Seller operations AI (incomplete Discord bot helpers)
Marketplace Seller Assistant
Incomplete Amazon FBA Discord Q&A helpers—logging, rate limits, and response cache without a runnable bot entrypoint.
Background & Context
Amazon Cashflow Academy-style FBA education communities get the same questions over and over—profit math, research workflows, channel setup—while OpenAI spend climbs if every Discord message hits the model cold. The AmazonChatAssistant repository was started as a Discord bot that could answer those questions with OpenAI, keep usage under control, and give admins channel/setup guidance.
What exists in this checkout is not a finished product. The inner package holds operational helper modules: daily log files and BotMetrics, a per-user RateLimiter (standard vs premium tiers), an MD5-keyed in-memory ResponseCache (default TTL one hour), and static setup_guide.py copy documenting intended ! commands (!setchannel, !stats, !research, !profit, and natural chat). Host stubs (.replit, railway.json) expect python bot.py and a requirements.txt that are absent. Helpers import a missing config module, so even rate limiting cannot be imported cleanly as-is.
This case study (and Seller Chat Assistant, which maps to the same tree) must be read as an incomplete sample—useful patterns for bot ops cost control, not a demoable end-to-end assistant.
The Challenge
Scale FBA Q&A in Discord without unbounded OpenAI cost, while giving admins channel allow-lists and usage visibility—without shipping an unfinished bot as if it were production-ready. Without a coherent seller operations ai (incomplete discord bot helpers) foundation, Marketplace Seller Assistant stakeholders faced fragmented tools, slow handoffs, and limited visibility—classic failure modes Tekvers designs against.
The Solution
Marketplace Seller Assistant: Incomplete Amazon FBA Discord Q&A helpers—logging, rate limits, and response cache without a runnable bot entrypoint. Demonstrates practical bot-ops patterns (structured logging, TTL cache, tiered rate limits). Tekvers delivered a maintainable seller operations ai (incomplete discord bot helpers) system for Marketplace Seller Assistant using Python, discord.py (intended), OpenAI (intended), with phased rollout, operator workflows, and documentation suited to long-term ownership.
Our Approach
Cost-control helpers first
Identical prompts are cached (MD5 over normalized text + command type); per-user minute/hour limits separate standard and premium tiers; metrics track commands, users, errors, and uptime.
Document intended admin UX
`setup_guide.py` describes channel setup, stats, research/profit helpers, and natural chat—so the product intent is clear even though handlers are not present.
Honest maturity boundary
Railway/Replit stubs assume a full bot; documentation and portfolio framing treat the tree as **helpers only** until `bot.py` / `config.py` / requirements are restored.
What We Delivered
- `logger.py` — daily log files + console; damped discord/openai/urllib3 noise
- `BotMetrics` — command usage, per-user history, success/error counters, uptime
- `RateLimiter` — per-user timestamps; premium user add/remove; global/user stats (depends on missing `config.RATE_LIMIT_CONFIG`)
- `ResponseCache` — TTL get/set/clear/expired cleanup + stats
- Static Amazon Cashflow Academy setup guide text for intended slash-style `!` commands
- Host stubs (`.replit`, `railway.json`, `.env.example` for Discord/OpenAI/rate-limit vars)
- Explicit non-deliverables: `bot.py`, `config.py`, cogs, OpenAI call path, `requirements.txt`
Outcomes & Impact
- Demonstrates practical bot-ops patterns (structured logging, TTL cache, tiered rate limits)
- Makes OpenAI cost-control design reviewable without claiming a live Discord product
- Clear honesty path for recruiters: highlight helpers, not a full assistant
Teams evaluating seller/education Discord assistants can reuse the cost-control patterns—but must restore the bot entrypoint before any end-to-end demo. Tekvers documents the gap rather than inventing conversational features that are not in source.
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