Case Studies & Wins

Real results from AI productivity and developer experience implementations.

Press-inquiry response optimizer

Tech Startup

14× productivity boost

Context

The comms team handled only about 10 journalist inquiries per week. Manual inbox triage and bespoke drafting caused high toil; most leads expired before anyone replied.

Action

  • Built a multi-agent LLM system that reacts to new requests every hour, scores relevance and "draftability," pulls supporting sources, and prepares a draft structure.
  • Editors use a lightweight interface (desktop or phone) to review, add feedback, and trigger the agent to rewrite into final copy; loop repeats until approved.
  • All sensitive fields stay local—nothing exposed to external LLMs. A human-in-the-loop editor remains the final gate.

Outcome

  • Response time dropped from ~7 days to <8 hours.
  • Team now sends 20+ tailored responses per day (≈14× productivity boost).
  • Best single-day record so far: 11 pitches sent.
  • Manual effort on this task cut by roughly 90 percent.

Takeaway

Decoupling a high-volume text pipeline into triage, context gathering, draft, and human review steps—then automating each with cooperating agents—turns a slow, error-prone process into a rapid, scalable one while preserving quality and compliance. The same pattern can lift productivity in any stream of repetitive knowledge work such as RFPs, support tickets, or bug-report triage.

LLMMulti-AgentCommunicationsAutomationHuman-in-Loop

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