Your company already knows the answer. Nobody can find it.
The policy exists. The past proposal exists. The spec that answers this exact question exists, in a folder, in a thread, in someone's head. A generic chatbot doesn't know any of it. We build systems that answer from your documents and show you which paragraph they came from.
• Support Policy 2026 · p.11
Built for teams sitting on institutional knowledge.
Teams answering the same question forever
Support, HR, and ops burn hours re-deriving answers that are written down somewhere already.
Businesses sitting on unsearchable documents
Policies, contracts, specs, and past work that nobody can retrieve without knowing exactly where to look.
Anyone who tried a generic chatbot and stopped
It sounded confident and was wrong, so it got quietly abandoned. The fix is grounding, not a better model.
SUPPORTED LLMS & DATA SOURCES
What we build.
Grounded, citation-backed AI systems with strict permission boundaries and zero hallucination tolerances.
Internal knowledge assistants
Ask in plain language, get an answer drawn from your verified documents with the source shown. If the answer isn't in your documents, it says so instead of inventing one.
- Answers from verified documents
- Source paragraph citations
- Explicit out-of-scope refusals
Customer-facing copilots
Support and sales assistants that work from approved material only, with a defined scope and a clean handoff to a person when they hit its edge.
- Approved-material-only responses
- Defined scope boundaries
- Clean handoff to a person
Document intelligence
Contracts, policies, and specs parsed into something queryable — so "which of our agreements have a 30-day termination clause" becomes a question you can actually ask.
- Contract and policy parsing
- Clause and term querying
- Structured output for reuse
Proposal and RFP support
Drafts assembled from your past winning work and current approved language, staged for a human to finish rather than start from blank.
- Drafts from past winning work
- Approved language enforcement
- Staged for human completion
Role-based access controls
Permissions inherited from the systems you already run. A person sees what their role allows and nothing else, and that boundary is enforced at retrieval, not by asking the model to behave.
- Permissions inherited from your systems
- Enforced at retrieval
- Per-role audit logging
How search changes.
A representative internal knowledge workflow.
| Stage | Before | After |
|---|---|---|
| Finding the answer | Search the drive, ask in Slack, wait | Asked in plain language, answered in seconds |
| Trusting the answer | Depends who replied | Cited to document and section |
| Out-of-scope questions | Confident guess from a generic tool | Explicit "not in your documents" |
| Permissions | Whatever the folder happens to allow | Enforced by role at retrieval |
| Document updates | Old version circulates for months | Re-indexed on your schedule |
How implementation works.
Grounding and retrieval first, interface second, with accuracy tested on your real documents.
1. Audit
Free, 30 minutes. We look at what your documents actually are, where they live, and whether they're in a state a system can use. Some aren't, and that's worth knowing first.
2. Scope
Fixed price, week one. Sources, permissions model, scope boundaries, and what the system must refuse to answer.
3. Build
5 to 8 weeks. Grounding and retrieval first, interface second. Accuracy is tested against a question set you approve.
4. Pilot
Measured. A real team uses it on real questions. We track what it gets right, what it declines, and what it should have declined.
5. Managed run
Monitoring, cost governance, evaluation tracking, and version upgrades under Managed AI →.
Reference build. This is a standard implementation pattern for this problem, not a client project. It describes how CloudArk builds this kind of system and what delivery involves.
Permissioned Policy & Renewal Intelligence
Account managers rebuilt renewal summaries by hand from carrier PDFs, policy documents, and email threads spread across shared drives.
A permissioned knowledge index over historical policies and carrier documentation, producing cited-source renewal summary drafts staged for account manager review.
Figures are modelled to illustrate the shape of the change, not measured client results.
A knowledge system is only as current as its last index.
Policies get revised, contracts get signed, products change. Left alone, the system confidently cites a document you retired in spring. Scheduled re-indexing, accuracy regression checks, and permission audits are covered under Managed AI.
Frequently asked questions.
Ground your business in verified knowledge.
Book a 30-minute discovery session to evaluate your document infrastructure and design a grounded AI architecture.
- Practical business conversation with an engineer, not a sales deck.
- Clear architecture recommendation and permissions design.
- You keep the audit findings whether we work together or not.
Thanks for reaching out.
A CloudArk solutions engineer will review your inquiry and email you within one business day with scheduling availability.