Dark Matter’s Ask Aiva Lets Lenders Query Los Data In Plain Language

Dark Matter Technologies has launched Ask Aiva, a conversational AI-powered assistant embedded in its Empower loan origination system (LOS). The technology lets mortgage lenders query their origination environment in plain language and receive instant answers that can be traced to trusted sources, the company announced recently.

Available now to Empower clients and debuting at Dark Matter’s Horizon 2026 user conference, Ask Aiva is designed to surface operational and performance insights from lenders’ own LOS data without requiring custom reports, IT intervention or external business intelligence tools.

“The data lenders need to answer important operational questions has been just out of reach — buried in their own systems,” Sean Dugan, CEO of Dark Matter Technologies, said in a statement. “Ask Aiva changes that by allowing users to ask questions of their origination environment and receive answers they can act on, with the ability to trace those answers back to the source.”

Ask Aiva is built on a retrieval-augmented generation (RAG) architecture. The tool searches connected data sources in real time, retrieves relevant context and generates responses in natural language. Unlike many generic AI chat tools, Ask Aiva is integrated directly into the LOS workflow and is focused on a lender’s own data and configuration.

A key differentiator, according to Dark Matter, is the ability for users to click into each result and see the specific source data elements and logic behind the answer. That audit trail is intended to address common compliance and risk concerns about opaque AI outputs — particularly in a heavily regulated mortgage environment where lenders must be able to show how decisions and metrics were derived.

Dark Matter said Ask Aiva also serves as an embedded support layer for Empower, providing immediate answers to “how do I” questions on system use and configuration without requiring support tickets or long response times from help desks. For lenders, this could reduce training overhead and speed adoption of LOS features across distributed teams.

“The industry has seen a surge of AI tools that operate as bolt-ons, requiring users to leave their core systems and trust outputs without clear visibility into how they’re generated,” said Vikas Rao, chief technology officer at Dark Matter Technologies. “We built Ask Aiva differently. As one of the first AI experiences woven into the fabric of a mortgage LOS and deployed at scale, it gives lenders the ability to trace every answer back to its source, all within the system where they already work.”

Mortgage lenders have been under pressure to leverage their data to manage loan expenses, turn times and capacity planning, but most organizations still rely on static reports or analytics teams to answer basic operational questions. Embedded AI assistants that understand LOS data models and business rules could shorten that feedback loop, especially for line-of-business leaders who need quick insight into pipeline health, loan defects or bottlenecks.

Future releases, Dark Matter said, will expand Ask Aiva’s reach beyond core LOS data. Planned enhancements include support for lender-specific content such as underwriting guidelines, product matrices and internal policies, as well as broader integration across Dark Matter’s loan officer, borrower, broker and seller portals. The company also expects to introduce borrower-facing capabilities and extend Ask Aiva across the rest of its product suite.

For lenders evaluating AI in production environments, the launch underscores a broader shift from experimental pilots to embedded, workflow-level tools that must satisfy regulators’ expectations around explainability and data governance. Tools like Ask Aiva may give operations, risk and compliance leaders more comfort by surfacing not just an answer but the exact fields, rules and documents that inform it.

This article was generated using HousingWire Automation and reviewed by a HousingWire editor before publication. The system helps convert company announcements and industry data into HousingWire-style news coverage.