Synaptik Core

Who Is Synaptik For?

Synaptik Core is designed for any AI system that needs governance, accountability, and audit.

Multi-Agent Systems with Restricted Information

  • • Multiple agents collaborate (retrieval, tool, planner). Some sources are restricted, including PII or internal documents.
  • • Only specific agents or memory scopes can access certain information. Must prove compliance.
  • • Why it's broken: Permissions exist at the app layer, but memory is a shared blob. Once information enters context, there's no way to prove it was "forgotten" after use.

ML Workflow Provenance and Reproducibility

  • • A team trains and ships models regularly. They need to answer exactly what data, features, and configurations produced a specific artifact.
  • • Must know what changed since the previous run and what human overrides were applied.
  • • Why it's broken: ML tooling tracks artifacts, but decision state lives across scattered logs, notebooks, and tickets. Reproducibility is manual. No unified, enforceable memory.

Regulated AI Decision Workflows

  • • Enterprise runs AI-assisted workflows for approvals: refunds, claims, clinical coding, vendor access.
  • • System must remember prior interactions, policy changes, and exceptions, and prove what it relied on for each decision.
  • • Why it's broken: Current stacks reconstruct context after the fact, but can't prove restricted data wasn't used or that policy constraints were applied at runtime.

RAG Systems with Document-Level Access Control

  • • RAG over internal documents where some are confidential, some public, some require specific clearance.
  • • Prove which documents influenced an answer and that restricted ones weren't used without authorization.
  • • Why it's broken: Retrieval logs show what was fetched, but context is merged before generation. No proof restricted content didn't leak into the response.

Chained Model Pipelines with Silent Failures

  • • Pipeline chains models: classifier → extractor → summarizer → generator. Output is wrong.
  • • Must identify exactly which model introduced the error or drift.
  • • Why it's broken: Each model logs independently. No unified trace across the chain. Failures appear at the end with no upstream visibility.

Agentic Systems Taking Irreversible Actions

  • • Agent can call APIs, write to databases, send emails, execute transactions.
  • • Before each action executes, verify it's authorized and log why it was taken.
  • • Why it's broken: Guardrails often run post-hoc or in parallel. Action may execute before policy check completes. Can't prove intent was gated before execution.

Why Synaptik?

Synaptik sits outside the model as a governance layer. It enforces policy at admission, creates tamper-proof audit trails, and preserves memory with provenance.

Without this layer, AI systems rely on logs and prompts after the fact. With it, you get enforceable guarantees at decision time.

Works with any stack:

Chatbots
LangChain / CrewAI
Custom inference
Fine-tuned models
Multi-model chains
Any AI system

Think Synaptik might be right for you?

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