A company assistant that actually knows the company
Scenario: a 200-person engineering org replaces "ask on Slack" with an assistant that pulls context from the wiki, the ticket tracker, and a Postgres schema catalog — all through a single FlowDSL flow. Every answer cites sources. Every LLM call is costed to the asking team.
Illustrative scenario — not a customer deployment. Numbers are targets for comparable workloads.
Target outcomes (illustrative)
Median answer time
Resolved without human
Questions the assistant answers end-to-end with citations
Weekly question volume
Illustrative target adoption curve
Cost per answer
The flow
Click any node to inspect it. Traveling dots are live — each colour is one packet path through the pipeline.
The scenario
The wiki exists, the ticket history exists, the schema catalog exists — but "where does the billing code live" still turns into a 4-hop Slack thread that lands on whoever happens to be online.
The flow
One retrieval flow that fans out to three knowledge sources in parallel, merges the top-k results, and feeds an answer LLM that's instructed to cite every claim. Each source node is swappable — adding Confluence is one node, not a project.
Cost attribution
The assistant writes `team_id` into `runInput.meta` on every question. The LLM ledger records it on every call. Finance generates the internal chargeback report from the same collection.
Where the targets come from
The target takes median time from ~18 minutes (Slack ask → colleague replies) to under a minute (the assistant streams an answer with citations). Cost per answer stays low because most of the work is retrieval, not generation, and the ledger shows where a cheaper answer model is good enough.
The stack
- Query flow:
redelay/event-source→redelay/llm-embed→ parallelwiki/tickets/schemalookups → compose →redelay/llm-chat - Retrieval nodes: custom vector-search handler +
mongoops/find+postgres/query - Session + cost:
assistant/chatstiesteam_idtorunInput.meta;go-ai/ledgerrecords usage