TS Imagine has connected Gentek.ai’s Model Context Protocol service to TSIQ, giving risk and compliance teams a natural-language route into regulatory intelligence alongside their trading, risk and reference data. The integration is designed to help firms determine what a report requires, locate the information available inside their systems and identify missing fields before a submission reaches a regulator.The product addresses a recurring regulatory-reporting problem. Rules are interpreted by legal and compliance teams, transaction data sits in trading systems, reference attributes may come from separate vendors, and the final report is assembled through another workflow. A filing can fail even when each component works because an identifier is absent, a rule is mapped incorrectly or the source of a reported value cannot be reconstructed. From Regulatory Search to Pre-Submission Control A general AI assistant can summarize a regulation. TSIQ and Gentek.ai are aiming at a more operational task: comparing an obligation with the data and business context available to a particular firm. A user could ask which fields apply to a transaction, whether the necessary attributes exist and where a gap must be resolved. That moves the system closer to a control in the reporting process rather than a research interface sitting beside it. The difference is material. Regulatory reporting is rarely blocked by an inability to find a rule. It is blocked by the distance between legal language, data models and operational ownership. A field may be mandatory only for a certain instrument, venue or counterparty. The value may exist under a different label in a source system. A report may be technically complete while still using an invalid classification. Connecting regulatory logic to a financial ontology can reduce that translation work. Gentek.ai’s MCP provides the specialist regulatory layer, while TSIQ supplies the data, workflows and business context. MCP offers a standardized way for an AI system to request information and tools from an external service. In this use case, the important feature is not the protocol itself but the control boundary around it: what the model is permitted to query, what evidence comes back and whether each step can be recorded. Auditability Is the Real Product Requirement Compliance teams cannot rely on an answer merely because it sounds plausible. They need to know which rule, version and jurisdiction informed it, which internal fields were examined and how the system reached its conclusion. A useful regulatory agent must therefore return evidence and provenance, preserve an audit trail and make uncertainty visible. It must also distinguish a missing value from a value that is present but invalid. TS Imagine says the intelligence is delivered while maintaining governance and auditability. That claim will be tested by implementation details. Firms will want version controls for regulatory content, access controls for sensitive trading data, logs of prompts and responses, and a way to reproduce an earlier assessment after rules or source records change. They will also need escalation paths for questions the system cannot resolve. Human oversight remains central because the cost of a false negative can be high. If the system says a report is complete when a required field is missing, the firm may submit defective data. False positives also carry a cost by sending staff to investigate gaps that do not exist. Accuracy should therefore be measured at the field and rule level, with testing across products and edge cases rather than through broad claims about answer quality. TSIQ’s $100 Million Foundation TSIQ followed five years and $100 million of investment in data, infrastructure and financial context. TS Imagine built a proprietary ontology that connects financial concepts, products, relationships and business logic. The company positions that semantic layer as the basis for grounded answers and controlled actions across capital-markets workflows. That investment matters because language models do not naturally understand how one firm’s instrument master, account hierarchy and reporting schema fit together. An ontology can establish that two differently named fields represent the same concept, link a trade to the correct product definition and identify the rule set that applies. The system still depends on the quality and timeliness of the underlying mapping. The Gentek.ai connection also builds on an earlier development partnership between the companies. They initially identified trading, execution, risk, financing, prime services and wealth management as areas for agentic workflows. Regulatory reporting is a practical entry point because the task has repeatable rules, measurable outputs and significant manual cost, although regulatory interpretation leaves less room for unsupervised action than many administrative processes. Where the Efficiency Claim Can Be Measured Andrew Morgan, President and Chief Revenue Officer at TS Imagine, said the integration gives firms specialist expertise within the context of their own data, processes and controls. Pierre Khemdoudi, Chief Executive Officer of Gentek.ai, said users can interrogate reporting requirements alongside relevant trading, risk and reference data. The clearest performance measures would include the number of data gaps found before submission, time spent investigating exceptions, reduction in rejected reports and the share of answers supported by traceable sources. Firms should also measure how often users override the agent and whether those overrides reveal outdated rules, weak mappings or model errors. The integration does not eliminate the need for regulatory experts. It could change where they spend their time. Instead of manually gathering rules and checking fields across several systems, specialists could review exceptions, validate interpretations and address gaps with data owners. That is a more defensible use of AI than delegating the final compliance judgment to a language model. TS Imagine is presenting TSIQ as a platform that can move beyond retrieval into real workflows. The Gentek.ai MCP is an early test of that claim. Success will depend less on fluent answers than on whether a regulated institution can show an auditor exactly what the system checked, what it found and why a human accepted the result. Deployment will probably begin with narrow reports and controlled user groups rather than every jurisdiction at once. Firms can compare the agent against completed filings, build an error set and define when a question must be escalated. That staged approach creates evidence for model governance and gives data owners time to correct source problems. It also prevents a promising interface from becoming an untested dependency inside a mandatory reporting process.
