A managed engineering engagement that audits your cloud LLM workloads, routes them across the cheapest model that holds your quality bar, and writes the savings into a contract before any work begins. Free audit up front. Savings-share pricing on the back end.
Calculate Your SavingsBoutique engineering up front, automated drift insurance long term. Profile C is your bus-factor answer.
Most "AI cost optimization" tools are dashboards. NoCode is engineers. Here is what that actually means in practice.
The hosts on the NotebookLM critique cycle kept conflating "managed engagement" with "consulting hourly billing trap." This is the structural difference: scope is fixed in writing, pricing is anchored to your real savings, and you walk away with four artifacts you own forever.
Your gold-standard responses + tonality + format rules, locked at audit. The contractual definition of "same quality."
Human-readable YAML. Workload-to-model mapping. Threshold-to-tier rules. Yours forever.
Docker Compose, open-standard weights, OpenAI-schema endpoints. Regenerated daily, verified weekly.
Per-workload confidence trend, alert history, SLA breach log. Auto-credit reads from this.
Real demos. No slides. No hand-waving.
Multi-Agent Swarm
Style, logic, and security agents review your codebase in parallel. 900× faster than a senior dev team. Runs locally — your code never leaves your machine.
API Cost Audit
Feed it your API logs. Get back exactly which tasks are burning money and how much you'd save by moving them to local inference.
You're paying frontier prices for tasks that don't need frontier models.
We analyze your AI workloads, optimize them for local execution, and deliver a turnkey solution. Your tasks run on your hardware, forever, at dramatically lower cost.
"Up to 95%" is real for basic workloads. It is not the number you take to your board. This is.
Blended average is the right frame, but your own number is driven by your portfolio shape. Self-identify honestly before the audit. The audit is there to confirm the number, not to invent it.
Roughly 90% of traffic is routing, ticket classification, sentiment, FAQ retrieval. Little multi-step reasoning.
Mix of routing, structured extraction, summarization, and a minority of genuine reasoning. The profile most enterprises actually have.
Pharma, legal, biotech, and novel-analysis buyers do not buy NoCode for the savings. They buy auto-credit SLA enforcement, the customer-defined rubric, and pre-prod drift simulation. The math: contractual quality guarantees on outputs that lawyers and regulators actually have to defend. The cost reduction is a bonus, not the headline.
Footnote: typical blended savings 15-20% on this profile, since most traffic stays on frontier APIs by design. The drift dashboard, the rubric scorer, and the auto-credit SLA do the heavy lifting.
"It is a nutritional label for AI infrastructure. Find your profile, see the honest range, then let the audit confirm the number."
A legal-tech firm seeing 20% blended is a victory, not a disappointment. A support-heavy shop seeing 70% is the rule, not an outlier. Matching expectation to profile before the audit is what turns a savings pitch into a defensible board number.
The top of the spectrum is not theoretical. Companies are already making the switch.
A major e-commerce platform migrated their data extraction pipeline to optimized local inference. 75x reduction, same extraction quality, real public-record numbers. This is the upper end of the 80-90% extraction tier. Blend with their complex workloads and the total bill reduction lands in the 40-50% range - which is the number most enterprises end up with.
Set your monthly spend, then drag the sliders to match your real workload mix. The calculator runs the weighted blended math live.
Live blended math. Each archetype carries its own typical savings band: Support ~65-75%, Balanced ~40-50%, Deep Research ~15-20%. The result above is your weighted blended savings - the only number a CFO can defend to a board. Move the sliders to match your real distribution and the math updates live.
Sliders not summing to 100%? The calculator auto-normalizes to your declared shape. The presets give you the three canonical archetype profiles. The audit pins your exact mix in writing.
Illustrative example based on frontier pricing as of April 18, 2026: Claude Opus 4.7 ($5 / $25 per M tokens), GPT-5.4 ($2.50 / $15 per M), Gemini 3.1 Pro ($2 / $12 per M). Per-archetype savings bands sourced from the Savings Spectrum. Your actual savings depend on your specific workload mix. If you're not a fit, we'll tell you, and you keep your money.
Four steps from overspending to optimized. Full methodology + migration anatomy →
We analyze your LLM API usage. Which tasks, what volume, what you're spending per task.
Our proprietary engine tunes your workloads so local models match your current quality benchmarks.
We deliver a packaged solution. Runs on your hardware. No external dependencies.
Your API bill drops. Your data stays local. Continuous Calibration keeps quality locked to the confidence threshold set at audit time. Drift is measured. Rollback is automatic. See the SLA framework →
Procurement-grade audit evidence, not vibes. You keep the deliverable, regardless of whether you hire us.
"The roadmap isn't our product. Our ability to execute it instantly is our product."
We replace the vulnerability of being a small team with the lethal advantage of being incredibly fast. The audit is the roadmap. The execution is why customers stop trying to DIY.
Most service businesses want your money regardless of whether they can deliver. We don't.
When we say no, you keep your money. When we say yes, you get a clear ROI number in writing before you commit a cent. That's the deal.
Engineering-leader, CISO, or CFO?
Methodology + model lineage → · Calibration + volume-spike SLA → · Trust + security → · Portability + off-boarding → · CFO playbook →
Start a free audit via encrypted chat. No calls, no scheduling. Just results.
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