source
Fuentes
pdf · sql · tix
Kodex
Technology solutions
RAG Systems
We turn your company’s knowledge into searchable memory: AI looks through your documents, cites the source, and speeds decisions — without training public models on your data.
What it is and why it matters
From scattered folders to answers that drive revenue
RAG (Retrieval-Augmented Generation) is not “a pretty chatbot”: it is retrieving the right fragment of your knowledge before the model speaks. Fewer errors, less wasted time, more capacity to serve customers.
- 01
We index
PDFs, Word files, tickets, and SQL become chunks with embeddings — ready for semantic search.
- 02
We retrieve
When a question arrives, the system pulls the most relevant passages from your vector store.
- 03
They answer with proof
The model synthesizes while citing sources. That lowers legal risk and speeds case closure.
Without RAG
- AI invents or mixes outdated policies
- Experts repeat the same searches
- Onboarding is slow and expensive
- L1 support saturates senior staff
With Kodex RAG
- Answers anchored to current documents
- Auditable citations in every interaction
- 24/7 knowledge without linear headcount growth
- More tickets resolved · fewer escalations
Live flow
Del documento a la acción en sus sistemas
Two lanes like a professional workflow: indexing (documents → vectors) and live query (question → retrieve → cited answer → CRM/ticket event).
Flujo RAG vertical: indexación y consulta en vivo hasta el evento en el sistema del cliente
01 · Indexación
ingest
Embeddings
chunk · encode
vector db
Índice semántico
ann · top-k
02 · Consulta en vivo
query
Pregunta
user · canal
retrieve
Recuperar
top-k chunks
generate
Respuesta
llm · citas
client system
Evento
crm · ticket · erp
dato
consulta
Depth
Choose your corporate memory level
Three depths. Start with a critical corpus and grow toward multi-source with identity governance.
- 01Corpus Key docs
- 02Operational Support · legal
- 03Industrial Multi-source
- 04Actions RAG → events
-
01
Nivel 01
Corpus RAG
One source of truth — answers with citations
Ideal for manuals, policies, or product knowledge that today live in SharePoint or PDFs.
Qué incluye
- Ingestion and chunking
- Vector index
- Chat with citations
- Basic access control
Ideal para · Teams that need veracity now in a specific domain.
Resultado · Fewer hallucinations on the knowledge that hurts most.
-
02
Nivel 02
Operational RAG
Support, legal, or ops with metrics
We connect multiple repositories, measure perceived accuracy, and reduce mean time to resolution.
Qué incluye
- Multi-collection
- Role-based permissions
- Answer evaluation
- Helpdesk / CRM integration
Ideal para · CX, legal, and middle-office teams with high query volume.
Resultado · More cases closed at L1 · fewer expert hours.
-
03
Nivel 03
RecomendadoIndustrial RAG + actions
Retrieve → decide → execute in your systems
The answer does not stop in chat: it triggers events in CRM, ERP, or tickets — with HITL when risk requires it.
Qué incluye
- Everything in operational RAG
- Bridge to orchestration / MCP
- Evidence logs
- Private / on-prem deployment
Ideal para · Organizations that want AI that moves work, not just text.
Resultado · Knowledge that accelerates revenue and reduces error cost.
You can start with one corpus and add sources later: permission and citation design avoids rebuilding the foundation.
Vector memory
How RAG works in your company
From the client document to action in your systems: every step is visible. Business sees value; engineering sees embeddings, retrieval, and evidence.
- Semantic memory
- Vectors
- Proof in every answer
- Citations
- No public training
- Private
- RAG → systems
- Actions
What the client gains
RAG is not a trendy cost: it is sellable operational capacity and controlled risk.
01
More revenue per service
Support and consulting close more cases per agent: less time searching, more time resolving (and billing).
02
Lower cost of error
Answers anchored to current policy reduce rework, escalations, and legal exposure.
03
Scale without linear headcount
Senior knowledge stays available 24/7 — onboarding and L1 no longer depend on “the person who knows.”
04
Defensible decisions
Every answer carries a source: auditable for ISO, clients, or the board.
Dual reading
Same architecture, two languages.
-
Negocio
AI uses our documents, it does not invent
Ingeniería
Retrieve top-k chunks → prompt grounded → citations
-
Negocio
We find the paragraph in seconds
Ingeniería
Embeddings + ANN search on a vector index
-
Negocio
Only the right people see it
Ingeniería
ACL / tenant filters on retrieval
-
Negocio
The answer moves the CRM or ticket
Ingeniería
Tool calls / post-answer events with HITL
From wasted time to sellable capacity
Illustration: how effort redistributes when search stops being manual.
- Incoming queries 100%
- Manual search today ~60%
- With grounded RAG ~22%
- Escalated to expert ~12%
- Errors / retractions ~3%
Casos de uso
01
Technical assistant
Business: faster diagnostics. Technical: retrieval over AMM/IPC and history with citations.
02
Legal and compliance
Business: less contractual risk. Technical: semantic eDiscovery with permission filters.
03
Support that bills
Business: more L1 tickets closed. Technical: RAG + bridge to helpdesk / CRM.
Why indexed truth protects margin
An “almost correct” answer in legal or aerospace is not a nuance: it is risk. Well-built RAG is veracity infrastructure.
Business
Capacity and trust
More queries resolved per person, fewer escalations, and a brand that does not improvise policies.
Engineering
Vectors + governance
Embeddings, ANN, re-ranking, and identity filters — with logs of which chunk fed each answer.
Illustration · Knowledge return
+28%
illustrative resolution capacity vs manual search + chat without grounding
Time searching −55%
↓ 55 % vs chat without RAG
Cases closed at L1 +28 pts
↑ 28 pts vs chat without RAG
Perceived veracity index (0–100)
Illustrative example: trust rises when moving from a bare LLM to RAG with citations and an audited corpus.
Impact on time, risk, and revenue
Each lever shortens the path from question to useful action — in minutes for ops, in evidence for legal.
- 01 −70% search
Time-to-answer
From minutes of searching to seconds with the right paragraph.
- 02 Grounded
Fewer hallucinations
The model only speaks with retrieved, current context.
- 03 Evidence
Auditable citations
Every answer links to the source — defensible before a client or ISO.
- 04 → Event
Action in systems
The answer can open a ticket, update CRM, or trigger a flow.
Cycle in minutes
18m → 3m
illustrative time from question → useful answer with evidence
- Locate document 8m → 0.5m
Antes
Con Kodex
- Validate currency 4m → 0.8m
Antes
Con Kodex
- Draft / respond 4m → 1.2m
Antes
Con Kodex
- Log in system 2m → 0.5m
Antes
Con Kodex
Illustrative values. In project we measure your baseline (AHT, escalation %, CSAT) and the post-RAG delta.
How we deliver
Methodology: path to truthful AI
From sources of truth to a production system — with clear deliverables for business and engineering.
- 1–2 wk
Fase 1
Knowledge consulting
What must be true
We identify sources, owners, permissions, and questions that burn hours or create risk today.
- Corpus map
- Prioritized use cases
- Corpus / operational / industrial scope
- 2–3 wk
Fase 2
Data audit
No indexing noise
We clean and structure documentation so retrieval does not pull garbage.
- Chunking rules
- Quality gaps
- Update policy
- By scope
Fase 3
RAG implementation
Vectors + identity + citations
Vector architecture, access security, and UI/API with evidence in every answer.
- Production index
- Quality evaluation
- Channel integration
- Ongoing
Fase 4
Evolutionary maintenance
New docs, same accuracy
Continuous ingestion, model tuning, and a bridge to actions in client systems.
- Reindex pipeline
- Quality monitor
- Actions roadmap
Trust perimeter
Sovereignty, security, and evidence
Knowledge does not leave to train public models. Three folders, one Kodex standard.
-
Data
Private perimeter
Private cloud or on-premise: your documents stay under your control.
No public training
-
Access
Identity and ACL
Retrieval filtered by role and tenant — no one sees what they should not.
Least privilege
-
Proof
Citations and logs
Every answer records which chunks it used — auditable for ISO and legal.
Evidence trail
FAQ — board and engineering
Are public models trained on my data?
No. We deploy in private cloud or on-premise. RAG queries your index; it does not hand your corpus to public training.
How does it generate more revenue?
By cutting search time and escalations, the same team handles more cases (support, legal, ops) with fewer errors — sellable capacity without linear headcount.
What latency do you offer?
Retrieval + generation in times comparable to a good internal search, with synthesis and citations. We optimize ANN and indexes by volume.
How are documents updated?
An ingestion pipeline from your audited repository: adds, removals, and versions reindex without rebuilding the product.
Filtro de oro
How much accuracy do you need?
Option A · Cognitive infrastructure (DFY)
For projects that require absolute veracity, traceability, and industrial-grade security.
Launch RAG configuratorOption B · Data preparation (DIY)
To start by structuring files with conversion and cleanup tools.
Explore KodexLABMás soluciones
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Procesos
Automatizaciones orquestadas
Menos horas y errores para negocio; eventos, MCP y logs para ingeniería — no triggers aislados.
Ver → -
MCP
Integraciones & MCP
Puerto MCP: la IA lee y actúa sobre ERP/CRM con scopes y auditoría — sin conectores punto a punto.
Ver → -
LLM
LLM & asistentes de IA
Puestos aumentados: LLM + RAG + MCP con guardrails — respuestas con evidencia y acciones gobernadas.
Ver →
Turn knowledge into competitive advantage
A Kodex RAG does not just “answer”: it retrieves your company’s truth, cites it, and can move work in your systems.