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Generative AI

Generative AI that works with company data and systems

Generative AI is AI that produces text, summaries or drafts from instructions. It is most useful when connected to business context: internal data, company rules and the applications your team uses every day. MBT builds that integration layer.

Generative AI Development, AI Copilot

  • AI copilots inside your applications
  • Summarization & drafting
  • Guardrails and access control
  1. 01Business data & context
  2. 02Prompts & instructions
  3. 03AI model
  4. 04Output validation
  5. 05User application

Business problems

Business problems we solve

Drafting takes time

Customer replies, reports and routine documents are written from scratch every time.

Long information is hard to digest

Long reports, tickets and conversations have to be read in full to get the point.

Public AI is a data risk

Employees paste company data into public AI services without any control.

What we build

What we build

  • 01

    AI Copilot

    An assistant inside internal applications that drafts content, answers questions and handles routine tasks.

  • 02

    Summarization

    Automatic summaries of reports, tickets, conversations and long documents.

  • 03

    Document Q&A

    Direct question answering over documents with source references.

  • 04

    Prompt & guardrail engineering

    Instructions, topic limits and output validation so answers stay consistent with company policy.

Example applications

Example applications

  • Customer reply drafts

    Service teams get reply drafts based on ticket history and the knowledge base.

  • Daily report summaries

    Managers receive summaries of operational activity from system data.

  • Document drafting assistant

    Letters, minutes or proposals are drafted from templates and existing data.

Architecture & approach

Architecture & approach

AI output always passes a control layer before it reaches users.

  1. 01

    Context

    Relevant data is pulled from systems according to the user's access rights.

  2. 02

    Instructions

    Prompts and topic limits follow company policy.

  3. 03

    Validation

    Output format and content are checked, with human approval for important actions.

  4. 04

    Logging

    Usage, cost and feedback are recorded for regular improvement.

Technology & integration

Technology & integration

  • LLM API integration
  • Python
  • Prompts & guardrails
  • REST API
  • Laravel (PHP)

Deployment

Deployment models

Option Description Best for
Model via API High quality and a fast start, with control over the data sent.
  • General drafting & summaries
Private model Open-source models on internal servers for data that must not leave.
  • Sensitive data
  • Strict data policies

FAQ

Frequently asked questions

Which AI models do you use?
We select models by quality, cost, latency and company data policy, then integrate them through APIs or run them on internal servers.
How do you prevent wrong AI answers?
By providing context from company data, limiting topics, validating output and requiring human approval for actions with impact.

Ready to discuss your requirements?

Tell us about your context and goals. We will help map the solution, priorities and an initial estimate.