Agentic AI for Business Process Operations

Cape AI is an Agentic AI platform designed for use in financial services and complex business operations; it is capable of combining unstructured documents with organizational rules to produce verifiable, structured results and workflows. Its flagship product, DirectiveIQ, is used by banks, trust firms, and wealth management organizations to process customer funding instructions.

DirectiveIQ extracts instructions related to transfers, purchases, sales, or account operations from emails, letters, and PDF files; it applies institutional rules and Standing Instructions to generate transactions that can be integrated into existing funding systems. The platform places emphasis on source attribution, data lineage, manual verification, and a complete audit trail.

What is Cape AI?

Cape AI is not a general-purpose chatbot; rather, it is an Agentic Data Intelligence and automation platform designed for use in business processes. It combines large-model reasoning with deterministic rules, domain ontologies, validation tools, and human approval processes, making it suitable for those processes that require high levels of accuracy and traceability.

• Integrate structured and unstructured data sources
• Establish entity and relationship models based on the business domain.
• Use Agents to handle multi-step documents and operational tasks
• Verify AI outputs by combining deterministic rules
• Retain the source and data lineage for each result.
• Hand over items that are abnormal or have low confidence levels to humans.
• Deployed in the customer’s on-premises environment or private cloud

What is DirectiveIQ?

DirectiveIQ is Cape’s solution for managing the flow of customer funds. It operates between the customer communication channels and the trading systems, converting instructions in free format into validated transaction records ready for execution.

• Read customer emails, letters, and PDF attachments
• Identify the account, beneficiary, amount, and transaction type.
• Split a document into multiple fund or securities transaction instructions
• Apply institutional rules and existing Standing Instructions
• Check field integrity, relationships, and business constraints.
• Generate structured transactions that can be loaded by downstream systems.
• To display evidence, confidence levels, and reasons for anomalies to operators.

DirectiveIQ processing workflow

Document understanding and domain ontologies

Cape uses domain ontologies to describe financial entities, transactions, dependencies, and rules, enabling the system to go beyond simply extracting isolated text. It can understand the business relationships between source accounts, target accounts, beneficiaries, transaction relationships, and amounts.

• Identify entities, accounts, and parties involved in the document.
• Maps transaction types such as fund transfers, purchases, and sales.
• Connect information across pages and paragraphs.
• Distinguishing multiple separate transactions within a single request
• Check whether the entity relationships conform to the organization’s definitions.
• Maintain the correspondence between the original text and the structured fields.

Self-correction and dual-layer verification

The engineering documentation made public by Cape mentions iterative self-correction and two-layer verification. The agent first extracts the results, then uses ontologies and business rules to detect any inconsistencies; if necessary, it re-analyses the data, rather than passing the output of the initial model directly on to subsequent stages.

• Check data types, required fields, and format.
• Verify the amount, account, and transaction relationship.
• Identify internal contradictions in the model’s output.
• Use the reasons for failure to guide the next round of corrections.
• When a reliable solution cannot be found, acknowledge the failure and escalate to human intervention.
• Avoid proceeding silently by using data that appears reasonable but is based on incorrect sources.

Source citation and accurate labeling

DirectiveIQ not only returns structured values but also identifies the specific page numbers and text areas in which the fields are located. Operators can thus see the source of these fields within the PDF, which reduces the risk of making decisions based solely on the outputs of the model.

• Shows which page and which section of text the field comes from.
• Highlight the original content of the correct answer in the document.
• Maintain the connection between the extracted results and the evidence.
• Allow the reviewers to quickly check the account, amounts, and beneficiaries.
• Retain traceable evidence for auditing and dispute resolution

Human-robot collaboration

Cape uses Human-in-the-Loop as a control mechanism, rather than as a final remedy. The system can decide whether to proceed automatically, pause operations, or request approval based on the type of transaction, its amount, the level of confidence, and the results of the applicable rules.

• Low-risk and complete commands are routed to the fast processing path.
• Missing or ambiguous information is placed in the queue for manual supplementation.
• High-value or sensitive transactions require dual approval.
• The reviewer can modify the fields and record the reasons.
• Each time, a human decision is made to improve subsequent rules and processes.
• In the end, the authority to utilize funds remains under the control of the institutional systems and authorized personnel.

Capabilities of the Cape Agentic platform

Dynamic Agent Collaboration

Complex tasks can be completed through the collaboration of multiple agents and deterministic components; for example, one agent is responsible for understanding the document, another checks the rules, and a workflow decides whether manual review is needed. Each component should have clear boundaries and observable states.

• Select different Agents and tools based on task type.
• Separate document extraction, verification, and business actions.
• Adjust the next step dynamically based on intermediate results.
• Configure alternative paths for failures, timeouts, and missing data.
• Record the inputs and outputs used by each Agent.
• Prevent unauthorized agents from directly performing high-risk actions.

Continuous Compliance Monitoring

CCM Cape is designed to ensure compliance with regulations in the banking, financial services, and regulated consumer sectors. It continuously monitors risks throughout the process of drafting and approving content, using Guardrails to decide whether to block, route, or approve it, while also retaining auditable evidence.

• Continuously monitor compliance risks related to marketing and customer content.
• Sort the findings according to a unified severity level.
• Establish approval and control mechanisms for AI-generated content.
• Route high-risk content to compliance officers
• Retain test results, modification, and approval history
• Analyze long-term risk themes and trends

Cape CCM does not provide legal advice. The responsibility for regulatory interpretation, final approval, and public disclosure remains with the company and its compliance or legal team.

Applicable scenarios

• The trust department is responsible for handling customer fund transfers and securities orders.
• Wealth management firms convert emails and PDFs into transaction drafts.
• Banks are accelerating the onboarding of customers and the review of their documents.
• The compliance team carries out Enhanced Due Diligence.
• Financial institutions continuously monitor marketing and AI-generated content.
• Structured extraction of loan, supplier, or audit documents
• Companies integrate the capabilities of controlled Agents into Salesforce or case management systems.

Which teams are suitable?

• Banks, trust companies, and wealth management firms
• Companies that need to process a large volume of unstructured operational documents
• Organizations that place emphasis on data residency, permissions, and audit evidence
• An operations team with clear SOPs and manual approval processes
• IT teams that wish to add Agentic processing capabilities to existing systems
• Companies that have the budget to carry out production-level pilots and custom deployments

Customer onboarding and due diligence

The case studies related to Cape illustrate scenarios such as customer onboarding and Enhanced Due Diligence. The platform is capable of collecting documents from various sources, extracting entity and risk information, and providing the relevant evidence and verification results to the reviewers.

• Organize the forms, documents, and correspondence provided by the clients.
• Extract identity, entity, relationship, and business information.
• Perform integrity and institutional rule checks.
• Items with conflicting data and those that require further investigation
• Retain the source of evidence and the review pathway
• Reduce redundant data entry and manual search time

Integration with existing systems

Cape can connect to Salesforce Agentforce, Claude Desktop, or other MCP-compatible clients via interfaces and MCP. The core processing of DirectiveIQ still takes place in Cape Enterprise; external systems can interact with it only through controlled tool interfaces.

MCP integration process

1. Identify the file entries and user roles that need to be analyzed by DirectiveIQ.
2. Deploy a protected MCP bridging service and configure the network boundaries.
3. Verify each client request using a key or enterprise credentials.
4. Map the calling identity to the permission scope in Cape Enterprise.
5. Define the tool input, file acquisition, and structured output format.
6. Validate for incorrect input, malicious files, timeouts, and duplicate requests.
7. Check fields, confidence levels, and business rules before writing downstream.
8. Record calls, evidence, manual modifications, and the final execution status.

DirectiveIQ launch process

1. Collect representative customer instructions and abnormal cases.
2. Define accounts, transactions, beneficiaries, and other entities in various fields.
3. Review the Standing Instructions as well as the verification and approval rules.
4. Define the boundaries for automatic processing, manual review, and rejection.
5. Test the accuracy of extraction, splitting, and verification using historical documents.
6. Run it in parallel with existing human-generated results and analyze the differences.
7. Start with low-risk transactions and gradually expand the scope.
8. Continuously review errors, rule changes, and user feedback.

Result quality assessment

Cape AI price

Cape AI offers Discovery, Strategic, and Catalyst solutions based on the number of use cases, with prices tailored for enterprise-level projects. The page also outlines the secure access options for local or VPC deployments, as well as for public and private models.

The page does not specify the exact duration for which the price applies, the amount of usage, the time required for implementation, or the subsequent subscription fees; therefore, these details must be confirmed in the order. Professional services, integration, cloud resources, model calls, and the scope of expansion can also affect the total cost.

Contract and payment considerations

• The specific costs and professional services are specified in the formal order.
• The default invoice is usually paid within 30 days after it is issued.
• Unless otherwise specified in the order, orders are generally non-cancellable and no refunds will be issued.
• Subscriptions are renewed in accordance with the terms specified in the order; if no terms are specified, they may be automatically renewed for the original duration.
• To terminate conveniently, it is usually necessary to give notice at least 60 days before the current deadline expires.
• The renewal price may be adjusted upon prior written notice.
• Before signing the contract, it is necessary to clarify the plans for Pilot acceptance, data transfer, and withdrawal.

Deployment method

Cape AI can be deployed on the customer’s own infrastructure, in an on-premise environment, or within a VPC, thereby reducing the need to send sensitive data to new multi-tenant services. Companies can connect to Azure OpenAI, OpenAI, or models hosted locally.

Security and access control

• Data remains encrypted during transmission and storage.
• Set fine-grained data permissions by user and role.
• Organizational access can be granted or revoked at any time.
• Compatible with identity providers such as Google and Azure Active Directory.
• Track data, models, and user activities through audit logs
• To which models and services are the monitoring data sent?
• Isolate core Enterprise services within a virtual private network.

AI governance

A production agent needs to manage models, data, tools, and business processes simultaneously. Cape’s exportable outputs and logs are useful for monitoring, but organizations still need to define their own responsibilities, approval procedures, monitoring mechanisms, and incident response strategies.

• Create a list of permitted models and data categories.
• Restrict the files and tools that each Agent can access.
• Set separate permissions for transaction writing, approval, and execution.
• Implement version control for changes to prompts, rules, and ontologies.
• Continuously monitor deviations, errors, and manual overrides.
• Establish pause, rollback, and investigation processes for erroneous executions.

Product advantages

• Special DirectiveIQ is provided for trust and wealth management.
• Convert unstructured instructions into verifiable transaction structures
• Domain ontologies and dual-layer verification enhance business consistency
• Accurate source labeling facilitates quick verification by operators.
• Supports Human-in-the-Loop and full data lineage.
• It can be deployed locally or within the customer’s VPC.
• The MCP approach facilitates integration with existing work environments such as Salesforce.

Usage restrictions and precautions

• The starting price is 100,000 dollars, and it is suitable mainly for companies with sufficient budget for production.
• Deployment and training require domain experts, historical samples, and institutional rules.
• Complex or ambiguous customer instructions may still require manual clarification.
• Structured results cannot be used to carry out financial transactions without authorization.
• MCP and integration with external systems increase the complexity of identity, network, and key management.
• The terms related to the behavior and data of third-party large models may change.
• The results of publicly available cases cannot directly reflect the profits of each individual organization.
• The core platform, the DirectiveIQ engine, and the models are not open source.

GitHub and open source

Cape has published articles on its engineering architecture, but neither the official DirectiveIQ core engine nor the source code repository for Cape Enterprise was made available. Just because MCP uses open standards does not mean that Cape’s business logic, documentation tools, or validation engines are open source.

Basic information

Recommendation score

4.6 / 5. Cape AI is suitable for banks and trust institutions that need to convert complex financial documents into auditable transaction workflows; DirectiveIQ excels in terms of domain validation and evidence tracking, but it comes with higher costs, greater implementation complexity, and higher demands for manual control due to the associated risks.

Frequently Asked Questions

What does Cape AI do mainly?

It uses Agentic AI to handle complex business documents and operational processes, and ensures that the results are traceable through information on their origin, data lineage, verification processes, and human approval.

What is DirectiveIQ?

It reads financial instructions from customers’ emails, letters, and PDF files, and converts them into structured transactions that have been checked against the institution’s rules.

Which companies are suitable for Cape AI?

It is primarily suitable for banks, trusts, wealth management firms, and other organizations that need to handle sensitive documents and comply with regulatory requirements.

How much is Cape AI?

The cost for a single use case with Discovery is $100,000; for Strategic, it is $240,000 for three use cases. A customized quote is required for the Catalyst enterprise solution.

Can Cape AI be deployed locally?

Yes, the official package includes options for on-premise or VPC deployment, and it allows connection to public or local models that are trusted by the enterprise.

Will DirectiveIQ make the transfer directly?

It generates Transfer-ready transactions and loads them into the existing system; ultimately, execution is still controlled by the institution’s trading system, along with its permission and approval processes.

Does Cape AI support source citation?

Supported: it allows the data lineage to be retained, and structured fields can be linked to the original document pages and specific text segments.

Does Cape AI support MCP?

It supports bridging DirectiveIQ via MCP, enabling compatible clients such as Salesforce Agentforce to make calls using controlled tools.

Does Cape CCM provide legal advice?

It is not provided. CCM is used for continuous monitoring, routing, and evidence retention; ultimately, it is the company’s compliance and legal staff who are responsible for making legal decisions.

Is Cape AI open source?

No. No official core source code repository was found this time, and the use of the open MCP protocol does not mean that DirectiveIQ or Cape Enterprise are open-source.