DataInn Technologies

Data engineering · Master data · Applied AI

The data foundation your AI ambitions need.

DataInn builds governed data platforms and the analytics, models, and AI agents that run on them. One partner from raw sources to decisions you can trust, with teams in Toronto and Dubai.

delivery hubs
2
delivery hubs
Toronto · Dubai
products shipped
3
products shipped
on our own platform
to first pilot
6 wk
to first pilot
typical engagement

Reference architecture

governed end to end

Sources

  • ERP · CRM · EHR
  • Events & IoT
  • Documents & files
  • SaaS & partner APIs

Data foundation

  • Ingestion & pipelines
  • Lakehouse / warehouse
  • Master data & quality
  • Catalog, lineage, access

Activation

  • Analytics & metrics
  • ML models
  • LLM apps & RAG
  • AI agents with human gates
ownershipyour cloud, your repos
qualitytests & contracts in CI
oversighthumans at named gates

Industries served

  • Financial services
  • Healthcare
  • Manufacturing
  • Retail
  • Professional services
  • Public sector

What we do

Two practices, one team.

Most AI programmes stall on data. Most data programmes stall without a use case. We run both practices together so neither waits on the other.

Corporate AI training

Build AI fluency where decisions happen.

Role-based training tailored to your workflows, data, and policies—not generic demos. We help leaders and teams understand AI, apply it responsibly, and turn learning into useful operating capability.

ExecutivesProduct & operationsData & engineeringRisk & governance
Programme 012–3 hours

Executive AI briefing

Align leaders on practical opportunities, responsible adoption, investment choices, and the operating model required to scale.

Programme 021–2 days

Practitioner labs

Build hands-on capability in prompt design, grounded assistants, agent workflows, evaluation, and safe experimentation.

Programme 034–6 weeks

Team enablement programme

Move from learning to adoption through role-based cohorts, use-case labs, governance playbooks, and a coached prototype.

Selected work

Outcomes, not slideware.

A sample of engagements across our two practices. Each one shipped to production and is measured against the numbers below.

All case studies

How we work

Assess, build, operate.

Short, measurable increments. Senior people on the work. Everything in your repositories and your cloud.

Engagement models

Advisory
Strategy, architecture, and vendor selection with senior practitioners.
Delivery squad
A dedicated team that owns an outcome end to end.
Embedded engineers
Senior data and AI engineers inside your team, your tooling.
Managed platform
We operate your data platform and AI workloads to agreed SLAs.
  1. 1

    Assess

    2–3 weeks

    Data estate, use-case inventory, and readiness scoring. You get a prioritised roadmap and a business case, whether or not you continue with us.

  2. 2

    Build

    6–12 weeks per increment

    Cross-functional squad ships production increments: pipelines, models, master data domains, or agents, each with tests, documentation, and owners.

  3. 3

    Operate

    ongoing, optional

    We run what we built alongside your team, or hand it over fully. SLAs, cost governance, evaluation, and continuous improvement.

Insights

Notes from the practice.

All insights

Start with a conversation

Tell us where your data is letting you down.

A 45-minute readiness call with a senior practitioner. We will tell you plainly what to fix first and what it would take.