Halfcycle fixes that — and helps teams who haven't adopted yet do it properly the first time. We help engineering teams cut development and operating costs by re-engineering how they build with AI: the workflows, the tooling, the review and testing practices, the model and infrastructure choices. AI-augmented engineering, run properly. We work in focused engagements with measurable outcomes, and we tie our fee to results.
Backed by nuCode Tech Capital
Whether you're rolling AI out for the first time or trying to get more out of what you've already adopted, these are the three areas where most mid-size engineering teams are leaving money on the table.
Assisted refactoring, test generation, automated review, and the slower work of stripping manual effort out of the development pipeline. The point isn't adopting more AI tools. It's choosing the right ones for the right places, and building the practices around them so the gains are real.
Model selection, inference cost, caching strategy, infrastructure tuning. Running AI in production has economics. Most teams treat the bill as something that just arrives each month. We treat it as something to engineer.
Ticket flow, handoffs, coordination overhead. This is the part that gets skipped in most "AI transformation" pitches because it doesn't sell tools. It's also where the metrics tend to move.
Every engagement is a pilot first. Three to six months on one or two core applications. We take a measurement at the start, take readings through the middle, and re-measure at the end against the same baseline.
We instrument what's actually happening: dev-month load, cycle time, inference spend, coordination overhead. Most teams have a sense of these but no clean numbers. The baseline ends up being something the team keeps using long after we leave.
AI-augmented workflows, process redesign, infrastructure changes — whichever combination the baseline points to. We work with your team, not parallel to them. The changes have to be ones they'd make on their own once we leave.
We embed with the team for the duration. The deliverable is changes in production, not a strategy deck. The work has to keep working after we're gone.
The same instruments as the baseline, run again against the targets we agreed on. The fee is tied to the outcome. If we didn't move the number, we say so.
The firm is built for engineering teams at three different points in their AI adoption. If one of these sounds like where you are, we should talk.
Two engagement scenarios that show how the work tends to play out. Both are illustrative composites drawn from patterns we've seen across multiple teams, not specific clients. Real client case studies will replace these as engagements complete and clients allow publication.
Illustrative engagement scenario.
A growth-stage SaaS platform was shipping slower each quarter as the codebase compounded, and inference spend on its AI features had started to outrun revenue per user. We worked with the platform team for four months on test generation, caching strategy, and ticket-flow redesign.
Illustrative engagement scenario.
A 1,200-engineer product company had been "modernising" its core platform for two years with not much to show for it. We ran a six-month engagement focused on process re-engineering and embedded delivery on two of the workstreams that had stalled.
Halfcycle is a small firm. The principals are engineering leaders who've built and run organisations at both startup and enterprise scale, shipped product through hard cycles, and lived through the cost and velocity problems we now help other teams work through. The firm is backed by nuCode Tech Capital, a venture studio that put capital, technical guidance, and operating support into Halfcycle from the start. We're set up to take on a small number of clients at a time, and to be accountable for the work.
If any of this lines up with what your team is dealing with, the next step is a thirty-minute call. No deck and no pre-call form. We'll talk through what you're seeing. You'll ask whatever you want to ask about how the firm works. By the end we'll both have a sense of whether it's a fit.