Audit
A fixed-scope read of the current architecture and spend, ending in a written list of changes ranked by saving against risk. Useful on its own — several engagements stop here, and that is a legitimate outcome rather than a failed sale.
Cut inference spend without giving back throughput.
We migrate live production AI workloads to lower-cost architectures — while they stay live. The point is not a cheaper bill at the price of a slower product; it is the same product, costing less per request.
- Engagement model
- Fixed audit, sprint, or retainer
- Service area
- United States
- Response time
- Two business days
When this is the right call
The bill grew faster than the usage did.
Inference is now a top-three line item
Spend is material enough to show up in board conversations, and nobody can say precisely which feature or which prompt is responsible for it.
One model does everything
Every call goes to the largest model available, including the classification and extraction work that a far smaller one would answer identically.
Cutting cost is assumed to cost quality
Nobody will touch it because there is no way to prove a change was safe. Without a measurement harness, that caution is correct.
What the engagement covers
Measure, then change, then prove the change held.
Token spend reduction
Attributing spend to the feature, prompt, and call path that causes it, so the expensive 5% is visible instead of averaged away. Prompt and context size are usually where the first cut comes from, and it is the cut nobody has to approve.
Model routing
Sending each class of request to the cheapest model that answers it correctly, with a path back to a larger one when confidence is low. Most production traffic is not the hard case, and does not need to be priced as though it were.
Caching
Exact and semantic caching where the workload repeats, plus provider-side prompt caching where a long stable prefix is being resent on every call. Repeat traffic is usually a larger share than teams expect.
Latency benchmarking
A harness that measures quality and response time before and after, on your traffic, so a change ships on evidence rather than on nerve. This is what makes the rest of the work safe to do at all — and it outlives the engagement.
How it runs
Sprint
We implement the top of that list with your team, starting with the measurement harness so every subsequent change is provable. Work lands in your repository, in your style, reviewed by your people.
Retainer
Ongoing review as traffic, models, and prices move — all three of which change faster than an annual architecture review can track.
Why us
We ran this on our own bill before we ran it on anyone else's.
In-house venture
Imerzn Social™
A multi-tenant SaaS product whose core loop is AI-drafted campaigns from a business's own photo and video assets — a production inference workload we pay for ourselves, which is a materially different kind of attention than advising on someone else's.
social.imerzn.com →Track record
Thirty years of production systems
Lead and architect on tax engines at Amazon spanning global, state, and federal jurisdictions; principal architect across high-throughput payment systems and real-time transaction pipelines. Cost and reliability work, judged on both.
Full track record →Questions we get
Will this make our product worse?
That is the question the measurement harness exists to answer. We build it first, so every change is scored against your traffic before it ships. If a change costs quality, it does not go out.
Do we have to change model provider?
Usually not. Most of the saving in a first pass comes from context size, caching, and routing within a provider's own range. Switching is a tool, not the goal.
Can you work on a live system?
Yes — that is the normal case. Changes go out behind the harness and incrementally, because a workload worth optimizing is a workload nobody can take offline.
What do we keep afterwards?
The attribution, the harness, and the changes themselves — in your repository, run by your team. An engagement that leaves you needing us again has failed.
Tell us what you're spending it on.
Describe the workload and roughly what it costs you a month. We reply within two business days, and we will say plainly if we think there is nothing here worth paying us for.