Bomshteyn
Consulting

Canadian-led · Serving businesses across the US and Canada

AI integration services for your business systems

Connect ChatGPT and Claude to the information your business already holds. We build practical AI integrations that help owners ask better questions, follow the source records and use business definitions that match how the operation works.

Discuss your AI integration

  1. 01

    Connect

    Approved access to the systems you already use.

  2. 02

    Define

    Your reporting terms, dates and source boundaries.

  3. 03

    Ask

    Useful business questions with a traceable basis.

Put your business context within reach

Your software holds the records, but answering a useful question often means asking someone to export them, explain the columns and rebuild a report. Our AI integration work joins that information to a conversational tool, with the business meaning included in the connection.

01

ChatGPT and Claude connectors

We have built MCP connectors for rental-management and charity reporting systems. MCP is the connection layer through which an AI tool can discover the available business information and request a query.

For an owner, the useful result is a way to investigate a question in familiar language. A rental question can use operational records, while a donation question can identify the source and reporting period behind an amount. We build the connection around the actual database structure and the questions the business needs to answer.

02

Definitions that belong to your business

A column name rarely explains what a number means to the people using it. Our reporting connector includes editable business definitions that describe terms, reporting rules and the sources behind a calculation.

Those definitions help distinguish a recorded donation from an issued receipt, or a transaction date from an upload date. They give the AI tool context before it prepares a query. When a reporting convention changes, the business has a defined place to update the explanation instead of relying on instructions scattered across conversations.

03

Old records, new systems and accounting exports

Our charity reporting work connects older and newer application databases alongside uploaded accounting exports. That makes source selection a central part of the integration: an all-history question has a different scope from a question about the current application.

The reporting context identifies date coverage, amount conventions and overlapping records. An operational donation and its accounting entry can describe the same activity, so the connection supplies reconciliation guidance. This gives the reviewer a stronger starting point for comparing reports and tracing a difference back to its source.

04

A controlled route into existing systems

The connectors use OAuth sign-in and read-only reporting access, with allowed tables and query restrictions. Access is limited to the information needed for the reporting task, and people remain responsible for interpreting consequential answers.

Your databases remain in your systems as the source of truth. Selected results sent to ChatGPT or Claude are part of the agreed provider boundary. We define that boundary during discovery so the owner understands which information the connection can retrieve and where the AI tool processes it.

From a reporting question to a working connection

Delivery is Canadian-led, with our senior developers in Europe building Laravel/PHP connections around your existing applications. Paid discovery establishes the first reporting task, then phased builds make the connection useful in manageable steps.

  1. 01

    Discover the reporting job

    We map who needs an answer, where the records live and how staff calculate it today. Discovery covers database access, older records, exports and the definitions behind recurring reports.

    Together we choose a focused first connection and agree how its answers will be checked. That gives the build a concrete scope and gives your staff a clear role in reviewing the result.

  2. 02

    Build and test the connector

    The first phase connects the approved sources and supplies the reporting context. We test familiar questions against known records, including questions where the date basis or source scope changes the answer.

    Your team reviews the results alongside the way it already works. The aim is a connection people can understand and use during a real reporting conversation.

  3. 03

    Extend the useful coverage

    Later phases can add another source, a reporting definition or a reviewed query pattern. We work through the next operational question rather than expanding access without a purpose.

    The owner can decide which questions are worth supporting next. Each phase builds on the connection, definitions and checks already in place.

Systems that give the connector useful context

These examples show the rental and charity workflows behind the reporting questions. They explain the operational records and processes that a useful AI connection needs to understand.

Questions about AI integration and MCP

What is MCP server development?

It is the work of building a connection that exposes defined tools and business context to an AI application. For our reporting connectors, that includes discovering the available data, explaining business terms and running permitted queries. The scope follows the reporting job and the underlying systems.

Can we use ChatGPT or Claude with our own database?

Yes, our connector work supports that pattern. We first review the database, the required access and the questions you want to answer. The connector supplies both the available information and the definitions needed to interpret it in the context of your business.

Can the integration include older records?

Our reporting work includes old and new application databases alongside uploaded accounting exports. We identify which sources cover which periods and how their records relate. This is especially important when historical and current systems represent the same business activity in different ways.

Who maintains the meaning of a report?

The connection can carry editable definitions for reporting terms and business rules. During discovery we identify who owns those definitions and how changes should be reviewed. That keeps the explanation close to the reporting tools and gives staff a shared reference when questions arise.

How do we start an AI integration project?

Bring a recurring question and an example of the report or export used to answer it today. We will map the sources and propose a paid discovery step. From there, the first build phase can focus on a connection your team can check against familiar records.

Related services

Start with a question your business asks every week.

Show us the report, export or database behind the answer. We will help define the first connection, the business context it needs and a phased route to putting it into use.

Start a project brief
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