Week 1 - Workload profiling.
We instrument your AI workloads, measure inference patterns, GPU utilization, latency distributions, and cost per business event (per chart summarized, per claim processed, per agent response).
Your AI Bill Is Outgrowing Your AI ROI. We Fix That.
AI optimization and performance services for healthcare AI workloads - cut inference, GPU, and platform spend by 40–70% without giving up accuracy or compliance posture.
If you're operating production AI in a healthcare environment without dedicated performance engineering, you are almost certainly leaving real money on the table. Here is what we typically find in a teardown.
Cumulatively, mid-market healthcare AI programs typically run 40–70% above their efficient cost profile. At enterprise scale, the gap is measured in seven figures annually.
Every AI optimization engagement we run targets the same six levers. The mix varies by workload; the categories don't.
Model selection and right-sizing. Most production workloads are running models that are bigger than the task requires. The right model at the right precision is usually the single largest cost lever.
Inference architecture. Batching, caching, speculative decoding, distillation, quantization, KV-cache management - the engineering layer that turns a model into a workload.
Infrastructure and GPU economics. Instance family selection, reserved vs. on-demand mix, spot strategy where compliance allows, GPU sharing patterns, regional cost arbitrage.
Foundation model vendor strategy. Negotiated pricing, multi-model fallback, open-weight alternatives where the workload tolerates them.
Data efficiency. Right-sized context, smarter retrieval, deduplication, prompt compression - reducing the tokens that touch the model in the first place.
Operational efficiency. Workload consolidation, shared platform infrastructure, eliminated redundant inference paths across teams.
The teardown is the standard starting point. It is a fixed-scope 4-week engagement that produces a written cost optimization roadmap with quantified savings against each lever above.
We instrument your AI workloads, measure inference patterns, GPU utilization, latency distributions, and cost per business event (per chart summarized, per claim processed, per agent response).
We decompose total AI spend by model, by workload, by team, by environment. Most clients have never seen this view before - and the line items it surfaces become the targets.
For each cost lever in scope, we model the savings, the engineering effort, the risk to accuracy and compliance posture, and the time-to-realize.
A written roadmap with prioritized initiatives, expected savings ranges, accuracy and latency impact, and a sequencing plan. This is the artifact you take to your CFO and CMIO.
After the teardown, most clients move into a 12–24 week implementation engagement. The engineering work is concrete - not consulting.
Compliance posture is preserved throughout. Every optimization is evaluated against HIPAA, BAA, audit, and accuracy constraints before it ships.
For an illustrative mid-market healthcare AI workload at $2.4M annual run-rate, a typical optimization engagement profile:
28–42% inference cost reduction.
Additional 18–26% reduction in residual inference cost.
22–34% reduction in compute cost layer.
8–18% reduction in foundation model and managed service spend.
$900K to $1.6M against the original $2.4M baseline.
Typically 3–6 months.
A generic AI cost optimization practice will find some savings in a healthcare environment. It will leave more on the table than it captures, because healthcare workloads have constraints that change the optimization math.
Logiciel's optimization practice operates inside those constraints by default. We don't ship recommendations that pass FinOps and fail compliance.
AI optimization and performance services are engineering engagements that reduce the cost and latency of production AI workloads while preserving accuracy and compliance posture. The work spans model right-sizing, inference engineering, infrastructure restructuring, vendor strategy, and platform consolidation. For healthcare AI workloads, the engagement is constrained by HIPAA, BAA, and clinical accuracy requirements throughout.
Mid-market healthcare AI programs typically run 40–70% above their efficient cost profile when first profiled. Realized savings in the 12 months after a Logiciel engagement usually land in the 35–55% range against the original baseline, after factoring in implementation effort. The teardown produces your specific quantified business case before any commitment to implementation.
Every optimization is evaluated against a workload-specific evaluation suite tied to the clinical or operational outcome - not against a generic benchmark. Optimizations that produce material accuracy degradation are not shipped. The teardown surfaces the accuracy headroom available against each cost lever before implementation begins.
The teardown is a fixed 4-week engagement. A typical implementation phase runs 12–24 weeks depending on workload count and complexity. Most clients see realized cost reduction in the cloud bill within 60–90 days of starting implementation. Continuous FinOps runs ongoing, sized to the AI portfolio.
Yes. Logiciel's optimization practice is vendor-neutral. We work across AWS Bedrock, Azure OpenAI, GCP Vertex, Anthropic, OpenAI, open-weight models (Llama, Mistral, Qwen), and self-hosted patterns. We optimize against the platform and vendor mix you've already chosen - and recommend changes only where the savings justify the migration cost.
The 4-week teardown is a fixed-price engagement. The implementation phase is scoped against the prioritized initiatives in the roadmap. We size engagements so that the realized first-year savings cover the engagement cost 3–6x over for typical mid-market healthcare AI programs. The teardown produces your specific numbers.
Yes. The teardown produces the cost and utilization data your procurement team needs to renegotiate contracts - specifically, evidence of consumption patterns, alternative vendor benchmarks, and accuracy-equivalent fallback options. We provide the data; your procurement team runs the negotiation.
Use the AI Savings Calculator to model your inference, GPU, and vendor spend against the six optimization levers. If the savings look meaningful, book the teardown. If they don't, we'll tell you.