Meet thinking sense.
Ask a question that spans Oracle EBS, SAP, Salesforce, ServiceNow, SharePoint, and Snowflake — in plain English. thinking sense grounds your intent, compiles a governed plan, and returns one auditable answer. In milliseconds. Every execution trace becomes clean training signal for task-specific models — so thinking sense gets sharper with every workflow. Route to any model, or fine-tune SLMs for higher precision at lower token cost.
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What early teams are seeing
Here’s how thinking sense delivers this.
thinking sense compiles intent · executes and governs
Layer 6 of 7
Where the Governed Ontology Layer sits in the stack
The industry has mapped five layers of knowledge architecture — from Taxonomy up through Context Graph. thinking sense's Governed Ontology Layer is Layer 6: the first layer that doesn’t retrieve context for a model to synthesize, but executes a governed, typed plan against live data — and generates clean training signal for task-specific models in the process.
The enterprise agent hierarchy
thinking sense enables every level.
Incumbents automate within their record. General-purpose agents reach across systems but hit semantic drift — every application describes the same customer differently. thinking sense resolves that before any model sees the data.
The result: all four agent types, operating across all enterprise data, with institution knowledge that compounds with every use.
Finds information, summarizes documents, answers questions across connected systems.
thinking sense enables this
Semantic resolution means "revenue" means the same thing across SAP, Salesforce, and Snowflake before the model sees it. No more three dashboards giving three different numbers.
Carries out rule-based work across systems — update records, route approvals, trigger workflows.
thinking sense enables this
Policy is compiled at plan time, not checked at runtime. Unauthorized actions fail before they reach any system. Swap the model and the policy executes identically.
Applies the organization's playbooks, thresholds, and precedents to ambiguous cases across boundaries.
thinking sense enables this
The ontology holds policies across all systems. A credit threshold extracted from a contract document governs queries against financial data — automatically, without manual wiring.
Makes open-ended judgment calls about strategy, risk, and resources — the work that requires knowing how this firm thinks.
thinking sense enables this
Institution learning through RLBUF. Every verified decision compounds into task-specific SLMs that learn how this enterprise makes those calls — not how the profession does it generically.
Framework: Seema Amble, a16z — “The Incumbents Are Coming” (Sep 3, 2026)
The IP
The Governed Ontology Layer
SQL optimizers transformed relational databases. thinking sense's Governed Ontology Layer does the same for enterprise AI — across structured databases, document stores, and vector indexes, generating governed, verified execution traces that train task-specific models.
Structured + unstructured. Governed the same way. Fine-tuned for your business.
01 · Multimodal grounding
Structured, document, and vector — one semantic graph.
thinking sense compiles against the enterprise knowledge graph across all source types. SQL indexes for structured data. Full-text indexes for documents and contracts. Distance-based vector similarity for images, video, audio, and text. Ontology traversal and GraphRAG for graph-augmented retrieval. All grounded before any model sees the query.
02 · Federated execution
One SQO plan spans every source type simultaneously.
A single optimized plan fans out across SQL dialects, REST APIs, document databases, and vector indexes in one execution. Your Oracle EBS financials, your SharePoint contracts, and your S3 video archive answer the same question — together. Data stays in place. No integration tax per source type.
03 · Policy compilation
The same governance whether the answer comes from a database or a video transcript.
RBAC and ABAC are compiled into the SQO plan before execution — regardless of source type. A vector similarity search against customer footage is governed identically to a SQL query against financial records. Unauthorized queries fail to compile. There is no path to the data. Swap the model and the plan executes identically.
How thinking sense is different
Search retrieves. thinking sense executes.
| Search-based retrieval (e.g. Glean, Microsoft Copilot) | thinking sense | |
|---|---|---|
| How it works | Index content → retrieve relevant passages → LLM synthesizes an answer | → Compile query against semantic graph → execute federated plan → return verified answer |
| Data it reaches | Documents, wikis, tickets, conversations — modern SaaS with APIs | → Structured databases, document stores, vector indexes, legacy ERP — all source types |
| Legacy systems | No — requires modern APIs and connectors | → Yes — Oracle EBS, SAP, JD Edwards, systems with no REST API |
| Governance | Permission-aware at retrieval time — can this user see this document? | → Policy compiled at plan time — unauthorized queries never reach the data |
| Answer type | Synthesized from retrieved passages — qualitative, cited to source docs | → Verified execution result — governed, typed, 98–100% accuracy on covered questions |
| Audit trail | Document citations — what was retrieved | → Full query lineage — what data was touched, by whom, under which policy |
| Token cost | Grows with corpus size and query volume | → Fixed — semantic cache absorbs repeated patterns, compiled not re-planned |
thinking sense governs every source type the same way. That’s the enterprise superset.
Platform in action · thinking sense
Ask in plain language
Type a question the way you'd ask a colleague — no SQL, no data model knowledge required.
Watch thinking sense ground your intent
thinking sense resolves entities, checks permissions, and assembles context from enterprise sources before building a plan.
See the governed answer
Every response comes with the execution trace — what ran, what was checked, and why the answer can be trusted.
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The execution layer
thinking sense + OmniGate
Two layers, one stack. thinking sense is the intelligence layer — it understands, governs, plans, and learns. OmniGate is the execution engine beneath it — connecting, federating, routing, and running the query against pooled enterprise backends.
thinking sense decides what should happen. OmniGate makes it happen. No query reaches a backend without passing every governance check first.
Every client protocol — MCP, gRPC, JDBC/ODBC, SQL — lands through a health-checked load balancer onto identical thinking sense nodes
NL2SQL conversion, SQL firewall, RBAC/ABAC injection, and QoS enforcement — all applied before any query reaches a backend
Cache hit, shard scatter-gather, primary/standby failover, or target backend — resolved per request with sub-millisecond cache lookup
Pooled backends execute; results return to thinking sense for eval and learning. Every answer traceable end to end.
Ready to deploy thinking sense in your enterprise?
thinking sense · Early access 2026
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