Turn your software services company into an AI software factory.
Increase project capacity, shorten delivery cycles and protect margins without increasing engineering headcount at the same rate.
What scales when you win more work?
That single question separates the two models.
The old model
More projects
Capacity is a hiring decision. Margin is whatever survives the gap between the bid and the bench.
The factory model
More projects
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Capacity is a throughput decision. Your seniors supervise execution instead of producing all of it.
The pipeline
From client requirement to client delivery.
The same path every engagement takes, so the trail looks the same on every project you hand back.
Economics
Your clients already know what AI does to the cost of code.
They will expect that efficiency in your timelines and your pricing, whether or not your delivery model is ready for it. Giving each developer a coding agent speeds up individual tasks, but you still coordinate requirements, teams, QA, reviews and releases the way you always have — so the saving never reaches your cost structure.
LFG changes the unit of production. That is what makes room to bid competitively without giving away the margin.
Delivery capacity
Take on more concurrent client projects without growing the bench in proportion. Capacity stops being a hiring decision.
Margin
Reduce the engineering labour a project requires, so AI efficiency lands in your cost structure rather than only in your quote.
Senior leverage
Experienced engineers supervise several streams of execution instead of hand-writing each implementation.
We are not going to put a modelled margin chart on this page. The honest version is that the effect depends on your stack, your clients and how much of your delivery is coordination rather than construction — which is exactly what a pilot measures. Run one project through it and use your own numbers.
Straight answers
The objections we actually get.
My customers already expect an AI discount.
They do, and that pressure is not going away. The question is whether the efficiency shows up in your cost structure as well as your pricing. If you pass on lower billable hours without changing how delivery works, you absorb the whole compression yourself. The point of the factory is to change what a project costs you to deliver, so you have something to give.
My developers already use Claude Code. Why do I need this?
Because a coding agent makes an individual developer faster at an individual task. It does not scope the project, hold the architecture, sequence the dependencies, run the regression pass, or keep the trail your client will ask for. Today your developers do that coordination by hand, which quietly makes each of them the bottleneck. LFG moves that work into the pipeline and runs the same agents inside it.
I cannot let agents loose on a client repository.
Nor would we. Work happens on branches in isolated environments, never against a client's main line. Nothing merges without a person approving it, and every change traces back to the ticket and requirement that produced it. The controls are the reason this is sellable to your clients, not an afterthought.
Our developers understand the client's systems better than any AI.
Agreed, and that is the point. The factory is not a substitute for domain knowledge — it is a way to package it. Architecture decisions, conventions, the awkward parts of a client's stack and the corrections you have already made become context the pipeline carries into the next ticket, instead of living in one engineer's head.
My clients care about accountability.
So the audit trail matters more than the speed. Requirements, tickets, diffs, test results and approvals all stay traceable, and a named engineer signs off on every release. When a client asks why something was built the way it was, there is an answer with a date on it.
Will this replace my developers?
No, and we would not sell it that way. Your senior engineers become the control layer for a great deal more software execution than they could write themselves. The work that disappears is the coordination overhead — context-feeding, retry-babysitting, first-pass review — not the judgment.
Fit
Shaped around how your firm already works.
We configure the product to the client rather than handing over something generic and expecting you to adapt to it. No migration, no rebuild of your process — it attaches to the repositories and the way of working you have today.
Configured to your process
Your standards, your definition of done, your review rules and the shape of the work you deliver. We tune the product per client, not per plan tier.
Your repositories
Branches and pull requests land in the GitHub or GitLab repo the client already owns.
Your engineers
They hold the review gate and the architecture calls, which is where their judgment pays.
Your model policy
Some clients will not allow certain vendors near their code. Set the policy per project.
Your infrastructure
Run it in your own environment when a client's requirements demand it. The core is MIT.
The offer
Run one project through LFG.
Pick a real project or backlog item — ideally one you have already scoped and priced the traditional way. We put it through the pipeline, and you compare it against your own baseline:
That makes the decision empirical instead of a matter of believing our marketing.