Problem

Senso addresses the problem that AI agents often operate without reliable, current enterprise context. As agents increasingly shape discovery, decisions, and transactions, an inaccurate answer about a company’s products, policies, eligibility rules, or procedures can create compliance exposure, customer frustration, missed revenue, and loss of trust. The pain is especially acute in regulated financial services, where rates, fees, underwriting criteria, and disclosures change frequently. Historically, Senso identified substantial operational waste in credit unions: support staff could spend five to seven minutes locating information, while marketing teams faced two-to-four-week delays in accessing behavioral data and missed personalization opportunities.

The clearest use case is lending. Banks and lenders must reconcile regulatory or institutional SOPs with their own lending practices, review incomplete documentation, make consistent decisions, and keep borrowers informed. Senso’s SBA-lending deployment targets exactly these bottlenecks by identifying missing documentation, reducing repetitive work, and improving communication. The underlying economics are compelling: faster processing lowers operational cost and borrower friction, while better retention, more consistent decisions, and fewer errors protect loan volume and customer lifetime value.

Product / Service

Senso provides a context and knowledge-orchestration layer for AI agents. It ingests raw enterprise sources such as PDFs, policies, SOPs, rate sheets, knowledge bases, and conversational data; parses and compiles them into a verified knowledge base; and exposes that knowledge to workflow agents so they can return grounded, cited answers. The system is designed to stay synchronized as source materials change, allowing one compiled knowledge base to power multiple internal and external agents rather than forcing each application to maintain its own prompts and document connections.

The delivery model combines a direct enterprise platform with APIs and partner integrations. Senso can publish structured, agent-ready content for external AI discovery, while products such as Agent Fetch and Agent Echo have been integrated into FundMore’s lending-origination system. In an internal-agent deployment for TruStone Financial, Senso reports that 65,000 answers were evaluated at 93% response quality, with same-day context refreshes. The benefit is a more accurate and compliant AI layer that automates repetitive work, accelerates answers and underwriting workflows, and lets staff focus on higher-value decisions.

Market

Senso competes in the emerging market for AI-agent infrastructure, particularly verified enterprise context, knowledge orchestration, generative-engine optimization, and agentic-web enablement. Its initial wedge was financial services—credit unions, banks, mortgage lenders, servicers, and fintechs—where information is complex, regulated, high-intent, and directly tied to lending, retention, and revenue. The closest alternatives are partly overlapping rather than one-for-one equivalents: enterprise agent-orchestration platforms such as Rasa, Microsoft Agent Framework, Sema4.ai, CrewAI, n8n, and Make; and data-governance or context-layer platforms such as Atlan, with Snowflake, Databricks, and Palantir offering platform-native alternatives.

Senso has evidence of commercial activity rather than being merely pre-product: its materials name TruStone Financial, FundMore, Finov8r, CU 2.0/CuCopilot, and earlier relationships with RBC and other lenders. Its YC profile lists it as an active Winter 2024 company with an eight-person team and three open roles, while a 2019 report documented a C$1.5 million seed round and an RBC-linked customer base. The sources also describe an evolution from a 2017 predictive mortgage-retention business into the current agentic-web and verified-context positioning. No reliable revenue or ARR figure is disclosed in the research, so traction is best characterized as early commercial deployments and partnerships, with revenue scale not established.