Case studies

The organization needed an enterprise-grade foundation for multi-tenant AI agents: marketplace and identity on one plane, LLM/RAG/memory on another, tool routing and workflow execution on a third. Planes had to stay decoupled so isolation, releases, and onboarding did not break.

Enterprise customers needed tenant-safe retrieval across unstructured documents: graph-aware search, governed tool access, and hardening for compliance products. A single shared index and ad-hoc prompts were not acceptable.

Agent changes were hard to ship confidently: no consistent trace-backed evaluation, slow production-readiness cycles, and shared Postgres saturation hitting identity and agent services. This was platform-wide pain, not one bad query.

Internal product teams needed browser automation agents that could run reliably at scale: distributed sessions, CDP control, and human-in-the-loop when flows broke. A major revenue line was stuck behind manual workflow gaps.

Enterprises needed a voice-AI-first platform for inbound and outbound customer interactions. Not single prompt chains: collaborative agents that reason through complex queries, plug into CRM and ops systems, and deploy on voice and messaging without rebuilding workflows per channel.

Workflow automation with LLMs breaks when one agent must handle every step. Coordination, tool use, and failure recovery need explicit multi-agent design, not ad-hoc prompt chains. The platform had to reflect incentive alignment and robustness under decentralized execution.

Realty brokers waited up to two days for answers that spanned structured listings and unstructured project docs. SQL-only or vector-only retrieval could not handle mixed queries, which drove churn across a developer's portfolio.