ARIA turns an organization's knowledge into cited, policy-checked, structured answers — and logs the full trace of how every one was made. Multi-tenant. Auditable. Running in production on real infrastructure.
6 real scenarios — including a hard block, a KB gap, and a human-approval hold · ↺ Replay cycles the next query · ask your own in the live app ↗
Most chatbots call a model and hope. ARIA treats every answer like a deploy: gated, validated, logged. These ten steps run on every single query — this is the actual execution order in production.
No managed AI platform, no glue services. The entire system runs as a containerized stack on a single small cloud server — deliberately simple, fully owned, end to end.
Versioned schema migrations · every architectural choice favors one thing: a system one person can fully understand and operate.
Not a roadmap — these are live in the demo right now.
Every org gets its own knowledge base, policies, prompts, branding, and API keys. Bell's data can never leak into Shopify's answers — isolation is enforced at the auth layer, before any data access.
org_id scoping on every table · per-org rate limitsEach response links back to the exact source chunks it drew from. If the KB can't support an answer, ARIA says so instead of inventing one.
require_citations enforceable as a policy ruleA complexity classifier scores every query and routes it — simple lookups go to a model 10–20× cheaper, hard reasoning goes to the strong one. Automatically, per query.
fast ↔ strong tiers · cost logged per runConfigurable rules run before and after the model: block, redact, require citations, or hold for human sign-off. Built for answers that carry real consequences.
pre + post phases · every match logged as a policy eventA background analyzer scores each knowledge base for gaps, contradictions, and unanswerable questions — surfacing coverage problems before users hit them.
nightly scans · per-org gap reportsRegression suites for answer quality, prompt variants tested against each other, and a full audit log of every run, decision, and dollar spent.
eval suites · variant assignment per runDemo tenants run on pre-built knowledge base templates — switch between them live in the app.
Built in deliberate phases — each one shipped before the next began.
RAG pipeline with vector search, reranking, and confidence scoring. The policy engine and ops console landed here — guardrails were never an afterthought.
File and URL ingestion through background workers, then full org-agnostic tenancy: per-org identity, industry prompt templates, isolated KBs, manual scrape triggers. Rebranded Shoppy Bot → ARIA.
Complexity-based cost routing, escalation detection, per-session agent memory, thumbs-up/down feedback loop, and A/B prompt testing.
Audit logs, rate limiting with quota management, analytics dashboard, evals runner, and admin tooling for orgs, quotas, and variants.
Containerized stack on a single cloud server, dual-domain routing, TLS everywhere — aria-bot.com and app.aria-bot.com. Total infra cost: about a lunch per month.
Google/GitHub auth, a Chrome extension that brings ARIA into any tab, and the next round of pipeline intelligence.
The demo is the real system — same pipeline, same database, same guardrails you just read about. Switch orgs, ask questions, watch the citations come back.