Depot RTB Assistant

The problem

Depot RTB serves veterinary clinics in Thailand with warehouse management (products, orders, cold chain, SOPs). Clinic staff needed answers fast:

• "What's the loading dose for Metacam on a 15kg dog?"
• "What temperature range for zone B cold storage?"
• "What documents are needed to order controlled medications?"

Traditional lookup: 3-8 minutes per query across multiple SOP PDFs, product spec sheets, and business process docs. Multiply by 30 queries/day per clinic. Multiply by 20 clinics.

The waste added up. And nobody wanted to memorize 200 pages of SOPs.

What we tried first (and rejected)

Before shipping, we tested three alternatives:

• ChatGPT plugin. Fast to demo. Broke in production — no citations, hallucinated Thai product names, no cost tracking.
• Fine-tuned model. $2000+ in training costs, brittle to corpus updates, would break every quarterly SOP change.
• Simple search UI. Returned documents but not answers. Staff still had to read + interpret.

None of these solved the actual problem: synthesized cited answers, provably correct, with cost transparency.

The approach

Retrieval-augmented generation, but built for production from day one:

• Retrieval: Voyage AI embeddings (voyage-3-large) into Postgres/pgvector. Semantic + Thai-language aware.
• Generation: Anthropic Claude Sonnet 4.6 primary, Haiku 4.5 for simple refusals.
• Guardrails: PII redaction (Thai national ID, phone, email, credit card). Prompt injection detection (2-layer: regex + LLM classifier).
• Cost optimization: Prompt caching (85% savings on repeated queries). Query complexity classifier routes to cheaper model when possible.
• Infrastructure: Docker Compose (nginx + php-fpm + Postgres). Streaming SSE responses. Full monitoring with per-query cost tracking.

Everything wired into the existing Laravel app — no separate service, no exotic dependencies.

Results (measured, not estimated)

What broke (and how we caught it)

Transparency matters more than perfection. Bugs we caught during development, before shipping:

• LLM judge bug. Our quality judge received only document titles, not full content. Result: 50% false-negative pass rate. Fix: pass full document text to judge context. Real quality was ~90%, not 50%.
• SDK positional args. Anthropic SDK expected named arguments; we passed positional via spread operator. Trace showed createStream(1024, Array, 'sonnet', NULL, ...) — args scrambled. Fix: explicit named arguments in both complete() and streamComplete().
• Cache API camelCase. Documentation showed cache_control; SDK expected cacheControl. Fix: found via error message + retry.
• Context assembly regression. A refactor left placeholder variables never populated. Sources labeled [doc_1] but content was from doc_5. LLM refused correct answers. Fix: proper variable assignment in loop + end-to-end test after every refactor.

Every bug: caught, documented in notes, fixed transparently. This discipline is the actual product differentiator — not "AI expertise", but measurement rigor.

What Depot Assistant ships with

Depot RTB deployment produced the product. Everything below ships to every future deployment:

• Complete Docker Compose stack (nginx + php-fpm + Postgres)
• 3 safety layers (PII, injection, scope discipline)
• Prompt caching + smart model routing
• Analytics DB + cost dashboard endpoint + CLI stats
• Health check + readiness endpoints
• README + operations runbook + failure playbook + client onboarding checklist
• 30 days of post-launch support

The v0.3 code that runs Depot RTB is the same code that ships to your Laravel app.

What's next

Package A pricing: $2,500 flat install fee. You own the code + infrastructure. We install + configure it in your existing Laravel + Postgres stack.

Delivery timeline: 2-3 weeks from kickoff to production.