We rebuilt our own operations around AI first, built a product out of it, and run the same playbook for clients. One measure of success: what reaches production.
Ends in a working prototype on your own data — aimed where your data and domain knowledge give you an edge nobody can copy.
A structured review of how work moves from ticket to production — where AI actually helps, and what's blocking the gains.
The same scan applies from a 30-person back office to an enterprise IT organization. First candidates by area:
AI does the repetitive production, a person keeps the decision — everything runs with an approval gate. This is a sample; the full checklist is part of the first conversation.
We ran this transformation on ourselves first. Our own operations — back office included — run on the playbook we sell. Ask us to show you.
The old consulting model — weeks of interviews, workshops and waiting for a deck — is exactly what AI made obsolete.
Docs, tickets, templates, exports, logs — AI digests in days what interview rounds used to surface over weeks.
Short conversations confirm what the scan found, instead of discovering it from scratch.
Findings come from your own systems — the speed of the first step is a live demo of how we work.
Both first steps end in proof and a roadmap. From there, the same team takes it through a production pilot to scaled, governed delivery.
Our own platform — visible, testable proof that the method works. Adoption is part of the path: early wins spread across teams, your people get trained, and the process keeps working after we step back.
Answer client security questionnaires with a report, not a promise.
Build your AI SDLC your way — keep your tools, agents and engineering culture. We prove that what ships matches the spec and holds up to an audit. If your clients are banks, insurers or carriers, your AI use will come up in their questionnaires; we make the answer easy.
AI didn't break your code — it broke the chain of trust your auditors verify. Procurement already asks about it; an answer that stands on paper speeds the deal instead of stalling it.
A one-pager you attach to RFPs and security questionnaires — how AI-written code is specified, reviewed and gated.
How your current practice measures against whatever framework your auditors use — evidence, not assurances.
Rework hours and escaped defects, annualized in your own volumes and rates — a number that survives the CFO.
AI sped up coding; review and QA didn't keep up. Catch spec gaps at the pull request.
Auditors will ask how you control AI-written code — we generate the evidence.
A security questionnaire that stalls is a deal that stalls. Answer it with a report.
Bank-partner reviews and compliance scrutiny — prove control of AI-written code.
Carrier and regulator scrutiny — every system change traceable to an approved requirement.
Client SLAs and integration quality — ship faster with fewer defects and less rework.
Bring us the problem. Twenty minutes is enough to tell you honestly which first step fits — or whether you need one at all.
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