We are the engineering layer of your company.

Why we started this

In the process of building automations and data warehouses for companies, two things became clear about the present moment:

1. AI has lowered the cost of automation dramatically. Workflows that needed an engineer to write, test, and maintain custom code used to take weeks. Now an AI engineer pair-coding alongside a human can ship and maintain them in a fraction of the time. The economics of "we should automate this" tasks changed entirely.
2. The same shift makes data warehouses much easier to build and run. What used to need a dedicated data team can now be a one- or two-person job with AI assistance — provided the AI has the right context.

Working across both kinds of projects, the deeper pattern became obvious: they need the same knowledge. What SKUs the company sells. Which sales channels matter. Which dashboards are canonical and which are abandoned. What the data quirks are. The bottleneck wasn't AI capability — it was giving AI the right context to be useful for this specific business.

So we built a single persistent, searchable knowledge base that an AI agent can pull from whenever it's asked to ship engineering work or answer an analytical question. Same agent serves engineers, analysts, and operators inside the company. The knowledge compounds week over week.

We implemented this pattern at a US-headquartered DTC consumer brand and observed roughly a 3× rise in the speed at which code shipped, analyses ran, and business questions got answered.

What we take on for clients

We become the engineering side of your company. Specifically:

• Internal automations that save your team time on repetitive work.
• The engineering side of your analytics — pipelines, dashboards, alerts — so KPIs are one click away and no one is running Excels on Monday mornings.
• Surrounding integrations, internal tools, and pipelines that knit your stack together.

The same knowledge base also makes your existing people more productive — engineers designing new processes, analysts running ad-hoc questions, operators making decisions. It is built once and pays compounding interest.

Recent shipped work

A few production systems we've shipped — the pattern at work in different contexts. Full case studies.

• US-headquartered DTC consumer brand

  Implemented the agent + knowledge-base pattern end-to-end. Roughly 3× faster code shipping, analyses, and business-question turnaround.

• Nao Medical — production AI cold-email engine

  6,000 personalised emails per month at $80/month total cost. ~95% cheaper than comparable SaaS.

• Qlub — restaurant ops automation suite

  Three production systems on near-zero infra cost: automated credit scoring, real-time restaurant ops via Google Maps API, WhatsApp + Sheets referral pipeline.

• Word → webpage publishing pipeline

  77 web pages auto-generated from content docs. 90% reduction in publishing time for a content-heavy operation.

How we work with you

Taking over the engineering layer of a company is impossible without knowing the company well — so we are built for engagement, not async-only delivery. We'll join your weekly ops syncs if useful, meet stakeholders, sit in on the conversations where decisions actually get made. The first six weeks especially are about depth: the agent only gets smart about your business because we get smart about it first.

Day-to-day, Slack is the default for asks and updates. For anything strategic or context-heavy, we show up live.

Where we work from

Headquartered in the UAE. Working with clients in the UAE, US, UK, Australia, Canada, and the wider GCC.