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Ahmad Humayun
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Data systems & AI access

Connect Claude or ChatGPT to reliable business data.

Bring scattered business data together, agree what the metrics mean, and make the prepared data available through an authenticated AI connection. Start with a defined set of questions and expand as the need grows.

For founders and teams who want to explore reporting or application data through the AI tools they use.

Discuss AI access to your data

Start with the questions your team needs answered.

  • How did spend and new paying customers compare by channel?
  • What were orders, revenue, and AOV by market?
  • Which campaign metrics changed compared with the previous period?
  • What do recent application alerts and historical measurements show?

These are starting points for scope. We agree the available sources, history, attribution, and customer definitions behind each question.

Three ways to start

Connect scattered sources

Bring accessible marketing, commerce, subscription, or operational inputs into a defined reporting view. Existing connectors may be enough; add a warehouse when shared definitions, joins, or history require it.

Use an existing warehouse

Prepare the right reporting tables, metric definitions, and source context in BigQuery or your existing data layer, then connect an AI interface to the useful outputs.

Open up existing APIs

Expose selected read tools over application data using the backend’s identity and permissions. The application can remain the source of truth.

From business systems to useful answers

  1. 01

    Connect

    Source platforms, a warehouse, or existing application APIs.

  2. 02

    Define

    Validated reporting data and explicit metric definitions.

  3. 03

    Authorize

    An authenticated connection with scoped read access.

  4. 04

    Explore

    Questions and comparisons in Claude or ChatGPT.

Does this sound familiar?

  • Your team wants to ask questions in AI, but the data is scattered across tools.
  • The warehouse exists, but AI cannot find the right tables or interpret your metrics consistently.
  • Your application has useful data, but it needs an authenticated interface for AI access.

What you receive

  • Agreed questions, metric definitions, sources, and known limitations.
  • A prepared reporting dataset or selected capabilities over existing APIs.
  • An authenticated connection to the chosen AI client, with access enforced by the backend.
  • Answer checks against known SQL, API, or reporting results.
  • Freshness and availability information, setup notes, and a practical handoff.

A focused first delivery

Start with one existing reporting dataset or a small set of accessible sources and a handful of useful questions. Agree the definitions, connect the selected AI client, and compare its answers with known reporting results before expanding.

  • Explore defined marketing metrics from a prepared BigQuery dataset.
  • Use a dashboard and AI access over the same reporting calculations.
  • Ask Claude about selected application records, alerts, or historical measurements through existing APIs.

Access and scope

The relevant dataset or API, example questions and reports, metric definitions, source refresh details, and the account/workspace for the chosen Claude or ChatGPT connection. Available connectors, authentication, and permissions are checked during scoping.

Pricing and delivery timing follow agreed scope and source access. Refresh frequency and historical coverage depend on the systems involved.

When the work needs to go deeper

Connect additional sources, improve warehouse models, add scoped reporting tools, or expose more application capabilities when the questions justify it. Marketing, commerce, subscriptions, and operational data each need clear definitions before they can be combined.

Keep calculations and metric definitions in the reporting or backend layer. AI can request results, compare them, explain them, and suggest follow-up questions. A dashboard and an AI conversation can share those definitions.

Tools chosen around the problem

BigQuery, SQL, reporting APIs, and Model Context Protocol (MCP) provide possible connection paths. MCP lets an AI client request data through defined tools. Existing connectors or managed MCP options are useful where they fit; custom tools serve business definitions and access needs that require more control.

Choose a connection that fits.

An existing connection

Use a supported connector when it provides the required fields, history, and access. Validate the questions it can answer before adding custom infrastructure.

Warehouse access

Use an appropriate BigQuery/MCP option over prepared reporting tables. Add metric-specific tools when shared calculations or bounded query behavior make them useful.

Application read tools

Expose selected backend operations through MCP. Keep account authorization in the server and return the records and context needed for each question.

Check the answers, then hand it over.

  • Compare representative answers with known reporting queries or API responses.
  • Check the period, currency, customer definition, and freshness behind each result.
  • Exercise missing data, ambiguous questions, and access outside the user’s scope.
  • Agree query/data limits and operating costs where relevant, then document setup and handoff.

The reporting period, metric definitions, freshness, and missing-data notes travel with the result. Setup and example questions make the connection practical for the people using it.

Relevant work

Marketing Data & Campaign Analytics Platform →

Designed the BigQuery serving layer behind an MCP integration: current entity state, stable references, and read-time status handling. A colleague built the MCP server transport, authentication, and scaffolding.

AWS Connected-Device Backend & Telemetry →

Implemented authenticated Claude/MCP access to existing application and telemetry APIs, using scoped read tools and the product’s account and access model.

The marketing platform also includes applied AI for creative classification and analytical recommendations. Those workflows depend on modeled inputs, output validation, and a useful reporting layer.

Practical reading

The systems behind AI access

Source integration, warehouse reliability, and backend workflows support the connection. Start with the part your business needs.

Find a useful starting point.

Share the problem and relevant systems. We can identify a manageable first output before agreeing a build.

Discuss AI access to your data