SERVICE · RAG KNOWLEDGE
RAG Knowledge System
Turn enterprise documents and data into answers you can actually trust. Every answer cites its source, retrieval respects your permissions, and you watch retrieval quality as a number against eval benchmarks.
DEFINITION · WHAT RAG IS
What is a RAG knowledge system?
A RAG knowledge system (Retrieval-Augmented Generation) is an architecture in which a large language model first retrieves relevant passages from your own document corpus at query time, then generates an answer grounded in them. The point isn't to make the model smarter. It's to make answers verifiable: every claim carries sentence-level citations (document, page, chunk), retrieval inherits your existing AD/SSO permissions, quality is measured with metrics like retrieval recall, citation accuracy, and answer faithfulness, and incremental indexing keeps data fresh. In short, RAG swaps the model's memory for knowledge you can audit.
Retrieval before generation
The model doesn't answer from what it memorized in training. It retrieves the most relevant passages from your corpus on each query and grounds the answer in the source. Updating knowledge means re-indexing, not retraining.
Answers carry citations
Every conclusion traces back to a specific document, page and passage you can open and verify. When there's no reliable basis, the system says so rather than filling the gap with plausible-sounding text.
Permissions and isolation built in
The retrieval layer applies your existing access controls, so users only retrieve what they're already allowed to see; data can stay in your VPC or on-prem, adding no new exposure surface from adopting AI.
Quality you can accept on numbers
Against a golden set built with your domain experts, recall, citation accuracy and faithfulness turn 'is it accurate?' into measurable numbers, so every tuning pass is backed by a benchmark, not a hunch.
COMPARISON · RAG vs A GENERAL LLM / KEYWORD SEARCH
How is RAG different from just asking an LLM or keyword search?
Ask a general LLM and you get an answer that sounds right but can't be checked; use keyword search and you get a pile of links to read yourself. A RAG knowledge system sits between the two and surpasses both: it uses semantic retrieval to find the right passages, then has the model assemble a cited, auditable answer. The table contrasts Tenten's delivered RAG with these alternatives, dimension by dimension.
General LLM / keyword search / fine-tuning only
RAG knowledge system (delivered by Tenten)
Source / traceability
A general LLM cites nothing; keyword search returns links and leaves you to assemble the answer
Every conclusion carries document / page / chunk citations you can open and verify
Hallucination control
The model fills gaps with plausible-sounding text, confidently and hard to spot
Answers are bounded by retrieved sources; with no reliable basis it clearly says 'not found'
Permission awareness
General models and most keyword search are permission-blind, exposing what users shouldn't see
Retrieval inherits existing AD/SSO/groups; users only retrieve documents they're entitled to
Freshness / updates
Model knowledge is frozen at the training cutoff; updating means costly, slow retraining or fine-tuning
Incremental sync with source systems; stale versions are demoted or re-indexed, fast and cheap
Measurable quality
Goodness is subjective; no consistent metric to track regressions or gains
A golden set quantifies recall / citation accuracy / faithfulness, comparable across iterations
Multi-format / table parsing
Keyword search struggles with scans and tables; general models scramble clause numbers and column structure
Structured parsing of PDFs / scans / Excel / long contracts, preserving clause numbers and table fidelity
Deployment / data isolation
Public APIs send sensitive documents past your boundary; fine-tuning bakes data into model weights
Deployable in your VPC or on-prem; data never leaves your boundary, meeting SOC 2 / GDPR
If an answer can't be checked, no one will use it
The 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 sit 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.
We deploy engineers on-site (FDE) and ship RAG as a retrieval system you can sign off on, not a chatbot demo. Every answer carries sentence-level citations down to the document, page, and chunk, so you 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 of all, it's measurable. On day one we build a golden eval set with your domain experts and track every change against retrieval recall, citation accuracy, and answer faithfulness. Data freshness holds through incremental indexing and source sync, so stale content gets flagged or down-ranked, and "is this answer still valid?" becomes a number you watch instead of 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, and 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
Retrieval quality you can read off a number
We build a golden eval set with your domain experts and track retrieval recall, citation accuracy, and answer faithfulness, so every tuning decision rests on 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 query thousands of contracts for auto-renewal clauses, liability caps, and data-protection obligations in seconds. 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. We define 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. That matters most 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. We define 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.
KNOWLEDGE BASE · FURTHER READING
The RAG knowledge system knowledge base
We've gathered the design trade-offs of enterprise RAG here, from Tenten AI's first-hand delivery experience to the original research and official docs that define the field, so you can build your own judgment framework.
Tenten AI whitepapers & perspectives
INSIGHTS · LATEST
The latest on enterprise AI & delivery
First-hand observations and methodology from Tenten AI's delivery floor.
AI workflows,
built into your operations
We deploy forward — FDE and FDM — to build the AI agents and workflows your team runs on. Live in weeks, not quarters.