caspar marketing agent

Revolutionize hotel marketing with agents that analyze property, craft targeted Google Ads campaigns, and optimize for 400%+ ROI. Boost occupancy & revenue—tailored strategies for owners worldwide

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Inspiration

  • We wanted an agent that could think like a full-stack marketer: research markets, generate creative, and optimize campaigns—without babysitting.
  • We were inspired by the friction marketers face juggling tools, approvals, and learning curves. We aimed for a system that moves from “insight → action → measurement” in one loop.
  • Technically, we were also motivated by the challenge of making LLM workflows reliable, auditable, and fast enough for real use.

What it does

  • Conducts market and competitor research, distills insights, and proposes strategy.
  • Generates channel-ready copy (ads, landing ideas, email snippets) with brand-safe controls.
  • Plans and optimizes campaigns, closing the loop with performance data.
  • Supports a supervisory agent that routes work to specialized agents (researcher, ad generator, optimizer) and requests user approval when needed.

How we built it

  • Multi-agent architecture: supervisor orchestrates researcher, ad_generator, and optimizer tasks.
  • Deterministic prompts and validators for safe, structured outputs (JSON schemas, content filters).
  • Integration surfaces for Google Ads and experimentation frameworks; logs and metrics feed a feedback loop.
  • Frontend demo for quick review/approval; CLI + scripts for batch runs and testing.
  • Persistence for memory/context; rate limiting, secrets management, and health checks to keep it robust.

A simplified control loop:

  • Research → Synthesize insights → Propose strategies
  • Generate creatives → Validate → Seek approval (if required)
  • Launch/Simulate → Measure → Optimize → Repeat

A small math note we used for prioritization:

  • We model expected uplift with a simple ROI: ( \mathrm{ROI} = \frac{\mathrm{Revenue} - \mathrm{Cost}}{\mathrm{Cost}} )
  • For exploration, a UCB-style score for a variant (i): ( \mathrm{UCB}_i = \hat{\mu}_i + c \sqrt{\frac{\ln t}{n_i}} ), balancing exploitation and exploration.

Challenges we ran into

  • Balancing creativity with brand and regulatory constraints.
  • Making LLM outputs structured and verifiable under tight latency budgets.
  • Avoiding prompt drift across multi-step workflows.
  • Handling partial integrations gracefully (sandbox vs. real APIs).
  • Ensuring observability: tracing across agents, runs, and approvals.

Accomplishments that we're proud of

  • A clean, auditable workflow that turns research into measurable experiments.
  • Robust validation layers that reduce hallucinations and enforce schemas.
  • Modular agents that can be extended or swapped without breaking the pipeline.
  • End-to-end demo with approvals, logs, tests, and example outputs for real scenarios.

What we learned

  • Guardrails must be layered: schema validation, policy filters, and deterministic prompting together.
  • Small evaluation harnesses (unit tests + golden outputs) catch regressions early.
  • Observability is a feature—good logs and metrics make tuning fast.
  • Human-in-the-loop points dramatically increase trust and adoption.

What’s next for caspar marketing agent

  • Deeper real-time integrations (ads APIs, analytics, CRM) with safer fallbacks.
  • Richer optimization (multi-objective, budget allocation, pacing controls).
  • Better memory and persona conditioning across campaigns and channels.
  • Team workflows: collaborative review, versioned strategies, and experiment registries.
  • Automatic brief extraction from existing assets to reduce onboarding friction.

Built With

  • beautifulsoup4
  • crewai
  • cryptography
  • flask
  • gunicorn
  • huggingface
  • langchain
  • lxml
  • ollama
  • pathlib
  • postgresql
  • python
  • sentencetransformers
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