AI is only as good as the data beneath it.
We build both.

An enterprise AI consulting and R&D firm. We design custom AI systems connected to your data, your tools, and your workflows — then transfer ownership: the code, the infrastructure, and the knowledge to run it.

01 / Owned infrastructure

Anthropic, OpenAI, Mistral, local, or private. The architecture belongs to you, not to a rented black box.

02 / Production agents

Agents that execute workflows, call tools, keep state, request human approval, and leave traces you can audit.

03 / Evals in the loop

We test every prompt, model, workflow, and retrieval change against speed, cost, memory, and quality thresholds.

Connects to the tools you already use
Gmail
Slack
Notion
Claude
GitHub
Google Drive
Stripe
Teams
HubSpot
ChatGPT
PostgreSQL
Outlook
Shopify
Linear
Google Calendar
Zendesk
Snowflake
OneDrive
Jira
Gemini
Zapier
Confluence
Airtable
MCP
Mistral AI
Google Sheets
Databricks
Intercom
Dropbox
MongoDB

Plus dozens more through MCP — one open protocol, every tool. See how MCP works

What we do

Custom AI systems, connected to your enterprise.

Group e-media is a consulting and R&D firm. We design and build AI systems around your actual operation — then hand them over, with the knowledge to run them.

01

Build

Custom AI systems designed for your operation: assistants, agents, and workflows, with the data layer underneath them built right.

02

Connect

Wired into the systems you already run — databases, documents, CRM, email, ticketing — through governed tool connections (MCP) with real permissions.

03

Transfer

You own the result: code, infrastructure, prompts, eval sets, and traces. We pair with your team until they run it without us.

How it works

From first conversation to owned system.

The same motion every time, sized to the problem. No black boxes at any step.

  1. 01

    Map

    We inventory your data, your systems, and your candidate workflows, then pick the first one worth automating. You get a roadmap either way.

  2. 02

    Build

    The studio components assemble the system: data catalog, model gateway, agent runtime, governed tools, retrieval. Custom where it matters, proven components everywhere else.

  3. 03

    Prove

    Evals gate every change and human approval sits on risky actions. We launch when the numbers pass — and every action leaves a trace you can audit.

  4. 04

    Own

    We transfer the system — code, infrastructure, prompts, eval sets — and pair with your engineers until they run and evolve it without us.

Why ownership

Why owning your AI beats renting it.

Subscription AI tools are fine until they become the operation. Once AI runs your workflows and touches your data, ownership is the safer position.

Model-agnostic

OpenAI, Anthropic, Google, or open-weight models behind one gateway. When a better or cheaper model ships, switching is a routing change, not a rewrite.

Your data stays yours

Knowledge bases, embeddings, memory, and traces live in your infrastructure under your access rules — not in a vendor's account.

Cost you control

Infrastructure cost instead of per-seat licenses. Prompt caching, batch inference, and model routing keep spend attributable and tunable.

Auditable by default

Every model call, tool action, and approval is traced. Compliance and security reviews read from your own records.

No platform risk

A vendor pivot, price change, or shutdown doesn't strand the operation. The system is yours to run and evolve.

A compounding asset

Eval sets, knowledge, and workflow patterns accumulate in your stack. Each project makes the next one faster and cheaper.

In production

One owned foundation. Different industries. In production.

The same stack — data foundations, agent runtimes, evals, and closed loops — runs across unrelated industries in production. We build the foundation once; every deployment makes it sharper.

Multiple industriesOne owned foundationModel-agnostic · self-hostable
Read the engineering case studies
Our history · 2003 → 2025 · Twenty-two years

From frameworks to cloud, to data, to AI.

Each era demanded a different substrate — frameworks, cloud, data, agents. We stayed close to the metal and kept shipping systems that had to work after launch.

  1. 01
    2003
    Frameworks
  2. 02
    2010
    Mobile + commerce
  3. 03
    2015
    Cloud + modern web
  4. 04
    2018
    Data became the product
  5. 05
    2023
    Agents + LLMs
  6. 06
    2025
    Enterprise AI · now
Read the full history