common pain
- Rules-only systems miss novel ring structures.
- Pure ML scoring miss the link evidence that explains the call.
- Investigators need both the anomaly score AND the graph path to take action.
SynapCores shape
- Graph traversal surfaces account clusters sharing devices/IPs/cards.
- AutoML or LLM scoring labels each cluster with risk + reasoning.
- One query returns both — ready for the investigator queue.
Find every account, ticket, document, or contract that "looks like" a known good or bad example — across structured columns AND unstructured text.
Common pain
- SQL LIKE / full-text search misses meaning ("MRI complications" ≠ "scan side effects").
- Vector-only search forgets the structured filters (region, plan tier, date).
- Hybrid search across two systems requires custom merge + re-rank code.
SynapCores shape
- EMBED() and COSINE_SIMILARITY() are first-class SQL operators.
- Combine semantic search with regular WHERE clauses in one statement.
- Ranked, filtered, paginated results without an orchestrator.
Want to run one of these on your data?
The Agent Memory JumpStart is a two-to-four-week founder-led sprint where we wire SynapCores into your real workflow. Free Design Partner slots or fixed-fee Paid Pilot. Talk to the engineer who built it.