A recorded baseline
Cost, latency and task quality captured on the current production path before a change is proposed.
AI optimization services
Optimize the AI you already run.
One operating model for inference cost, AI security and private deployment. We begin with your actual traffic and bills, make one change inside your environment, and leave the before-and-after on record.
What we deploy
Four capabilities. One embedded team.
We reduce AI cost, strengthen security and keep sensitive workloads under your control. Forward-deployed engineering is how we deliver those outcomes inside your systems.
Not sure which of these you need? That is what the audit is for →
How we prove the work
No borrowed benchmarks. Your baseline is the evidence.
Zylen is building its first published AI optimization case studies. Until a customer permits a measured result to be shared, we will not invent one or relabel earlier software projects as AI savings work. Every engagement creates the evidence needed for a defensible result.
One variable at a time
Routing, caching, prompt, guardrail or hosting changes released against written acceptance criteria.
The same traffic and quality bar
A before-and-after clients can inspect, reproduce and use to decide whether the next change is worth funding.
Start here
Every AI workload metered. One of them already fixed.
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.
$4,00010 working days · credited in full against a sprint
If nothing is live and measured at the end of day ten, there is no invoice.
Start an AI Optimization Audit AI optimization audit 10 working days
- Days 1–2MeterWe instrument what you already run. Every AI workload, what it costs per call and per month, which model it uses, who is paying for it, and the spend nobody has attributed to a team yet.
- Days 3–4RankEach workload scored on what it costs, what it would take to make cheaper, and how exposed it is — including an assessment against the OWASP GenAI LLM Top 10 2026 and a note of where your data leaves your network.
- Day 5ChooseYou and your sponsor pick the first target. We tell you which one we would pick, and which ones we would leave alone because the saving does not justify the change.
- Days 6–9ImplementWe build one of them for real, in your environment: a routing and caching layer, a redaction gateway, or one workload moved to a self-hosted open-weight model.
- Day 10Hand overThe measured before-and-after on your own numbers, the ranked backlog of everything we did not touch, and a fixed-price sprint quote with named owners and acceptance criteria.
How we deliver
Built inside your systems. Owned by your team.
We measure before changing anything, work in your repository and cloud, prove every improvement against your real traffic, and hand over the code, evidence and runbooks.
- We measure before we change anything
- Nothing is optimised until it is instrumented. Cost per call, latency and quality are on record before the first change, so the improvement is a comparison and not an assertion.
- We work in your systems
- Your repository, your cloud, your accounts. Nothing is built in a private environment of ours and handed over at the end.
- Savings are metered, not modelled
- The number we report is the one on your provider invoice after the change, on the same traffic. We do not ship a spreadsheet projection and call it a result.
- No fix ships without an eval
- Every routing, caching or model change is compared against the current setup on your own cases. If quality drops on the cases that matter, the change does not go out.
- We hand it over and leave
- Code, IP, runbooks and system knowledge transfer to your team. The engagement is designed to end.
Straight answers
The questions buyers actually ask.
AI optimization is the measured improvement of a production AI system across cost, quality, latency, security and data boundaries. Zylen starts from your traffic, invoices and risk constraints rather than a generic model benchmark.
Days one to five map workloads, provider spend, quality thresholds and exposed request paths. Days six to ten implement one agreed production change and record the before-and-after result, open risks and next priorities.
No. The audit may recommend keeping the current model, routing selected requests elsewhere, adding caching or guardrails, or moving a sensitive workload into your cloud. A provider change is made only when your evidence supports it.
Cost. Security. Sovereignty. One measured starting point.
Find the first AI change worth making.
The audit maps what you run, what it costs and where it is exposed. You finish with one change live, a measured result and a ranked plan you can choose to fund or keep.