01
Dissect
Take the workflow apart stage by stage and establish what each step actually produces.
→ workflow map
AI Data pipeline optimization specialist
Same models.
Same models.
A tenth of the bill.
Optimization that starts with dissecting your current workflow and determining what truly needs a model, and what should be handled differently.
The problem
AI in production rarely fails loudly
It gets expensive, then slow, then hard to reason about — usually in that order, and usually without a single incident to point at. If any of the following is familiar, the cause is almost always structural rather than a model choice.
The bill outgrew the usage
Spend climbs faster than traffic. Nobody can say which feature, which stage or which call is responsible, so the only lever left is to use the product less.
Performance is inconsistent
The same request is fast on Tuesday and times out on Thursday. Latency is fine in isolation and falls apart under concurrency, and the difference is invisible from the outside.
Load times are unacceptable
Work runs in sequence that could run in parallel, results are recomputed that were already produced, and every request pays for the whole chain whether it needed it or not.
The method
Data Optimization Framework
Most pipelines route everything through a model because that is how the prototype worked.
dissect → rebuild engagement
02
Classify
Separate what genuinely requires a model.
→ workload split
03
Reassign
Structure data with lightweight compute
→ reduced model surface
04
Rebuild
Restructure what remains — routing, caching, batching and concurrency, against real traces.
→ optimised pipeline
05
Operate
Hand over an orchestration layer that keeps the gains visible and the regressions loud.
→ control plane
Four layers of avoidable spend, resolved down to the work a model was actually needed for
The platform
Savings only count if they survive the next quarter
A one-off optimization decays the moment someone ships a feature. Every engagement ends with an in-house orchestration layer, so the pipeline stays observable and the gains stay measurable after we leave.
Cost you can locate
Spend attributed down to the stage, the feature and the request — not the monthly invoice. When a number moves you can name what moved it, which is the difference between managing a budget and watching one.
Performance that holds under load
Routing, batching and concurrency tuned against real traces rather than a benchmark. The target is not a faster best case — it is a tail that stops moving when traffic does.
Orchestration you own
The control plane runs in your infrastructure, against your models, under your keys. Full visibility across every workflow, and no new vendor sitting between you and your own production traffic.
Who we work with
Anyone whose compute bill has become a product constraint
Product teams shipping AI features
The feature works and users want more of it, but unit economics say otherwise. The goal is to make the thing you already built affordable to run at the scale you already have.
Data & platform engineering
You own the pipeline several teams depend on, and every one of them assumes their workload is the cheap one. Attribution settles that conversation with evidence.
Founders and small teams
No platform team, no observability stack, and a bill that is now a meaningful share of runway. The first audit usually pays for itself before the rebuild starts.
and if you would rather not run it
We can deploy it too
Not every team wants another system to operate. If you would rather hand the whole thing over, we will stand the pipeline up in your cloud, run it, and stay on the pager for it.
Still your infrastructure
Managed does not mean moved. Everything runs in your account, under your keys, against your data. You keep the ability to take it back in-house at any point, because it was never anywhere else.
Or just have a look
If you are only curious what the numbers would say, the audit stands alone. You get the cost map and the waste report whether or not anything follows it.