🎫 Knowledge & Cited Answers · Solution

Every ticket gets a
cited answer a human sends.

Each incoming ticket is answered from your own manuals and resolved cases — every step carries a source citation, hallucinations are caught by a grounding gate, and the draft reply and next-best-action wait for a human. Runs on your infrastructure, with a full audit trail.

The business case

Your best answers already exist — they just get re-invented on every ticket

The problem

Your best answers already exist — in manuals, runbooks, SOPs, and the tickets your team already resolved — but they live in silos, so every repeat question gets re-answered from scratch. Handle time climbs, answers drift between agents, and knowledge walks out the door with whoever retires.

The moment you point a generic AI chatbot at it, it confidently invents a fix it can't cite. In regulated and safety-critical work that isn't a quality problem, it's a liability — and it is exactly the authority you cannot hand a model: the authority to answer a customer, a patient, or a technician on its own.

Who feels it

  • Support and service-desk leaders carrying handle-time and backlog targets, and field-service managers whose technicians wait on tribal knowledge
  • Knowledge and enablement owners watching answers drift between agents as documentation ages
  • Compliance, quality, and risk officers who must certify that any AI touching a customer, a patient, or a machine is auditable, explainable, and never acting on its own
Time to value

Fast — assembled from flow8 building blocks that already exist and are adversarially hardened. Seed a small batch of manuals and resolved tickets, point it at your inbound queue with the kill-switch on, and it runs shadow-first — the first cited drafts land in the review mailbox the same day, before any reply can reach a customer.

What you get

The knowledge you already own becomes a same-day answer engine

The same pipeline serves every queue you own — one product line or ten.

📚

Your own knowledge becomes an answer engine

It turns the manuals and resolved tickets you already own into same-day cited drafts — no model training, no re-platforming your help desk, no data leaving your walls.

⏱️

Handle time drops on repeat tickets

The agent edits a drafted, cited answer instead of researching from zero — a root-cause plus step-by-step fix, ready to review and send.

🔗

Every step carries a source citation

Each answer step resolves to a specific manual section or prior case, so the reviewer trusts it in seconds and the auditor can reconstruct exactly where it came from later.

🛡️

Hallucinations are caught, not shipped

A grounding gate flags any uncited or off-base step before a human ever sees it — the assistant is honest about what it doesn't know instead of fluent about something it made up.

🙋

Nothing auto-sends to a customer

The draft reply and the next-best-action task wait for a human. No customer is emailed, no ticket auto-submitted, no part auto-ordered — a wrong answer can't reach anyone on its own.

🗺️

A knowledge-gap map, for free

Every ungrounded step and low-confidence answer is recorded, so the topics your corpus is thin on surface as data — you learn where the knowledge base needs filling.

How it works

One governed spine, from inbound ticket to human-approved reply

The model proposes a cited answer; a human sends it; nothing touching a customer or an action ever auto-fires. It is the same secure spine every flow8 Solution runs — here worn as a cited answer engine.

Every incoming ticket runs the identical sequence. The LLM is permanently demoted to an advisor that may only cite chunks it was given; the consequential output is a proposed cited draft on a tamper-evident ledger — not a sent reply.
01
📨
Retrieve over your corpus The ticket is embedded and matched against your indexed manuals and resolved cases — a read-only knowledge base you own. IMAP · OCR
02
🧪
Injection pre-scan A deterministic Code heuristic treats the ticket text — and every retrieved chunk — as data, before any model sees it. data, not instructions
03
🧩
Draft with citations A schema-locked LLM suggests a cited answer; it may only reference chunk ids it was handed, never invent provenance. model suggests
04
⚖️
Grounding gate decides Deterministic Code runs the grounding gate: any step citing outside the retrieved set is marked ungrounded — the verdict is never the model's. Code authoritative
05
📝
Draft-not-act ledger The cited answer and next-best-action are written as proposed rows on the shared actions ledger. draft, not act
06
🚦
Policy gate A deterministic gate caps every output at prepare-only; high-consequence categories are forced to needs-approval regardless of confidence. prepare-only
07
🙋
One human task Exactly one deduped task is opened per ticket; a full evidence record is written before any side-effect. audit-before-effect
👤
Human reviews & sends A person edits and approves in one click. The reply and the action fire under their authorship. human-gated
Safe output A cited draft reply + next-best-action approved by a human · every step traceable to a source · recorded on a signed ledger

Cited Auto-Resolution gives every incoming ticket a head start a human finishes. First it builds a knowledge base you own — ingesting your manuals and historically resolved tickets, embedding them into a vector store, and mirroring per-chunk provenance into a relational table so every citation resolves to a human-readable source. Then, per ticket, it embeds the query, retrieves the most relevant chunks, runs the injection pre-scan before any model sees the text, and a schema-locked LLM drafts a root-cause and step-by-step answer where every step cites a specific source.

Because the LLM may only reference chunk ids it was handed — the grounding gate flags any out-of-set or uncitable step, citations resolve deterministically from the provenance mirror, and the evidence row is written before any draft on a hash-chained, signed ledger — you get agentic value without ever handing a model the authority to answer on its own. Off-the-shelf chatbots let a model speak first and bolt on citations later; flow8 makes the citation the architecture.

Why it's safe to run

Secure and efficient by construction — not by policy

Secure by construction

The guardrail is the architecture, so adding AI to customer-facing answers stops being a risk-underwriting exercise.
  • Deterministic injection pre-scan. A Code heuristic (control / zero-width / bidi chars + imperative-override markers) treats the ticket AND every retrieved chunk as data — because a poisoned manual could have slipped past ingestion. A flagged unit takes zero LLM passes and is quarantined. There is no security module pretended.
  • Never auto-act on a customer or an action. No requester is emailed, no service ticket auto-submitted, no part auto-ordered — the cited reply and next-best-action are written as draft proposed rows and wait for one human approval. It is the program-defining invariant.
  • Grounding gate over audited evidence. Every answer step must cite a chunk from the retrieved set; citations resolve deterministically from the provenance mirror, and any out-of-set or uncitable step is flagged ungrounded — recorded with model, prompt version, retrieved chunk ids and scores before any draft fires.
  • Tamper-evident ledger. Each row can carry a per-actor hash chain plus an HMAC-SHA256 signature under a frozen canonicalization, with a read-only sweep re-verifying the chain to catch any sent-not-prepared escape — an EU-AI-Act-grade trail your auditor can read.
  • Sovereign and provider-swappable. Your corpus and the vector index live entirely inside your own infrastructure; the relational store is the citation system of record and the vector index is a rebuildable derived copy; the embedding and AI provider are swappable settings.

Efficient by construction

The same properties that make it safe make it cheap to run at volume.
  • Idempotent by construction. Ingestion dedupes by source key and content hash; a revised manual deletes-then-re-embeds in place via deterministic point ids, and a diagnosis keyed to ticket-plus-prompt-version makes a same-version re-run a no-op — bump the version to deliberately re-diagnose the whole queue.
  • Draft-not-act removes rework. The agent edits a cited starting point instead of researching from scratch, and because the audit row is written first, a flaky email or task call never loses the work the AI already did.
  • Scoped, cursored intake. The mailbox fetch limit is always hard-capped and the cursor advances only to the durably-processed watermark, so a lost cursor degrades to a paged drain, not a full re-pull — the DB unit-key is the real dedup authority.
  • Deterministic where it counts. Retrieval, grounding, citation resolution, thresholds, and the final verdict are pure Code over objective signals — the model supplies scores and a draft, Code decides, so quality never rides on a hallucinated confidence number.
  • Self-healing knowledge map. Answer coverage, ungrounded-step rate, low-confidence rate, and KB-gap topics recompute every run into a living map of where the knowledge base is thin — instead of freezing a stale number.
Built from

Assembled from proven, hardened capabilities

Not rebuilt from scratch — composed from the same governed building blocks every flow8 Solution shares, so it ships in days.

The capabilities it composes
Corpus ingestion & indexing Document & OCR extraction Injection pre-scan Semantic retrieval & re-ranking Schema-locked cited drafting Deterministic grounding gate Draft-not-act action ledger Tamper-evident audit trail
Connects to your stack
IMAP & Exchange mailboxes ERP & CRM systems of record Enterprise task & workflow queues On-prem vector store & knowledge base Manual, runbook & SOP repositories Reporting & BI dashboards Any REST / OData API
Where it fits

The same process shape serves every answer-driven industry

Any team whose questions arrive against a body of manuals, SOPs, or regulated guidance that every answer must be grounded in before it goes out.

Composes with

A draft from one solution is the clean upstream another consumes

Adopt this one and it plugs into the spine the others already speak.

Point it at your manuals. Kill-switch on. Shadow-first.

Point it at your manuals and your last year of resolved tickets, and watch the first cited drafts land in your review mailbox today — answers your team finishes, never answers your AI sends. When you're ready, flip on the human-task queue and add the next-best-action, a richer corpus pipeline, or the signed governance ledger on the exact same spine.

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