Your agents do not need more data. They need an ontology that decides what is true.
Rippletide builds your Decision Ontology automatically, from your documents, APIs, vector stores, workflow logs, and evaluated agent traces. Production ready in weeks, not quarters.
- First ontology pass in 2 weeks. Full enterprise integration in under 7 weeks.
- +15 points of agent outcome on average. Task completion rises from approximately 75% to approximately 90%.
- Auditable end to end. Every rule traceable to source, timestamped, attributable.
+15 points of agent outcome on average.
With the Rippletide Decision Ontology, agent task completion rises from approximately 75% to approximately 90% on evaluated production workloads.
Every enterprise has an ontology. Almost none of them know it.
Your business runs on rules. Refund thresholds. Credit pre approval logic. Compliance perimeters. IAM authority. SLA tiers. They live in PDFs, Confluence pages, API schemas, ticketing histories, and in the heads of people who left two quarters ago.
Your agents do not have access to any of that as a coherent model. They have access to fragments, retrieved by similarity, not by applicability. They guess. And when they guess wrong on a refund, a credit decision, or a compliance call, nobody can prove what rule was supposed to apply.
Manual ontology modeling has been the answer for twenty years. It takes six to twelve months, requires a dedicated team, and the moment it ships, it is already stale. Forward deployed engineers do not scale. Static ontologies do not survive contact with reality.
There is a faster path. We build your Decision Ontology automatically, and we keep it honest.
Not a knowledge graph. Not a semantic layer. A Decision Ontology.
Knowledge graphs store what your company knows. Semantic layers align what your company names. Neither tells your agents what to do.
A Decision Ontology stores what your company decides, and under what conditions. It is the typed, versioned, auditable representation of:
Entities
The things your agents can act on: customers, accounts, contracts, tickets, incidents, payments.
Rules
The conditions that govern those actions: thresholds, authorities, exceptions, escalation paths.
Policies
The constraints that surround them: compliance, IAM, contractual obligations, regulatory perimeters.
Outcomes
The verified results that validate them: what worked, what did not, under which conditions.
It is the substrate that makes the difference between an agent that sounds right and an agent that is right.
The Decision Ontology Manifesto
Five principles. The reason this category exists.
Decision first, not description first.
An ontology that does not encode decisions is documentation. Useful, but not enforceable. Decision Ontologies are wired into runtime enforcement from day one.
Outcome conditioned.
Rules are derived from evaluated outcomes, not from authored intent. A rule that does not correlate with successful results is a hypothesis, not a rule.
Automatically derived, humanly governed.
Bootstrap by machine. Validate by human. Edit in natural language. The expert’s job is judgment, not modeling syntax.
Living, not laminated.
The ontology updates from new policies, new APIs, and new agent traces continuously. There is no "version 1" frozen in amber.
Auditable end to end.
Every entity, every rule, every contradiction surfaced. Traceable to source, timestamped, attributable. Compliance is a built in property, not a deliverable.
Inject. Structure. Validate. Repeat.
Rippletide Automatic Ontologies runs a closed loop across four stages. Every iteration makes your ontology more complete, more accurate, and more aligned with how your business actually decides.
Inject: Connect any source
The Rippletide engine ingests, in parallel: documents (policies, contracts, SOPs, runbooks, onboarding guides, regulatory texts, any format), APIs and system schemas (CRM, ERP, ticketing, payment systems, identity providers), vector stores (existing RAG corpora and embeddings, recomposed into structured meaning), workflow logs and execution histories (what your processes actually do, not what their diagrams claim they do), and evaluated agent traces (every action your agents take, paired with its verified outcome).
A trace without an outcome is a sequence of actions. A trace with an outcome is a learning signal. Rippletide does not learn from noise. It learns from results.
Structure: Derive a candidate Decision Ontology
The Rippletide engine derives a candidate Decision Ontology: entities, typed relationships, rules, validity windows, exceptions. Neuro symbolic reasoning where it matters: LLM based extraction for breadth across heterogeneous sources, symbolic reasoning for type consistency, contradiction detection, and deterministic rule encoding.
Every fact ships with provenance: which document, which clause, which API field, which trace produced it. No black box. Full lineage from day one.
Validate: Notion style editor in plain English
A Notion style editor lets your domain experts review and edit the ontology in plain English. No DSL. No SPARQL. No graph query language. The expert writes a sentence; the system encodes the rule deterministically.
When Rippletide is uncertain, because the source documents are themselves ambiguous, it does not guess. It proposes two alternatives, A or B, and asks the human to choose. The choice is encoded. The reasoning is logged.
System of Record Integrity: Contradictions surfaced, routed, resolved
As Rippletide structures your sources into a Decision Ontology, it surfaces the contradictions. Policy A says one threshold. SOP B says another. The API schema enforces a third. The agent traces reveal a fourth, applied informally for six months. Rippletide flags every conflict and routes it to the right owner.
You do not just get an ontology. You get the truth about your documentation.
Anonymized signals from recent enterprise deployments
| Engagement type | Source scope | Contradiction signal |
|---|---|---|
| Tier 1 enterprise software vendor | Evaluated agent traces | 4% to 8% of traces flagged for inter document policy conflict |
| Global automotive OEM, credit pre approval agent | Documented process and policy | 20 to 40 contradictions per 100 policy PDFs, including IAM authority violations embedded in approved workflow |
| Tier 1 financial services group | API schemas vs. compliance policy | 12 to 18 policy schema mismatches surfaced in first ontology pass |
You do not just get an ontology. You get the truth about your documentation.
Outcomes, not vibes.
The Decision Ontology is not a research artifact. It is wired directly into the Rippletide decision runtime, where every agent action is evaluated against it before execution.
We measure agents on outcome based evaluation: did the agent complete the task with the expected result, verified against ground truth?
| Configuration | Outcome OK |
|---|---|
| Production agent without Rippletide Decision Ontology | ~75% |
| Same agent with Rippletide Decision Ontology | ~90% |
+15 points on average. Across customer support, credit decisioning, IT operations, and code generation agents.
This is not a leaderboard benchmark. It is what your agents do in production once they stop guessing.
From documentation to decidability.
From 6 months of manual modeling to 2 weeks of automatic bootstrap
A first production grade ontology pass ships in 2 weeks. Full enterprise integration ships in under 7 weeks. Your data architects stop modeling. They start curating. Your subject matter experts stop writing slide decks no agent will ever read. They start editing the ontology in natural language.
From stale documentation to living ontology
Every evaluated trace tightens the ontology. Every policy change is detected. Every contradiction is surfaced. The ontology that runs in production today is the one your business actually operates on, not the one you wrote 18 months ago and never re read.
From “trust me” to provable agent decisions
Every agent action is evaluated against typed entities, explicit rules, and verified facts. Every decision carries a full causal trace. Your compliance team gets the answers before they ask the questions. Your board gets evidence, not assurances.
From fragmented retrieval to applicability
The Decision Ontology feeds the Rippletide Context Graph. Your agents stop retrieving by similarity and start retrieving by applicability, the right context for this action, now.
Eighteen months of R&D. Production deployments. Public proof.
Automatic Ontology generation is the production application of years of neuro symbolic AI research led by Yann Bilien, Rippletide’s Chief Scientist (Imperial College).
Yann presented the approach at the inaugural Context Graph Meetup, hosted with Neo4j in San Francisco on February 26, 2026: “Automatic Ontologies for Trustworthy AI Agents.” The talk covers eighteen months of R&D, production case studies (testing agents on every commit, a credit decision system for a global automotive manufacturer), and the architecture that combines structured knowledge, embedded process, and deterministic enforcement.
The Rippletide engine combines:
- LLM based extraction for breadth across heterogeneous sources
- Symbolic reasoning for type consistency, contradiction detection, and rule encoding
- Outcome conditioned learning from evaluated agent traces
- Human in the loop validation through natural language editing
Backed by published research, production deployments at design partner enterprises, and a runtime engine evaluated under SOC 2 Type II, GDPR, and CCPA controls.
Where Decision Ontologies pay back, in weeks.
Every use case below is structured around a single question: where do agents make consequential decisions today, and how does an automatic Decision Ontology make them provably correct, faster than manual modeling could?
Customer Support
Refunds, escalations, entitlements.
- Typical scope: Tier 1 support automation, refund pre approval, churn prevention offers.
- Outcome lift: +12 to +18 points on policy compliant resolution rate.
- Compliance signal: every decision traceable to the policy clause that authorized it.
Credit and Financial Decisioning
Pre approval, KYC, risk.
- Typical scope: credit pre approval agents, KYC pre screening, fraud flag triage.
- Outcome lift: +10 to +20 points on policy compliant decision rate, with documented audit trail.
- Risk signal: every authority boundary tested against the ontology before execution.
IT Operations and Incident Response
Severity, blast radius, change management.
- Typical scope: incident response agents, automated remediation, change approval pre checks.
- Outcome lift: +15 points on correct action taken rate, with full post mortem traceability.
- Operational signal: no automated remediation runs outside its authority envelope.
Coding Agents
Governed code and architectural compliance.
- Typical scope: code generation, code review, dependency update pull requests.
- Outcome lift: +15 points on first time merge rate; sharp drop in security policy regressions.
- Engineering signal: patterns enforced at write time, not flagged at review time.
Background AI Analysts
Governed analysis, versioned assumptions.
- Typical scope: competitive analysis, forecasting, regulatory monitoring, executive briefings.
- Outcome lift: verifiable lineage from claim to source on every output.
- Governance signal: no analyst output references invalidated facts.
The old way does not scale. The new way is already in production.
| Static ontologies | Decision Ontologies (Rippletide) | |
|---|---|---|
| Bootstrap | 6 to 12 months, forward deployed engineers | 2 weeks automatic, expert validation only |
| Source coverage | One canonical schema, manually maintained | Docs, APIs, vector stores, workflow logs, evaluated traces, ingested in parallel |
| Contradictions | Unknown until production fails | Surfaced and routed during structuring (System of Record Integrity) |
| Maintenance | Quarterly review cycle, often skipped | Continuous, outcome conditioned from evaluated traces |
| Validation interface | Modeling DSL, ontology engineer required | Notion style editor in plain English; A or B proposals when uncertain |
| Outcome lift | Unmeasured, not wired to agents | +15 points outcome OK on average |
Frequently asked questions.
Common questions about Automatic Ontologies. Need more detail? Talk to our team
A knowledge graph captures what is known. A semantic layer aligns what is named. A Decision Ontology encodes what is decided, and under what conditions. It is action oriented, outcome conditioned, and wired directly into runtime enforcement. It is the substrate that makes agent decisions provable, not just plausible.
Source ingestion, candidate ontology generation, contradiction detection, validity window inference, and continuous refinement from evaluated traces. The human stays in the loop for validation, but does so in natural language, not in a modeling DSL. Typical first pass: 2 weeks instead of 6 months.
Documents in any format (PDF, Word, Confluence, Notion, SharePoint, contracts, SOPs, runbooks), APIs and system schemas (CRM, ERP, ticketing, payments, identity), vector stores (existing RAG corpora and embeddings), workflow logs and execution histories, and evaluated agent traces. The breadth is intentional: enterprise knowledge never lives in one place.
A trace without an outcome is a sequence of actions. A trace with an outcome is a learning signal. Rippletide only ingests evaluated traces (actions paired with verified results) because that is what makes them usable for rule derivation. We do not learn from noise. We learn from results.
As Rippletide structures your sources into a Decision Ontology, it detects contradictions across them: policies that disagree, SOPs that violate API constraints, IAM authority gaps embedded in documented workflows. Every conflict is flagged and routed for resolution. In recent enterprise deployments, contradiction rates ranged from 4% to 8% of evaluated traces, to 20 to 40 contradictions per 100 policy PDFs.
No. Rippletide is additive. If you already have an ontology, we ingest it, validate it against your sources, surface contradictions, and extend it with the rules and outcomes you did not have before.
Through a Notion style editor. Your domain experts review proposed entities, relationships, and rules in plain English. When Rippletide is uncertain, it proposes two alternatives (A or B) and asks the expert to choose. Edits in natural language are encoded deterministically. No graph query language. No DSL.
On outcome based evaluation, agent task completion rises from approximately 75% to approximately 90% on average across deployments. +15 points. Measured on real production workloads against ground truth outcomes, not on leaderboard benchmarks.
First production grade ontology pass in 2 weeks. Full enterprise integration in under 7 weeks, including source connectors, validation cycles, and runtime enforcement.
The Decision Ontology is yours. Exportable, versioned, auditable. Rippletide is the engine; you own the model. Confirm export modalities with our engineering team during the validation sprint.
The Decision Ontology defines the structure. The Context Graph stores the live facts within that structure, with temporal validity, provenance, and scope isolation. Together they form the reasoning substrate that the Rippletide decision runtime evaluates every agent action against.
Related reading.
Automatic Ontologies
Your agents are already deciding. Give them an ontology worth deciding from.
We start with a 2 week validation sprint. We connect to your sources, generate your first Decision Ontology, and surface the contradictions in your documentation along the way. No commitment. Concrete results.
- First ontology pass in 2 weeks
- +15 points outcome OK on average
- System of Record Integrity from day one
- Auditable, exportable, yours