Answers are scattered across too many documents.
The evidence may live in claim forms, notes, policies, contracts, case files, medical records, or operational systems. Finding it manually takes time.
Orion retrieves the right enterprise evidence before generating an answer — so teams can summarize cases, review documents, compare policies, identify risks, and make decisions with sources they can verify.
Orion is the reasoning layer of OmniSuite™. It turns classified documents and extracted data into answers, summaries, recommendations, and decision support. Instead of generating from model memory alone, Orion retrieves relevant enterprise content first. Then it reasons over that evidence and produces an output users can inspect, verify, and act on. That insight can support reviewers directly, feed host systems, or become the input for Polaris workflow automation.
Orion is the reasoning layer. It uses the organized content from Chicago and the structured facts from Nashville to produce grounded insight that supports human reviewers or drives Polaris workflows.
In high-stakes enterprise work, an answer is not enough. A claims reviewer, compliance analyst, benefits worker, loan officer, or healthcare administrator needs to know where the answer came from. Which document supports it? Which clause applies? Which record contradicts it? Which policy changed? Which evidence is missing?
Generic AI can sound confident without being grounded. That creates risk in workflows where decisions must be explained.
The evidence may live in claim forms, notes, policies, contracts, case files, medical records, or operational systems. Finding it manually takes time.
A fluent response is not the same as a verified one. In regulated work, every conclusion needs a source.
Summarizing documents is helpful, but teams also need to compare facts, find inconsistencies, apply rules, and understand what should happen next.
Orion is built around a simple idea: do not let the model guess when the enterprise already has the evidence. When a user asks a question, Orion retrieves the most relevant documents, records, extracted fields, or repository content first. Then it generates an answer inside that evidence boundary. The result is not just a response. It is grounded decision support: an answer, the supporting sources, and the context needed to trust it.
Ask questions against policies, claims, records, contracts, case files, medical documents, or enterprise repositories.
Turn long packets, document sets, and repository results into concise summaries users can review quickly.
Identify inconsistencies, missing information, policy conflicts, eligibility gaps, or risk signals across multiple sources.
Generate suggested actions based on the retrieved evidence, business rules, and workflow context.
Orion retrieves first, reasons second, and generates only after the relevant evidence has been assembled.
A user, host application, or workflow asks Orion for an answer, summary, recommendation, or decision-support output.
Orion searches across documents, extracted fields, repositories, operational records, and curated knowledge stores to assemble the right context.
The model reasons over retrieved content instead of relying on unconstrained model memory.
Orion generates answers, summaries, recommendations, and explanations with source references and supporting context.
Outputs can support human reviewers, update host systems, or trigger Polaris workflows.
Not every enterprise question is answered by one document. Orion supports different retrieval patterns depending on the type of content, complexity, and workflow.
Best for questions where the answer lives in a known set of policies, records, forms, claims, contracts, or operational documents.
Best for questions that require relationship-aware reasoning across multiple sources, entities, events, policies, or records.
Best for documents where important evidence lives outside plain text, including tables, diagrams, forms, images, and visual structures.
Real-time reasoning over inbound content.
Designed for transaction-heavy workflows where teams need answers from documents moving through active processes.
Deeper analysis across connected evidence.
Adds Graph RAG and more advanced contextual analysis for complex, multi-source questions.
Reasoning across enterprise repositories.
Extends insight across larger knowledge stores, shared repositories, and organization-wide content collections.
A chatbot answers from what the model already knows or what the prompt provides. Orion starts by finding the right enterprise evidence. That changes the role of generative AI. The output becomes easier to verify, easier to audit, and safer to use in regulated workflows.
Orion does not ask users to trust the model. It shows them the evidence.
Teams read full packets by hand, hunting for the clauses and records that matter to each case.
Generative answers may be fluent but can’t be tied back to a source — risky in regulated work.
Without a retrieval boundary, models invent details that look right but aren’t supported by your content.
Auditors and regulators can’t trace which document drove an outcome — the trail starts and ends in conversation.
Policies, procedures, and historical records remain inert documents instead of an answerable knowledge base.
Insight from one team has to be re-keyed before it can become an action in the next system.
Teams can ask questions across large document sets instead of manually reading every page.
Users get summaries, recommendations, and explanations grounded in the evidence they already trust.
Responses are constrained by retrieved enterprise content, reducing unsupported answers.
Users can trace outputs back to the source documents, fields, clauses, and records behind them.
Policies, procedures, records, and historical documents become searchable and actionable.
Once insight is grounded, Polaris can use it to trigger governed actions, escalations, and workflow steps.
Ask whether a claim qualifies, which policy applies, what evidence is missing, or whether fraud indicators exist.
Identify which internal policies, procedures, or controls are affected by a new regulation or audit finding.
Evaluate medical documentation, treatment records, payer rules, and plan criteria with source-backed reasoning.
Review loan files, underwriting policy, KYC records, financial documents, and exception criteria.
Answer questions from internal policies, procedures, manuals, training content, or customer-facing knowledge bases.
Connect documents, notes, prior records, external signals, and case evidence into a structured investigation summary.
Orion sits after classification and extraction. It uses the organized content from Chicago and the structured facts from Nashville to generate grounded insight that can support users or drive downstream action.
Classifies and separates mixed document streams.
Extracts fields, tables, entities, handwriting, clauses, and relationships.
Generates grounded answers, summaries, recommendations, and decision support.
Routes, escalates, validates, and triggers workflow actions.
Chicago identifies the document. Nashville extracts the facts. Orion explains what the evidence means. Polaris moves the work forward.
Orion retrieves relevant enterprise content and uses it to generate grounded answers, summaries, recommendations, and decision-support outputs.
No. Orion is a retrieval-first reasoning layer. It is designed to answer from enterprise evidence, not generic model memory alone.
It retrieves relevant source content before generating a response, then constrains the output to that evidence.
Documents, extracted fields, policies, contracts, claims, case files, enterprise repositories, knowledge stores, and operational records.
Yes. Orion is designed to provide source-backed outputs so users can verify the evidence behind the answer.
Graph RAG connects related facts across documents and systems, which helps with complex questions where the answer depends on relationships between multiple pieces of evidence.
Orion provides the grounded insight Polaris can use to trigger actions, escalate exceptions, route work, or coordinate multi-step workflows.
Orion turns enterprise content into grounded answers, summaries, recommendations, and decision support — with evidence users can verify.
We enable highly regulated organizations to build, govern, and operate domain-specific models within their own infrastructure and governance frameworks.
Orion retrieves the right enterprise evidence before generating an answer — so teams can summarize cases, review documents, compare policies, identify risks, and make decisions with sources they can verify.
Orion is the reasoning layer of OmniSuite™. It turns classified documents and extracted data into answers, summaries, recommendations, and decision support. Instead of generating from model memory alone, Orion retrieves relevant enterprise content first. Then it reasons over that evidence and produces an output users can inspect, verify, and act on. That insight can support reviewers directly, feed host systems, or become the input for Polaris workflow automation.
Orion is the reasoning layer. It uses the organized content from Chicago and the structured facts from Nashville to produce grounded insight that supports human reviewers or drives Polaris workflows.
In high-stakes enterprise work, an answer is not enough. A claims reviewer, compliance analyst, benefits worker, loan officer, or healthcare administrator needs to know where the answer came from. Which document supports it? Which clause applies? Which record contradicts it? Which policy changed? Which evidence is missing?
Generic AI can sound confident without being grounded. That creates risk in workflows where decisions must be explained.
The evidence may live in claim forms, notes, policies, contracts, case files, medical records, or operational systems. Finding it manually takes time.
A fluent response is not the same as a verified one. In regulated work, every conclusion needs a source.
Summarizing documents is helpful, but teams also need to compare facts, find inconsistencies, apply rules, and understand what should happen next.
Orion is built around a simple idea: do not let the model guess when the enterprise already has the evidence. When a user asks a question, Orion retrieves the most relevant documents, records, extracted fields, or repository content first. Then it generates an answer inside that evidence boundary. The result is not just a response. It is grounded decision support: an answer, the supporting sources, and the context needed to trust it.
Ask questions against policies, claims, records, contracts, case files, medical documents, or enterprise repositories.
Turn long packets, document sets, and repository results into concise summaries users can review quickly.
Identify inconsistencies, missing information, policy conflicts, eligibility gaps, or risk signals across multiple sources.
Generate suggested actions based on the retrieved evidence, business rules, and workflow context.
Orion retrieves first, reasons second, and generates only after the relevant evidence has been assembled.
A user, host application, or workflow asks Orion for an answer, summary, recommendation, or decision-support output.
Orion searches across documents, extracted fields, repositories, operational records, and curated knowledge stores to assemble the right context.
The model reasons over retrieved content instead of relying on unconstrained model memory.
Orion generates answers, summaries, recommendations, and explanations with source references and supporting context.
Outputs can support human reviewers, update host systems, or trigger Polaris workflows.
Not every enterprise question is answered by one document. Orion supports different retrieval patterns depending on the type of content, complexity, and workflow.
Best for questions where the answer lives in a known set of policies, records, forms, claims, contracts, or operational documents.
Best for questions that require relationship-aware reasoning across multiple sources, entities, events, policies, or records.
Best for documents where important evidence lives outside plain text, including tables, diagrams, forms, images, and visual structures.
Real-time reasoning over inbound content.
Designed for transaction-heavy workflows where teams need answers from documents moving through active processes.
Deeper analysis across connected evidence.
Adds Graph RAG and more advanced contextual analysis for complex, multi-source questions.
Reasoning across enterprise repositories.
Extends insight across larger knowledge stores, shared repositories, and organization-wide content collections.
A chatbot answers from what the model already knows or what the prompt provides. Orion starts by finding the right enterprise evidence. That changes the role of generative AI. The output becomes easier to verify, easier to audit, and safer to use in regulated workflows.
Orion does not ask users to trust the model. It shows them the evidence.
Teams read full packets by hand, hunting for the clauses and records that matter to each case.
Generative answers may be fluent but can’t be tied back to a source — risky in regulated work.
Without a retrieval boundary, models invent details that look right but aren’t supported by your content.
Auditors and regulators can’t trace which document drove an outcome — the trail starts and ends in conversation.
Policies, procedures, and historical records remain inert documents instead of an answerable knowledge base.
Insight from one team has to be re-keyed before it can become an action in the next system.
Teams can ask questions across large document sets instead of manually reading every page.
Users get summaries, recommendations, and explanations grounded in the evidence they already trust.
Responses are constrained by retrieved enterprise content, reducing unsupported answers.
Users can trace outputs back to the source documents, fields, clauses, and records behind them.
Policies, procedures, records, and historical documents become searchable and actionable.
Once insight is grounded, Polaris can use it to trigger governed actions, escalations, and workflow steps.
Ask whether a claim qualifies, which policy applies, what evidence is missing, or whether fraud indicators exist.
Identify which internal policies, procedures, or controls are affected by a new regulation or audit finding.
Evaluate medical documentation, treatment records, payer rules, and plan criteria with source-backed reasoning.
Review loan files, underwriting policy, KYC records, financial documents, and exception criteria.
Answer questions from internal policies, procedures, manuals, training content, or customer-facing knowledge bases.
Connect documents, notes, prior records, external signals, and case evidence into a structured investigation summary.
Orion sits after classification and extraction. It uses the organized content from Chicago and the structured facts from Nashville to generate grounded insight that can support users or drive downstream action.
Classifies and separates mixed document streams.
Extracts fields, tables, entities, handwriting, clauses, and relationships.
Generates grounded answers, summaries, recommendations, and decision support.
Routes, escalates, validates, and triggers workflow actions.
Chicago identifies the document. Nashville extracts the facts. Orion explains what the evidence means. Polaris moves the work forward.
Orion retrieves relevant enterprise content and uses it to generate grounded answers, summaries, recommendations, and decision-support outputs.
No. Orion is a retrieval-first reasoning layer. It is designed to answer from enterprise evidence, not generic model memory alone.
It retrieves relevant source content before generating a response, then constrains the output to that evidence.
Documents, extracted fields, policies, contracts, claims, case files, enterprise repositories, knowledge stores, and operational records.
Yes. Orion is designed to provide source-backed outputs so users can verify the evidence behind the answer.
Graph RAG connects related facts across documents and systems, which helps with complex questions where the answer depends on relationships between multiple pieces of evidence.
Orion provides the grounded insight Polaris can use to trigger actions, escalate exceptions, route work, or coordinate multi-step workflows.
Orion turns enterprise content into grounded answers, summaries, recommendations, and decision support — with evidence users can verify.
We enable highly regulated organizations to build, govern, and operate domain-specific models within their own infrastructure and governance frameworks.