AI Operations & Optimization

Audit, optimize, and monitor your AI in production.

It starts with a 2-week audit of your current system. From there, an optional retainer keeps your AI getting cheaper, sharper, and faster every sprint.

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What unmanaged AI actually costs you

Production AI doesn’t stay good on its own. Five things that quietly break after launch — and what each one costs your business.

01

Behavioral & Model Drift

AI outputs degrade as models age and your data evolves. What worked at launch starts to hallucinate, frustrating users and quietly burning the trust you spent months earning.

02

Revenue & Margin Leakage

Your AI bill grows faster than your business does. Every new user adds tokens, and unoptimized prompts mean you're paying retail for compute you don't need.

03

Data & Compliance Fragility

Unmonitored systems leak PII into prompts, logs, and third-party APIs without you knowing. By the time legal finds out, your sensitive data is already sitting on infrastructure you don't own.

04

Technical Stagnation

The AI landscape moves weekly. Static systems can't swap in faster or cheaper models — so your business runs on yesterday's tech at last year's prices.

05

Institutional Blind Spots

Without logs and analytics, you can't see why your AI fails certain users — or even know it's failing. You're running a black box and finding out about problems through support tickets.

What managed AI gives you back

Five things we put back in your control once your AI is under active management.

Cost Compression

We cut token usage, tune prompts, and route queries to the right model for the job — so your AI bill stops scaling linearly with your success.

Drift Control

We benchmark every prompt, model, and config change against an eval set built from your real traffic. When quality slips, we catch it before your users do.

Always Current

New models ship every month. We test them against your workloads and migrate when the data justifies it — so your system runs on the best price-performance available, not last year's defaults.

Full Visibility

We instrument every interaction so you can see exactly which workflows, queries, and users your AI is winning with — and which ones are quietly burning money.

Compliance Hardening

We audit prompts, logs, and third-party integrations for PII leaks, then build the redaction, retention, and access controls your security team needs to sign off — before the next audit, not after.

How the engagement works

A two-week audit to find what's broken. An optional retainer to keep it working as your system scales.

Entry point — fixed scope, fixed price

01

Performance Audit

Two weeks. We pull your logs, traces, and cost dashboards, and hand back a prioritized list of fixes ranked by impact and effort. You decide what to do next.

Optional retainer — ongoing

02

Targeted Fixes

We ship the highest-impact fixes from the audit — prompt compression, smart caching, model routing, eval sets to catch regressions. Most clients see meaningful cost or quality gains in the first 30 days.

03

Compounding Cycles

Optional retainer. Every 2 weeks: audit a slice of production, ship a focused improvement, measure the delta in cost, quality, and latency. Each cycle ties to a business KPI — gains compound, not vanish.

AI Operations Questions

Answers to the questions teams ask before they hand us the keys to their AI infrastructure.

We start with your logs, traces, and cost dashboards. The audit identifies where tokens are wasted, where the model is hallucinating or off-brand, where latency spikes hurt UX, and where compliance is at risk. You get a prioritized list of fixes ranked by impact and effort within two weeks.
Most teams running on public APIs without tuning are paying 30–60% more than they need to. Through prompt compression, smart caching, model routing, and retrieval tuning, we typically cut spend by 40% while improving response quality.
Read-only access to your logs, traces, prompts, and cost dashboards is enough to start. We sign NDAs and DPAs before any access is granted, and for sensitive workloads we work entirely inside your environment — your data never leaves your perimeter. We don't need access to your production keys or write permissions to deliver the audit.
We work with what you have. Whether you’re on OpenAI, Anthropic, Bedrock, or self-hosted, we tune the system in place. We only recommend swaps when the data justifies it — usually for cost or latency reasons, never to chase a logo.
We build an eval set from your own production traffic — real prompts, real edge cases, real failure modes — then benchmark every prompt, model, and config change against it. When quality slips, we catch it before your users do, and roll back automatically if needed.
2-week cycles: audit a slice of production, ship a focused improvement, measure the delta in cost / quality / latency, and report. Each cycle is tied to a business KPI so you can see ROI in real numbers, not vibes.
The 2-week audit is a fixed fee with a fixed deliverable — you know the total cost before kickoff and you keep the findings whether you continue with us or not. The optional retainer is a monthly engagement priced by scope and cycle volume. We share concrete numbers on the discovery call once we understand your stack and the scale of the work.
Most clients see measurable gains in the first 30 days — usually a 20–40% cost reduction on the highest-spend pipelines and a noticeable accuracy bump on top user flows. The compounding gains continue every cycle after that.
You keep everything. The audit deliverable, the prioritized fix list, the eval set we built, the runbooks — all of it is yours to hand to your internal team. The retainer is optional precisely because the audit has to stand on its own. If we haven't earned the next cycle, we haven't earned it.

Stop bleeding tokens. Start measuring outcomes.

One conversation to audit the system you have, identify the leaks, and build the optimization cycle that compounds every two weeks.