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// SOLUTION

AI integration and implementation services

We connect AI models to the systems, documents and processes a company already uses, so they answer with real information, carry out authorized tasks and keep a record of what they do.

SYSTEMSDATAMODELSPERMISSIONSAUDIT AI INTEGRATIONORVIXLABS
// SOLUTION ARCHITECTURE
CONNECT WHAT ALREADY WORKS · ADD THE INTELLIGENCE THAT IS MISSING CRM / ERPDOCUMENTSCHANNELS AI LAYERINTEGRATE · COORDINATEAUTOMATE · TRACEwithout replacing systems that still work ANSWERSTASKSAUDIT TRAIL measure → learn → adjust
// USE CASES
01AI connected to CRM and ERP
02Search across internal documents
03Assistants for the team
04AI inside in-house software

What integrating AI means

Using an AI assistant in a browser is not the same as integrating it into the company. An isolated model only knows what it is told at that moment. Integrated, it can query the CRM, read the procedures manual, check an order status or log a task, and its answer relies on company data instead of general knowledge. Integration is the work of connecting those pieces with clear permissions.

What can be connected

  • Business systems: CRM, ERP, scheduling, billing, inventory or custom-built software.
  • Documents: manuals, contracts, catalogues, internal policies, customer histories or case files.
  • Channels: WhatsApp, email, website chat, phone and the team’s internal tools.
  • Data: databases, spreadsheets and reports, with read-only access when that is enough.

How it answers with company information

For a model to use internal documents without making things up, a retrieval layer finds the relevant passages before the answer is generated (a technique known as RAG, retrieval-augmented generation). Each answer can show which document it came from. When the information does not exist or is contradictory, the system says so instead of filling the gap with guesses.

What it may and may not do

Reading, writing and executing are different permissions. An assistant may be allowed to check inventory but not to change prices; to draft a reply to a complaint but not to send it without review. Those limits are defined before building and enforced in code, not in a model instruction that could be ignored.

Which model to use

No single model is right for everything. Depending on the task, cost, latency and data sensitivity, we can use a commercial model through an API, an open model on your own servers or a combination. The architecture avoids depending on one provider: switching models should not mean rebuilding the system. When data cannot leave the company, see private AI.

How AI implementation works

  1. Pick one concrete case and measure how it works today.
  2. Define data sources, permissions and rules for handing off to people.
  3. Build a first version and test it with real cases, including the hard ones.
  4. Go live under supervision and compare against the initial measurement.
  5. Expand only what proved its value.

Privacy

Before sending information to an external provider we decide which data is actually needed. Personal, health or financial data can be anonymized or kept out of the flow. Varexis adds a technical boundary for those cases.

What is measured

Answer accuracy against real cases, tasks completed without correction, handoffs to people, time saved and integration errors. An integration is successful when it improves a business indicator, not when the demo looks impressive.

// RELATED SERVICESAI agent developmentWhen the integration has to carry out tasks, not only answer.→API and systems integrationConnecting CRM, ERP, online store and other platforms with each other.→Private AIModels running on your own infrastructure when data cannot leave.→VarexisA technical boundary to minimize data before using external models.→AI implementation costWhat drives the cost of building and running an AI system.→Digital transformationProcesses digitized and integrated before adding AI.→
// FAQ

Frequently asked questions

What is AI integration?+

It means connecting AI models to a company’s systems, documents and channels so they work with real information and within defined permissions, instead of using them as an isolated tool.

Can AI be integrated into our existing CRM or ERP?+

Usually yes, if the system offers an API, database access or another authorized integration path. We assess it case by case, and the current system remains the source of truth.

How much does AI integration cost?+

It depends on how many systems must be connected, usage volume and the chosen model. We quote after reviewing the case and separate the build cost from the monthly running cost.

Can the AI make up answers?+

A model can be wrong. That is why answers rely on documents and data retrieved from the company, show their source and, when the information is not enough, the system says so or hands off to a person.

What is RAG?+

Retrieval-augmented generation finds the relevant information in your own documents before the model answers. It lets the system use internal knowledge without retraining models or pasting whole documents into every request.

Do we need to replace our software to use AI?+

Not necessarily. The first option is to add the AI layer on top of what already works. Replacing a system is only proposed when there is no reasonable way to integrate it.

What does AI implementation involve?+

Choosing one concrete process, connecting the model to the data and systems that process needs, defining permissions and approval rules, testing with real cases and measuring the result against the previous situation before expanding.

How long does AI implementation take?+

It depends on the integrations and the quality of the data. A first, well-bounded project on one process and a few systems moves much faster than a company-wide change. The assessment sets scope and timing before work starts.

// ORVIXLABS

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