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Kodex

Technology solutions

Integrations & MCP

We give your AI a secure port to ERP, CRM, and SQL. Business sees decisions on live data; engineering sees MCP servers, tools, scopes, and audit.

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What it is and why it matters

From silo to universal port

MCP (Model Context Protocol) standardizes how a model discovers tools and data. Stop wiring every chatbot to every API: one protocol, clear policies, auditable execution.

  1. 01

    We inventory

    Systems, APIs, and actions the AI must be able to read or execute — with owner and risk.

  2. 02

    We expose

    MCP servers with tools and resources: the AI sees capabilities, not loose endpoints.

  3. 03

    We govern

    Scopes, approval gates, and logs: writes only with policy; reads with ACL.

Without MCP

  • Point-to-point integrations that break
  • AI that only ‘talks’ without touching systems
  • Opaque or overly broad permissions
  • Every new model = rebuild connectors

With Kodex MCP

  • Unified port over existing APIs
  • AI that queries and acts with evidence
  • Scopes and HITL on critical writes
  • Same connectors for Claude, GPT, or local

Live flow

Del agente al sistema — con protocolo y gobierno

Two lanes: tool discovery and live execution with policy — from agent to client system.

Flujo MCP vertical: descubrimiento de tools y ejecución con scopes hasta la acción en sistemas

01 · Descubrimiento

  1. systems

    Sistemas

    erp · crm · sql

  2. mcp server

    MCP Server

    tools · resources

  3. catalog

    Catálogo

    schemas · versions

02 · Ejecución en vivo

  1. agent

    Agente / LLM

    host · session

  2. mcp client

    MCP Client

    call · stream

  3. policy

    Scopes / HITL

    allow · approve

  4. action

    Acción

    read · write · audit

Descubrimiento de tools · luego ejecución con scopes hasta la acción.

Depth

Choose your MCP port level

Three depths. Start with safe read access and grow to governed execution on critical systems.

  1. 01
    Read Safe query
  2. 02
    Tools Bounded actions
  3. 03
    Port Multi-system
  4. 04
    Governance Scopes · HITL
  1. Safe system query via MCP

    01

    Nivel 01

    Read MCP

    Live data in chat — no writes

    Ideal for querying stock, accounts, or tickets from the assistant without touching write systems yet.

    Qué incluye

    • 1–2 MCP servers
    • Read tools
    • Basic ACL
    • Query logs

    Ideal para · Teams that want real context in the assistant now.

    Resultado · Answers anchored to live ERP/CRM.

  2. MCP tools with access governance

    02

    Nivel 02

    Operational MCP

    Read and act with scopes

    Bounded write tools (create ticket, update field) with policies and human review where needed.

    Qué incluye

    • Read + write tools
    • Role-based scopes
    • Approval gates
    • Helpdesk / CRM integration

    Ideal para · Ops and CX teams that want less manual work.

    Resultado · Fewer clicks · more actions completed by the agent.

  3. Multi-system MCP port

    03

    Nivel 03

    Recomendado

    Industrial port

    Multi-system + ISO audit

    Full layer: multiple backends, tool discovery, observability, and deployment inside your perimeter.

    Qué incluye

    • Everything in operational MCP
    • ERP + CRM + SQL / internal
    • Action audit
    • On-prem / private cloud

    Ideal para · IT and architecture teams that need executable AI at scale.

    Resultado · One port, many models — without rebuilding integrations.

MCP does not replace your APIs: it exposes them as discoverable, governed capabilities for AI.

Book MCP architecture Ver puerto MCP en vivo

MCP port

How AI talks to your systems

From agent to ERP: tools discovered, scopes applied, action logged. Business sees agility; engineering sees protocol and governance.

Protocol
MCP
Capabilities
Tools
Permissions
Scopes
Traceability
Audit

What the client gains

MCP is not ‘another API’: it is how AI uses your systems without explosive technical debt.

01

Decisions on live data

Quotes, stock, and tickets reflect real state — not yesterday’s export.

02

Less connector debt

A reusable port for new models or channels, instead of N fragile integrations.

03

Controlled write risk

Scopes and HITL stop an agent from ‘improvising’ in ERP or billing.

04

Auditable operations

Every tool call is recorded: defensible for IT, ISO, and leadership.

Dual reading

Same architecture, two languages.

  • Negocio

    AI queries ERP without leaving chat

    Ingeniería

    MCP tool → API/ERP adapter → response schema

  • Negocio

    It can only do what is allowed

    Ingeniería

    OAuth/scopes · allowlists · approval gates

  • Negocio

    We switch models without rebuilding everything

    Ingeniería

    Stable MCP host · versioned servers

  • Negocio

    We know what the agent did

    Ingeniería

    Structured logs · correlation IDs · replay

From fragile integrations to a stable port

Illustration: how effort redistributes when you stop wiring every channel to every API.

  • Systems to connect 100%
  • Point-to-point connectors today ~70%
  • With MCP port ~25%
  • Rewrites per new model ~8%
  • Writes without policy ~1%
System connectivity via MCP port
MCP value is measured in integration time and safe actions — not chat demos.

Casos de uso

AI connected to ERP

01

AI with ERP access

Business: orders and logistics in chat. Technical: read/write tools over the ERP API.

CRM sync via MCP

02

Real-time CRM

Business: quotes based on the current customer. Technical: sync tools + post-action events.

Own tools ecosystem

03

Your own tools

Business: your internal software usable by AI. Technical: MCP server over internal APIs.

Relacionado

Why the MCP port protects margin and architecture

Information without real-time access is dead information. Static integrations are debt that grows with every AI channel.

Business

Agility without chaos

AI operates on real stock, payments, and customers — with less waiting for ‘IT to build another connector.’

Engineering

Protocol + policies

MCP servers, tool schemas, and scopes: discoverable, versionable, and auditable over your current APIs.

Illustration · Integration time

−60%

illustrative effort for new AI channels vs point-to-point connectors

Connect DS
Connectors to rebuild −60%

↓ 60 % vs point-to-point APIs

Governed actions +45 pts

↑ 45 pts vs point-to-point APIs

Port reuse index (0–100)

Illustrative example: the MCP port raises reuse when adding models or channels without rewriting backends.

Impact on connectivity and risk

Each lever shortens the path from ‘AI knows it’ to ‘the system does it’ — with control.

  1. 01 −60% wiring

    Time-to-connect

    New channels reuse the same MCP port.

  2. 02 Live

    Live data

    Queries against real ERP/CRM state, not copies.

  3. 03 Scopes

    Safe writes

    Scopes and HITL on high-risk actions.

  4. 04 Audit

    Audit

    Every tool call is logged and correlated.

Cycle in days

12d → 3d

illustrative time to expose a new system to the assistant

  • API / permissions inventory 3d → 1d

    Antes

    Con Kodex

  • Connector / adapter 5d → 1.2d

    Antes

    Con Kodex

  • Scopes / HITL policy 2d → 0.5d

    Antes

    Con Kodex

  • Tests and go-live 2d → 0.3d

    Antes

    Con Kodex

Illustrative values. In project we measure your integration backlog and the post-MCP-port delta.

How we deliver

Methodology: from silo to universal port

From system inventory to a production port — with clear deliverables for business and engineering.

  1. 1–2 wk

    Fase 1

    Connectivity consulting

    What the AI must be able to do

    We inventory systems, APIs, and critical permissions — with a read vs write risk map.

    • System inventory
    • Tool matrix
    • Read / write / port scope
  2. 2–3 wk

    Fase 2

    Security audit

    Latency, contracts, and scopes

    We validate security, latency, and data contracts before exposing capabilities to the model.

    • API gap
    • Scope policy
    • HITL plan
  3. By scope

    Fase 3

    MCP implementation

    Servers, tools, and governance

    We deploy MCP servers, tool schemas, and access policies with observability.

    • MCP servers in prod
    • Tool catalog
    • Audit logs
  4. Ongoing

    Fase 4

    Maintenance

    When the stack changes, the port holds

    We evolve connectors and policies as ERP/CRM or new models change.

    • Tool versioning
    • Monitoring
    • Backend roadmap

Ver metodología completa →

Trust perimeter

Scopes, perimeter, and evidence

Executable AI is an extension of your security policy — not an ownerless webhook.

  • Permissions

    Scopes and allowlists

    Each tool declares what it can do; AI does not inherit ‘admin’ by default.

    Least privilege

  • Risk

    HITL on writes

    Critical actions go through human approval or strict rules.

    Approval gates

  • Evidence

    Tool-call audit

    Who, what, when, and on which system — correlated and retained.

    Audit trail

FAQ — architecture and IT

Does it replace our current APIs?

No. MCP sits as a discovery and safe-execution layer over your existing APIs and systems.

How is write risk controlled?

With scopes, approval gates, and action audit — AI does not write without explicit policy.

Does it work on-premise?

Yes. We can deploy the MCP port inside your perimeter with the same ISO controls.

Does it work with multiple models?

Yes. The port’s value is reusing servers/tools with Claude, GPT, or other MCP-compatible hosts.

Filtro de oro

How many systems should your AI talk to?

Option A · MCP port (DFY)

Design, deployment, and governance of critical connectors with traceability.

Book MCP architecture

Option B · Explore guides (DIY)

Start with patterns and technical docs in KodexLAB.

View MCP guides

Más soluciones

Turn integrations into a reusable port

Kodex MCP: your AI reads and acts on real systems — with scopes, audit, and without rewiring every channel.

Book a consultation View solutions catalog

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Tell us your goal

A short triage to qualify your request. In a few minutes we reach the right scope.

1

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