Most AI stalls at the demo.
We build the kind that ships.

Problem: Operations team running repetitive multi-step workflows across CRM, helpdesk, and back-office tools, losing 200+ hours a month to manual coordination.

Solution: Designed a LangGraph-based multi-agent system with typed tools, retries, and human-in-the-loop approvals on irreversible actions. Full Langfuse tracing on every run.

Outcome: Automated workflow coverage moved from 0 to 70% of in-scope tasks within a quarter, with a measurable drop in handle time and zero hallucinated actions in production.

Problem: Knowledge sprawl across 100k+ documents (contracts, SOPs, manuals) with brittle keyword search and growing tickets to legal & ops.

Solution: Hybrid retrieval (BM25 + dense + re-ranker), structured extraction, citation-grounded answers, and a regression eval suite on every change.

Outcome: Self-service answer rate measured against a 500-question eval set, with citation grounding above target. Significant reduction in escalations to internal subject-matter experts.

Problem: Recruiters losing hours on first-round screening calls: repetitive questions, repetitive note-taking, missed follow-ups.

Solution: Outbound voice agent for first-round screening with real-time ASR/TTS, structured note extraction, and direct CRM sync. Human handover on edge cases.

Outcome: Recruiter time on first-round screening dropped sharply; structured candidate data flows directly into the ATS with searchable transcripts attached.