Floqer Targets Open-Source Release for Its AI Sales Intelligence Platform

During that initial rollout, the founders noticed that enterprise teams were frequently relying on the extension to tidy up records inside their customer relationship management (CRM) software. Direct feedback revealed that keeping CRM data accurate and up to date was an arduous, highly manual chore lacking reliable automation. That realization spurred a complete pivot toward developing Floqer into an autonomous intelligence system.

Instead of requiring sales representatives to manually scour job boards, LinkedIn, corporate websites, and search engines to track down targets, Floqer deploys AI agents to execute those workflows automatically. Users define the criteria of the accounts they want to reach, and the system aggregates matching prospects.

Waterfall Enrichment and Continuous Tracking

To ensure contact details remain reliable, Floqer connects to more than 100 third-party data providers. When identifying contact numbers or validated email addresses, the platform implements a "waterfall" query process—iterating through data vendors in sequence until it secures verified information.

Beyond basic lookup, the fleet of autonomous agents continually monitors web activity. If an executive earns a promotion, switches employers, or if a prospect raises fresh venture capital, Floqer automatically updates the corresponding profile. According to Floqer, enterprise customers have reported slashing outbound prospecting time by up to 70%.

The decision to release the customer database as an open-source project stems from both operational capacity and enterprise requirements. Mahajan noted that the team faces more inbound demand than it can currently handle directly. Furthermore, several enterprise clients have expressed a strong preference for hosting customer intelligence internally rather than locking it into a third-party proprietary platform.

Floqer raised $2 million in pre-seed funding to support its development. Backers include Perplexity’s F7 Fund, N49P, Tidal Venture Partners, and Denis Yarats, co-founder of Perplexity.

What it means for developers

Floqer’s move toward open-sourcing its customer knowledge architecture in early 2027 will offer engineers a valuable template for orchestrating autonomous web-scraping and data verification workflows. Designing production systems that continuously monitor unstructured online changes while maintaining database integrity remains one of the more challenging engineering problems in the agentic AI landscape. Developers will soon have the chance to inspect how Floqer structures its multi-agent retrieval routines and implements waterfall data fallbacks.

For teams building their own proprietary research agents or automated enrichment services, choosing the right foundation model for reasoning and tool use is critical. Developers exploring architectures for data extraction can access models from providers like Anthropic, OpenAI, Google, and DeepSeek via unified, pay-per-token API access at Apixo.

Additionally, because Floqer's planned release involves enterprise customer data, the startup's ongoing security audits highlight an important priority for software teams: agentic data pipelines must enforce strict privacy boundaries before code is deployed across self-hosted enterprise environments.

Source: Floqer’s AI agents research sales prospects so teams don’t have to — TechCrunch AI. Written by the Apixo team from that report.