Forward deployed engineering
No specification thrown over a wall, and no private environment the work gets handed over from at the end. We work in your repository, on your accounts, against your real constraints, and we ship something that works every week until your team owns it.
From pilot spend to production economics
A pilot answers one question on one model with nobody watching the meter. Production has to answer thousands, on the right model each time, without your data leaving where it is allowed to be.
The delivery loop
A clear path from an AI bill nobody can explain to a change that is running, measured and owned by your team.
Instrument the workload before touching it: cost per call, latency, quality, and where data crosses a boundary. No baseline, no claim.
A measured baseline you can auditJoin your standup, your tools and your delivery cadence. We are reachable during your working hours, not batched over email.
Named owners, system access and delivery cadenceRelease working increments against your real traffic and actual constraints every week, behind a flag until the numbers hold.
Working changes in the target environmentEvaluations on your own cases run before and after. Cost, quality and safety are compared on the same set, or the change does not ship.
Before-and-after evidence on your own dataHand over the repository, the eval suite, the runbooks and the prioritised backlog to the team that will own it.
Runbooks, training and clear ownershipWhat embedded actually means
Every firm says embedded. The word only means something if you can catch it failing, so here is the version you can check rather than the version you have to take on trust.
What you are choosing between
You are pricing this against a consultancy, an offshore team or a product licence. Rather than pretend otherwise, here is what each one actually does well and where this model is the wrong purchase.
A strategy, a roadmap and a vendor shortlist, produced by a team that will not build any of it.
The same diagnosis, done by the engineers who then implement it, with one change already running before the recommendation is written.
When they win: If what you need really is board-level strategy across a portfolio, a consultancy is the right purchase.
Engineers by the seat at the lowest hourly rate, working to tickets someone on your side has to write and sequence.
A team that writes its own tickets from the metered baseline, owns the outcome, and hands the work back with runbooks.
When they win: This costs more per head. If your bottleneck is genuinely hands and you already have the specs, it is the cheaper answer.
A gateway, router or guardrail product, priced per seat or per token, that you integrate and keep paying for.
The same capability built from open-source or your own licences, in your accounts, with no Zylen software in the path and nothing to renew.
When they win: A good product beats a bespoke build when your needs are standard. We will say so, and integrate it for you instead.
Forward deployed engineering
The label only matters when the delivery model is visible in the work. These are the conditions that must remain true throughout an engagement, and the tests a client can use to hold us to them.
Customer-owned from day oneWhat we need to begin
That is what the first week meters. What we do need is someone who can open doors across departments, because AI spend is rarely all in one place, and a business willing to act on what the numbers say.
Start here
Provider invoices arrive as one number, so most companies cannot say which feature is costing them what, or where their data crosses a boundary. The first week answers both and ranks what is worth changing. The second week changes one of them, so you finish holding a measured result rather than a recommendation.
If nothing is live and measured at the end of day ten, there is no invoice.
Start an AI Optimization AuditStraight answers
It means the engineers who diagnose the problem are the ones who build the fix, working inside your systems rather than from a specification handed over a wall. The term comes from firms that put engineers on site with customers instead of shipping software and leaving support to handle the gap. In practice it is the four things above: our commits are in your repository, we are in your standup, we work on your accounts, and something works every week.
None of the three, and the comparison table above is the honest version of why. A consultancy diagnoses and leaves before building. Staff augmentation builds but does not own the diagnosis. An agency builds in its own environment and delivers at the end. We do the diagnosis, build it in your environment, and design the engagement to end with your team owning it.
Usually not, and it is not what the model depends on. What matters is working hours overlap, access to your actual systems, and presence in the channels where decisions get made. We travel for kickoffs and for the workshops where being in the room genuinely changes the outcome. If your environment requires on-site presence for access or compliance reasons, say so before the audit and we will scope it in.
You own it from the first commit, because the first commit is already in your repository. Ending is a deliverable rather than an event: the transfer stage hands over the repository, the evaluation suite, the runbooks and the ranked backlog, and we train the team that will own it. There is no Zylen software in the path, so nothing stops working when we stop being paid.
Most of the time "embedded" means a Slack channel and a weekly call. The test is whether the buyer can catch the claim failing. Ours are written to be checkable: read the commits, look at who is in the standup, check whose cloud account the workload runs in, and count the weeks with a working increment. If any of those is not true of an engagement, it is not forward deployed and you should say so.
The engagement shape is the same either way, but the audit is written around AI spend and exposure because that is where the measurable problems currently are. If your problem is an operations system with no AI in it at all, that is still work we do — the delivery loop does not change — but the first ten days will look like a scoping and prototype sprint rather than a metering exercise. Tell us which one you have.
Ten days. Fixed price. Inside your systems.
The audit is the shortest version of this model there is: ten days, in your environment, with something measured and running at the end of it. If the way we work does not suit you, you will know inside two weeks rather than two quarters.