asktiberius
anything.
Every conversation. Every objection. Every SOP, pricing sheet, tone-of-voice example your team ever wrote — welded into one editable knowledge graph, exposed as one API, grounded in every reply.
One API call. Five stages. Sub-second.
Tiberius isn't a wrapper around a chat model. It's a deterministic retrieval pipeline that feeds the generator exactly the context a human op would pull — and scores the result before anyone sees it.
Prospect pings you on WhatsApp, Telegram, Intercom, or your own widget. Forward the payload to /reply.
pgvector HNSW + Postgres FTS fused via Reciprocal Rank Fusion, plus entity-triggered + metadata-filtered lookups.
Facts, SOPs, ToV examples, similar past convos, state, history — each slot populated from retrieval, citeable by tag.
The generator drafts a reply grounded in the slots, with inline citations the sales op can double-check in one click.
Multi-signal confidence: retrieval coverage, intent classifier, LLM-judged groundedness, self-consistency. Below threshold → flag.
Your ops. One graph.
Every chunk editable, every edge earned.
Product docs, SOPs, glossaries, chat transcripts, tone-of-voice samples — chunked by content-type-aware splitters, enriched with stage / intent / entities, embedded, linked. Similarity edges come from cosine neighbours; co-retrieval edges earn themselves when two chunks keep getting pulled together for real replies.
pgvector HNSW + Postgres FTS, fused via RRF, then an LLM listwise reranker. Metadata filters + entity-triggered lookups stack on top.
Click a node, fix the sentence, save. The chunk re-embeds, the graph updates. No CI, no redeploys, no prompt engineering.
Co-retrieval edges grow from real reply logs. Over time the graph shows which chunks actually answer questions — not just which ones we indexed.
API-first.
MCP-first.
Human-last.
Most AI tools are UIs dragging an API behind them. asktiberius was API-first on day zero — the exact endpoints the web app uses are your endpoints too: /api/v1/*, Bearer-auth, OpenAPI spec, Swagger UI.
And because MCP is just the next layer over HTTP APIs, every asktiberius agent will ship as an MCP server — drop it into Claude, ChatGPT, Cursor, or your own agent stack and your knowledge graph becomes a first-class tool.
curl -X POST "$TIB_BASE/api/v1/agents/$TIB_AGENT/reply" \
-H "Authorization: Bearer $TIB_KEY" \
-H "Content-Type: application/json" \
-d '{
"trigger_message": "Hey, what does Ivy charge for USDC pay-ins? We do ~$50M/mo EU.",
"history": [
{"role": "assistant", "content": "Hi — Felix from Ivy, saw your USDC volume is growing."}
]
}'
# → {
# "reply_text": "Thanks — at that volume, pricing would be custom. …",
# "confidence": 0.87,
# "confidence_breakdown": { "retrieval": 0.99, "groundedness": 0.80 },
# "detected_intent": "pricing",
# "suggested_tool": "send_calendly_link"
# }One agent per job.
Infinite reach.
Each agent gets its own knowledge graph, its own config, its own API keys. Supabase RLS keeps tenants clean; API keys are agent-scoped so a support bot can't answer a legal question by accident. Spin up ten — or ten thousand — from the same codebase.
Wherever humans
reply to humans.
Sales was the wedge. The pattern is universal: any chat-based job where an op needs the right context in three seconds, cites a source, and moves on.
Outbound reps replying on WhatsApp / Telegram / LinkedIn need the right pricing, ToV, and SOP in three seconds.
Tier-1 agents covering thousands of tickets a week can't memorize every product page — but the graph can.
CSMs draft check-ins that pull the partner's usage, last QBR, and open tickets without switching tabs.
Employees ping HR / IT / Finance over Slack. One graph per function, answers that cite the actual policy.
Sourcers mirror the candidate's tone, pull the most recent JD, and keep the story consistent across 40 replies/day.
Dispatch + technicians answering from the cab. Chunks land on mobile as clean, grounded text — no hallucinated part numbers.
Get a key.
Post a message.
Get a reply.
No SDK. No SaaS onboarding. A bearer token and an HTTP client is enough.
curl -X POST https://asktiberius.de/api/v1/agents/$ID/reply \
-H "Authorization: Bearer $KEY" \
-H "Content-Type: application/json" \
-d '{ "trigger_message": "What are you charging per USDC txn?" }'