Frequently asked questions

Corvic AI is an AI operating system for complex enterprise data. It helps organizations connect, process, structure, validate, and use data across documents, databases, APIs, images, tables, logs, and other enterprise sources so AI applications can produce accurate, traceable, business-ready outputs.

Most AI tools are powerful, but they struggle when enterprise data is messy, fragmented, multimodal, or spread across many systems. Corvic helps close this gap by preparing enterprise data for AI through native tools, workflows, playbooks, and traceable processing.

The goal is simple: make AI more accurate, more reliable, and more useful on real business data.

Corvic is built for teams that need accurate AI outcomes from complex enterprise data, including:

• Business analysts
• Data analysts
• Operations teams
• Finance teams
• Compliance teams
• Engineering teams
• Product teams
• Data architects
• AI and automation teams
• Enterprise innovation teams

No. Corvic includes chat and agent experiences, but it is not just a chatbot. Corvic can process data, extract structure, run workflows, build knowledge graphs, validate outputs, generate reports, expose APIs, and create reusable playbooks.

Chat is one way to interact with Corvic, but the platform is designed to produce reliable outputs and repeatable workflows.

ChatGPT, Claude, and Copilot are general-purpose AI assistants. Corvic is designed for enterprise data workflows where accuracy, scale, structure, and traceability matter. Corvic helps teams:

• Process large volumes of data
• Work across many sources and formats
• Use native tools like OCR, extraction, SQL, Python, graph building, and validation
• Reduce hallucinations by grounding outputs in verified data
• Create repeatable workflows and playbooks
• Generate structured outputs such as tables, reports, JSON, XML, CSVs, and dashboards

A general AI assistant can help answer questions. Corvic helps prepare and operate on enterprise data so AI can produce trustworthy business outputs.

Basic RAG is useful for search and Q&A over documents. Corvic goes further. Corvic can:

• Extract tables from PDFs
• Extract entities and relationships
• Process images and multimodal content
• Join data across sources
• Run SQL and Python transformations
• Build knowledge graphs
• Validate results
• Generate structured outputs
• Run repeatable workflows and playbooks
• Keep results traceable to source data

If the use case is simple search, basic RAG may be enough. If the use case requires structured, validated, traceable outputs, Corvic is a stronger fit.

It means AI should not rely only on the model’s memory or a few retrieved text chunks. Corvic improves accuracy by giving AI a stronger operating layer around enterprise data:

• Data ingestion
• Data extraction
• Data transformation
• Persistent memory
• Native compute
• Structured outputs
• Validation
• Traceability
• Governance

This helps reduce hallucinations and makes AI more reliable for real enterprise workflows.

Common use cases include:

• Invoice and accounts payable automation
• Regulatory dossier and compliance reporting
• P&ID digitization
• Knowledge graph creation
• Customer support automation
• Customer 360 intelligence
• Root cause analysis
• ESG and compliance reporting
• Financial analysis
• Market and competitive intelligence
• Patent and technical research
• Sales pipeline enrichment
• Data enrichment and labeling

Corvic is a strong fit when:

• Data is large or fragmented
• Data exists in many formats
• Teams need structured outputs
• Accuracy matters
• Traceability matters
• The workflow is repeated often
• Manual SME work is expensive
• The output must be used in downstream systems

Examples include extracting tables from PDFs, building knowledge graphs, validating invoices, generating compliance reports, or creating dashboards from mixed data sources.

Corvic may be more than needed if the customer only wants:

• A simple chatbot
• A one-time document summary
• Basic search over a few files
• Simple OCR
• A small proof-of-concept with no production workflow
• A task that does not require validation or traceability
• A task that does not require structured outputs

Yes. Corvic can help automate invoice workflows by extracting invoice data, classifying line items, validating charges, applying business logic, and preparing data for downstream accounting or ERP systems. Typical tasks include:

• Header extraction
• Line-item extraction
• Vendor normalization
• Cost coding
• Contract or work order validation
• Exception routing
• ERP-ready outputs

Yes. Corvic can help teams extract structured information from large document sets, cross-reference values, apply rules, preserve traceability, and generate structured reports or tables.

This is useful for regulatory dossiers, audit preparation, ESG reporting, compliance reviews, and evidence gathering.

Yes. Corvic can process engineering diagrams and related documents to extract equipment, tags, instruments, connections, and relationships.

Outputs can support searchable archives, knowledge graphs, parts lists, JSON/XML exports, or downstream engineering workflows.

Yes. Corvic can extract entities and relationships from complex enterprise data and build knowledge graphs that represent assets, customers, products, documents, processes, systems, or other business entities.

Knowledge graphs are useful when teams need to understand relationships across many sources rather than search isolated documents.

Yes. Corvic can combine customer tickets, product manuals, FAQs, logs, CRM data, and other support knowledge to help generate accurate responses, triage issues, and support human agents.

Depending on the use case, Corvic can support agent assist, internal support copilots, or higher levels of automation.

Corvic can work with structured and unstructured enterprise data, including:

• PDFs
• Documents
• Spreadsheets
• CSV and tabular data
• Databases
• APIs
• Images
• Logs
• Web data
• Cloud storage
• Knowledge graphs
• Business applications

Yes. Corvic is designed to connect to existing enterprise data sources such as warehouses, object stores, databases, SaaS applications, APIs, and cloud storage.

The goal is to work with data where it already lives and avoid unnecessary migration.

Not always. Corvic can connect to external data sources and process data through workflows. Some workflows may ingest, cache, or persist derived assets such as tables, embeddings, graphs, or artifacts.

Depending on the workflow, Corvic can create:

• Clean tables
• Structured datasets
• Knowledge graphs
• Embeddings
• Reports
• Dashboards
• JSON/XML outputs
• CSV/Excel exports
• API-accessible outputs
• Traceable artifacts
• Playbooks
• Agents grounded in your data

Yes. Corvic can connect to external services through connectors and API integrations. It can also allow agents and workflows to call approved external APIs.

This is useful when teams need to pull live data, enrich records, push outputs, or integrate AI workflows into existing systems.

A workflow is a repeatable data process. A workflow can ingest data, extract content, transform tables, run AI augmentation, join data, build graphs, generate embeddings, validate outputs, and create downstream artifacts.

Workflows are useful when the same process needs to run repeatedly or at scale.

A playbook is a reusable, plain-English process for recurring work. For example, a playbook could:

• Pull new data
• Run analysis
• Generate a report
• Check exceptions
• Send results to a user or system

Playbooks make repeatable AI workflows easier for business users and analysts to run without rebuilding the process each time.

An agent is an AI assistant configured with access to specific data, tools, skills, workflows, and instructions. Agents can answer questions, run tools, generate outputs, use workflows, and help users interact with complex data.

Corvic agents are designed to be grounded in enterprise data and traceable to sources.

Agent Mode allows users to describe what they need in plain language and let Corvic help orchestrate the data work.

Instead of manually building every step, users can ask Corvic to help transform, process, analyze, and structure data.

Corvic supports native tools for enterprise data processing, including:

• OCR / document digitization
• Multimodal knowledge extraction
• Table extraction
• Image extraction
• AI augmentation
• Web augmentation
• Python transformations
• SQL transformations
• Joins
• Embeddings
• Knowledge graph building
• Graph AI and machine learning
• API calls
• Workflow automation
• Agentic development and interaction

Native tools reduce the need to send everything to the AI model. Instead of making the model do all the work through prompts, Corvic can use the right tool for the task: SQL for queries, Python for transformations, OCR for documents, graph building for relationships, and AI only where it adds value.

This improves accuracy, reduces token usage, and makes workflows more scalable.

Yes. Corvic can run AI-assisted Python and SQL transformations as part of data processing workflows.

This is useful for cleaning data, joining tables, computing metrics, reshaping outputs, and applying business logic.

Yes. Corvic can generate outputs such as tables, charts, reports, dashboards, and other artifacts including PDF, PPT, HTML, and more from agent and workflow results.

Corvic improves accuracy by combining AI with structured processing and validation. Instead of relying only on LLM-generated answers, Corvic can:

• Process the source data first
• Extract structured information
• Use native compute tools
• Apply deterministic logic
• Validate results
• Preserve source references
• Use review loops where needed

This makes outputs more reliable for enterprise workflows.

Corvic is designed to reduce hallucinations by grounding outputs in verified data, using citations, traceability, workflow logic, and validation. No AI system should be described as magically perfect in every situation.

Corvic helps reduce hallucinations and improve trust by grounding AI outputs in source data and making the reasoning path traceable.

Traceability means users can inspect where an answer, table, number, or claim came from. Depending on the workflow, this may include:

• Source document
• Page or section
• Table row
• Data source
• Transformation step
• Tool used
• Reasoning or validation chain

This is important for compliance, finance, engineering, and other high-stakes workflows.

Yes. Corvic can support human-in-the-loop workflows where confident results are processed automatically and exceptions are routed for review.

This is useful in invoice automation, compliance workflows, customer support, and regulated data processing.

Yes. Corvic can validate outputs using business rules, source references, deterministic checks, reconciliation logic, and workflow-specific review steps. Examples include:

• Checking invoice totals
• Comparing values across documents
• Matching data to a database
• Detecting missing fields
• Flagging inconsistent results
• Verifying extracted table values

Yes. Corvic can integrate with existing data sources, APIs, SaaS tools, databases, warehouses, and downstream systems.

It is designed to fit into existing enterprise stacks rather than require a full rip-and-replace.

Yes, Corvic can complement other AI tools. Corvic can prepare, structure, and expose enterprise data so other AI tools can use more reliable context.

Corvic also supports MCP-based integration patterns, allowing external AI tools and agent platforms to interact with Corvic agents and data outputs where supported.

MCP stands for Model Context Protocol. It is a way for AI tools to connect to external data, tools, and systems through a standard interface.

In Corvic, MCP can help external agents or applications interact with Corvic agents, workflows, and data outputs.

Yes. Corvic can expose outputs and support integration through secure MCP APIs, which can be used to interact with agents and data rooms, build workflows and playbooks, and trigger them for scheduled runs.

Corvic can generate structured outputs that are designed to be consumed by downstream systems. Depending on the integration, outputs may be pushed through APIs, downloaded, or connected through supported workflows.

Yes. Corvic is designed for enterprise security, including encryption, access control, tenant isolation, secure integrations, and governance capabilities.

For detailed security reviews, customers should consult Corvic’s security and trust materials or speak with the Corvic team.

Yes. Corvic supports enterprise access controls such as role-based access, SSO, and MFA.

Yes. Corvic’s public security materials describe encryption at rest and in transit. Refer to corvic.ai/security for more information.

Corvic’s public security page references SOC 2 Type II compliance. Refer to trust.corvic.ai for more information.

Yes. Corvic supports secure model usage configurations, including zero-data-retention options where supported by the underlying model provider and customer agreement. Customer data can be removed from Corvic on demand or after tenant termination.

Enterprise deployments may support advanced deployment models, including private deployment. Reach out to contact@corvic.ai for more information.

Corvic offers Developer, Premium, and Enterprise tiers. Please refer to corvic.ai/pricing or reach out to contact@corvic.ai for more information.

Yes, Corvic AI offers a free trial. Visit app.corvic.ai and sign up as a new user to get started.

The public pricing page describes Developer as a monthly plan for a solo developer seat with included AI tokens and web search queries.

The public pricing page describes Premium as a monthly plan for teams with multiple developer seats, higher included AI tokens, and more web search queries.

Enterprise is for organizations that need advanced requirements such as security reviews, SSO, compliance, private deployment, custom support, and custom usage limits.

A typical starting path is:

• Create an account
• Create or select a data room
• Connect to your data
• Start interacting with your agent(s)
• Create tables, graphs, embeddings, or outputs
• Review traceable results
• Build playbooks and workflows
• Share, schedule, or integrate outputs

Not always. Business users can use agents, playbooks, templates, and chat-driven workflows.

Technical users can go deeper with workflow configuration, Python, SQL, APIs, machine learning, connectors, and custom tools.

Yes. Corvic supports data room templates, and prebuilt skills and playbooks for common business use cases.

For more detailed information, please visit docs.corvic.ai/features/room-templates#using-a-template.

Yes. Corvic can help users build workflows through visual workflow configuration and AI-assisted building experiences.

For more advanced or production use cases, the Corvic team can also help scope the data, workflow, outputs, and validation requirements. Please reach out to contact@corvic.ai for more information.

Common reasons include:

• Not all data sources were connected
• Data was not processed yet
• The user prompt was not clear enough
• The agent does not have access to the right source
• The source format needs more advanced processing (OCR or extraction)

A graph may be incomplete if:

• Only a subset of data was processed
• Entity extraction rules are too narrow
• Relationship extraction was not configured fully
• Some source formats were not parsed
• Data sources were not joined correctly

Note: for large data knowledge graph extractions, we recommend using workflows with multi-modal knowledge extraction, python nodes, and AI augmentation to ensure full coverage over important graph entities.

Table extraction may need tuning when:

• The document has irregular formatting and needs enhanced processing
• The table spans multiple pages and the right prompt is needed
• The file needs OCR or multimodal extraction first

Suggested next step: use playbooks and workflows to improve performance via multimodal knowledge extraction, table extraction, and cleanup/validation steps.

Possible reasons include:

• The data is in a different data room
• The source connector is not configured correctly

Possible reasons include:

• The API secret was not configured
• The connection was not allowlisted
• Authentication failed
• The API endpoint changed
• The external service is unavailable

Ways to improve accuracy include:

• Add more complete source data
• Use OCR or multimodal extraction for scanned files
• Create structured tables before asking complex questions
• Use playbooks and workflows for repeatable logic
• Add validation rules
• Use human review for exceptions
• Ensure agents have access to the correct sources
• Review traceability and citations

Review the source references, workflow steps, and data coverage. Common checks:

• Was the right source used?
• Was the source parsed correctly?
• Was the data transformed correctly?
• Was the question ambiguous?
• Did the workflow need a validation step?
• Should the output be generated through a workflow instead of a one-off chat?

If the issue persists, contact Corvic support with the room, workflow, source, and example output.