AI that creates — text, code, images — but needs governance for enterprise use.
Generative AI transformed how people interact with technology — enabling anyone to draft documents, write code, or answer questions in natural language. For enterprise automation, the power is real but so are the risks: LLMs hallucinate, they may produce different outputs for identical inputs, and they lack the auditability that regulated industries require. The critical distinction is between using generative AI as a tool within a governed system versus using it as the autonomous decision-maker. Kognitos uses LLMs for what they excel at — understanding natural language intent — but delegates all execution to a deterministic symbolic engine. This gives enterprises the accessibility of generative AI with the reliability of traditional rule-based systems, combining both worlds in a single neurosymbolic architecture.
Kognitos uses generative AI for what it is good at — interpreting intent expressed in plain English — and routes every action through a deterministic symbolic executor that cannot improvise, invent values, or deviate from declared rules. The result: an invoice approval, a journal entry, or a reconciliation posting will produce the same outcome from the same inputs every time. Generative output is never the system of record for a money-bearing decision; the symbolic executor is. This is why Kognitos is deployable in SOX, HIPAA, and GDPR workloads where pure-LLM agents are not.
Customer data flows only within your tenant. Kognitos enforces a hard training boundary — no customer prompts, documents, or extracted values are ever used to train upstream foundation models. Data is encrypted in transit and at rest, regional data residency is available in North America, EMEA, and APAC, and signed BAAs are standard. The model providers Kognitos uses operate under signed enterprise terms that contractually preclude training on tenant data. Your proprietary information stays proprietary.
Model selection is centrally governed by Kognitos engineering, with frozen versions per tenant and explicit upgrade windows. When a new foundation model becomes available, Kognitos benchmarks it against your historical execution logs before promotion. Because the symbolic executor — not the LLM — produces the final action, model drift cannot silently change a posting result. Customers see model upgrades as scheduled, validated transitions rather than as drift events; this is a structural advantage of neurosymbolic over pure LLM stacks.
Kognitos integrates with Azure OpenAI, AWS Bedrock, and Vertex AI as either an additional model substrate (with your enterprise agreement governing the LLM) or as a downstream consumer of outputs you already produce there. The neurosymbolic execution layer remains identical regardless of which foundation model substrate you select. This decoupling protects you from foundation model vendor lock-in and lets your AI Center of Excellence retain control over the underlying model contracts.
Yes — generative AI in Kognitos reads, classifies, and extracts; the symbolic executor decides and acts. For a multi-million-dollar invoice, the generative layer extracts line items, taxes, vendor identity, and PO reference; the symbolic layer applies the rule set (PO match, contract escalation clause, tax jurisdiction logic, segregation of duties) deterministically. The same applies to contract review: extraction is generative, redline-decisioning is symbolic, and every conclusion is logged in plain English for audit.
Artificial intelligence that generates new content — text, code, images, audio — by learning patterns from large training datasets. In enterprise automation, generative AI provides language understanding but requires deterministic guardrails to be safe.
Generative AI transformed how people interact with technology — enabling anyone to draft documents, write code, or answer questions in natural language. For enterprise automation, the power is real but so are the risks: LLMs hallucinate, they may produce different outputs for identical inputs, and they lack the auditability that regulated industries require. The critical distinction is between using generative AI as a tool within a governed system versus using it as the autonomous decision-maker. Kognitos uses LLMs for what they excel at — understanding natural language intent — but delegates
Kognitos uses generative ai to power zero-hallucination enterprise automation — described in plain English, executed with deterministic precision.
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