SERVICE · RAG KNOWLEDGE
RAG Knowledge System
Turn enterprise documents and data into answers you can actually trust: 100% sourced citations, permission-aware retrieval, and retrieval quality you can measure with eval benchmarks.
If an answer can't be traced, no one will use it
The real blocker to enterprise RAG isn't that the model can't produce an answer — it's that no one trusts the answer it produces. Contracts, SOPs, project records, and support tickets are scattered across SharePoint, Confluence, Google Drive, ticketing systems, and email attachments, with conflicting versions and tangled permissions. When a system returns an answer but can't say which document, page, and version it came from, legal, audit, and revenue teams fall back to manual lookup — and the rollout stalls at PoC.
Tenten AI deploys engineers on-site (FDE) and ships RAG as a retrieval system you can actually sign off on, not a chatbot demo. Every answer carries sentence-level citations down to the document, page, and chunk — click through to verify against the source. The retrieval layer reuses your existing access controls (AD/SSO/permission groups), so users only ever retrieve what they already have rights to see. AI never becomes a backdoor around your permissions.
Most important, it's measurable. On day one we build a golden eval set together with your domain experts and track every change against retrieval recall, citation accuracy, and answer faithfulness. Data freshness is guaranteed through incremental indexing and source sync — stale content gets flagged or down-ranked — so "is this answer still valid?" becomes a number you monitor, not a complaint you hear after the fact.
Capabilities
01
100% sentence-level citations
Every answer maps to a specific document, page, and passage that users can expand and verify in one click. When no reliable source exists, the system says "no grounding found" instead of fabricating one.
02
Permission-aware retrieval
Reuses your existing AD / SSO / permission groups and enforces access control at both the retrieval and answer layers, so users only ever see documents they already have rights to — AI never bypasses your permissions.
03
Measurable retrieval quality
We build a golden eval set with your domain experts and continuously track retrieval recall, citation accuracy, and answer faithfulness, so every tuning decision is backed by a benchmark number rather than a gut feel.
04
Data freshness and incremental indexing
We sync incrementally with source systems — SharePoint, Confluence, Drive, ticketing, contract repositories — so superseded versions get flagged, down-ranked, or re-indexed, and answers never cite content that's no longer valid.
05
Multi-format and table parsing
Handles PDFs, scanned documents, Word, Excel, and long contracts with structured parsing that preserves clause numbers, table columns, and section hierarchy — so retrieval can pin down "Section X, Clause Y" precision.
06
Private deployment and data isolation
Deploy in your VPC or on-prem, with your choice of model from Anthropic, OpenAI, or a local open-source option. Data never leaves your boundary, meeting SOC 2, GDPR, and internal audit requirements.
Use cases
Legal contract search
Legal and sales teams instantly query thousands of contracts for auto-renewal clauses, liability caps, and data-protection obligations — each answer cites the exact contract passage, so negotiation and due diligence no longer mean manual page-flipping.
Manufacturing SOPs and equipment manuals
Line operators ask in plain language about machine troubleshooting, maintenance cycles, and safety clauses; the system retrieves from SOPs, equipment manuals, and MES ticket history, citing exactly which version of the work instruction it came from.
Customer support knowledge base
Support agents pull sourced answers from past tickets, product docs, and FAQs to cut first-response time while ensuring every reply stays consistent with the latest policy.
Financial compliance KYC/AML lookup
Compliance teams query internal policies, regulatory notices, and procedures with access control intact (different tiers see different documents), and every query and citation is logged for audit traceability.
Project knowledge and handover
Consultants and engineering teams query past project records, decision docs, and meeting outcomes so new joiners ramp fast on context — and institutional knowledge doesn't walk out the door with departing staff.
Delivery cadence
WEEK 1
Data inventory and eval baseline
Map source systems, permission models, and document formats, then build a golden eval set with your domain experts — defining what "correct" means before we build a single index.
WEEK 2–3
Indexing, permissions, and retrieval pipeline
Stand up the parsing and incremental indexing pipeline, wire in existing access controls, implement sourced retrieval and answering, and produce a first benchmark against the eval set.
WEEK 4–5
Tuning and pilot
Iterate on chunking, ranking, and prompting against the eval metrics, fold in internal pilot feedback, and keep pushing citation accuracy and faithfulness up.
WEEK 6
Launch and handover
Finalize private deployment, monitoring dashboards, and data-freshness alerts, and hand over the eval process and ops docs so your team can keep iterating on its own.
100%
answers with citations
6 weeks
to production
SOC 2 / GDPR
compliant deployment
FAQ
How do you keep answers from citing stale or outdated documents?
We sync incrementally with your source systems, so when a document is updated or superseded, the old version is re-indexed, down-ranked, or flagged as invalid and stops surfacing in retrieval. Data freshness is monitored as a metric with configurable alerts, so "is this answer still valid?" becomes something you track — not something you discover after a complaint.
How are permissions handled? Could an employee see content they shouldn't?
No. The system reuses your existing AD / SSO and permission groups and enforces access control at both the retrieval and answer layers, so users can only retrieve documents they already have rights to. We never stand up a looser permission model for the sake of RAG, which keeps AI from becoming a data-leak backdoor.
How do you prove retrieval is actually accurate, not just plausible-sounding?
On day one we build a golden eval set with your domain experts and quantify every change with retrieval recall, citation accuracy, and answer faithfulness. Accuracy shows up as benchmark numbers, so each iteration visibly improves or regresses — it's measured, not guessed.
Our contracts and internal docs are sensitive. Will data leave our environment?
You can deploy in your own VPC or on-prem, and data never leaves your boundary. You choose the model — Anthropic, OpenAI, or a local open-source option — based on your compliance needs, and the design meets SOC 2 and GDPR while logging queries and citations for internal audit.
Can it handle scanned PDFs and complex tables?
Yes. Our parsing pipeline handles PDFs, scanned documents, Word, and Excel while preserving clause numbers, table columns, and section hierarchy, so retrieval can pin down "Section X, Clause Y" precision — especially useful for long contracts and technical manuals.
Why 6 weeks? How is this different from a typical PoC?
Because we deploy engineers on-site (FDE) and target something you can sign off on from week one — defining eval criteria before building, so we never get stuck in a demo with no metrics. In 6 weeks you get a system with citations, permissions, benchmarks, and production ops — not a chat window built to impress in a meeting.
A new era of
AI-native products
Ship your first AI use case in weeks, not quarters.