Proof, not promises

We run our company on the AI we build.

The best proof that we can build secure, dependable AI for you is that we trust it with our own revenue, our own hiring, and our own code. Three examples.

01Our own sales · PipeAI

Our own outbound sales engine

The problem

Reaching the right buyers normally means a ~$50K/year outbound stack — lead data, sequencing, social selling, CRM, and an SDR to run it. Too expensive and too manual for a lean team.

What we built

PipeAI, an end-to-end AI outbound engine: it sources and scores leads, enriches them, runs multi-channel email and LinkedIn outreach, detects and classifies replies, drafts responses with AI, and tracks every deal in a native CRM with AI deal scoring. We run our own pipeline on it.

The result

We replaced the ~$50K/year stack with a system we own and operate for roughly $240/month — our outbound now runs on software we control end to end.

What it means for you

If we can build and run our own revenue engine, we can build the AI system your team runs on.

02Our own staffing desk · TrueNorth

Our own staffing desk

The problem

Our staffing desk ran on a generic ATS and was losing quality control — poorly matched candidates slipped through, and leadership had little visibility into submission quality or throughput.

What we built

TrueNorth, the AI recruitment suite we run our own desk on — 25,000+ candidates migrated, a 5-gate AI quality check on every submission, a role-based hierarchy, and AI-assisted matching.

The result

Every submission now passes a 5-gate quality check, uncontrolled submissions are eliminated, and leadership has real-time visibility into quality and throughput. See how we secure and test it →

What it means for you

We run our own desk on it — and we can build the same rigor into how your operations run.

03Our own dev QA

Our own development QA

The problem

As we shipped more software, manual code review didn't scale and project knowledge walked out the door when developers rotated off.

What we built

An AI operations layer over our own development: it auto-audits every pull request — an AI reviewer reads the full diff, checks it against the acceptance criteria, and posts a verdict as a PR comment — captures architectural knowledge from each audit, and generates handover packages when developers transition.

The result

It's modular — we activate it on client request. When active, every pull request is auto-audited before merge, and clients self-serve project status from a live dashboard in near real-time instead of waiting on manual update emails.

What it means for you

The same automated QA discipline we run on our own code, applied to your build.

Want this kind of rigor
on your side?

Let's talk about the AI system your team could be running on.