Square Peg Backs Enterprise AI Co-Worker Developer Future Secure AI

Square Peg announced on November 6, 2026, that it is backing Future Secure AI, whose enterprise AI Co-Workers are designed to complete complex business processes alongside employees and inside customers’ existing systems.

Future Secure AI designs, builds and runs these multi-agent systems. Each Co-Worker has a name, a face and a place on the organization chart, while underlying agents handle intake, judgment calls, system updates and hand-offs. Deterministic logic is used where a language model is the wrong tool, with human involvement where appropriate.

The company calls its target area “Red Zone” AI: workflows that cross multiple systems and teams and require accuracy, security and oversight. Examples include month-end close, tax calculations, reconciliations and customer service, which can sit inside decades-old systems of record and behind strict data rules. That makes completion of the whole operating process, rather than faster responses to individual prompts, the central product requirement.

Delivery is built around executive relationships and embedded implementation. Future Secure AI sells to senior executives, CEOs and boards, opening with their largest business problems. Senior former consultants and engineers then work with customer teams to scope, build and operate the Co-Workers. This forward-deployed model ties the software to a team responsible for fitting it into a customer’s actual processes.

Role-Specific Co-Workers

Future Secure AI says its AI Co-Workers execute complex, high-volume workflows instead of only individual tasks, and describes them as LLM-agnostic, resilient and fully auditable. Each is purpose-built for a business role and tailored to an organization’s people, processes and systems. Named examples include Amy AI, a finance month-end analyst for a global investment bank; Ari AI, a fringe benefits tax analyst for a global investment bank; Leo AI, a rostering manager for a medical diagnostic company; and Billie AI, a billing assistant for a global law firm.

Deployment and Governance Controls

Future Secure AI says its technology is not tied to a single large language model, cloud provider or enterprise platform, and is designed to integrate with a customer’s existing environment. Deployments can use single-tenant private-cloud infrastructure and containerized applications inside an enterprise network. Under that configuration, sensitive data remains within the environment and is not used to train public AI models.

The company describes an operating system that coordinates long-running workflows across systems, approvals, decisions and business processes. Its controls include configurable guardrails, monitoring and human oversight, together with operational visibility, explainability and audit trails. Lifecycle management covers deployment, monitoring, support and continuing optimization. Employees can collaborate with Co-Workers through email, voice, Slack and Teams, while connected data access covers structured and unstructured information.

Founders and Square Peg’s Thesis

Future Secure AI’s leadership page identifies Mehrdad Baghai and Michael Hunter as co-founders and co-CEOs. Baghai’s biography describes him as a former McKinsey partner and an adviser to CEOs and boards, while Hunter’s background covers entrepreneurship and investment banking at Macquarie Capital.

Square Peg’s account adds that Baghai co-led McKinsey’s worldwide Growth Practice and co-wrote The Alchemy of Growth, later running growth and commercialisation at CSIRO, Australia’s national science agency. Hunter spent more than a decade as a Macquarie investment banker working closely with senior leadership. Those careers help explain why the investor views executive relationships and credibility as part of the company’s delivery model.

Square Peg’s thesis is that enterprise AI adoption will combine products and services, that suppliers must define business problems and own delivery, and that technology alone will not determine who wins complex, regulated workflows where executive trust and delivery proof points are central. That view connects its backing to the founders’ backgrounds and Future Secure AI’s forward-deployed teams, beyond the capabilities of the underlying models.

For Future Secure AI, Square Peg’s backing is an endorsement of an enterprise-AI model built around execution, integration and governance. The central test is whether its role-specific Co-Workers and senior delivery teams can turn those controls into dependable operation across the Red Zone workflows it targets.