Optimization for production AI and software systems
AI delivers capability . Optimization delivers results.
Artemis makes your code, AI systems, and infrastructure faster, cheaper, and more reliable in production — without changing your existing stack.
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/30-min technical call. No sales pitch.
/30-min technical call. No sales pitch.
Selected customers and partners
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Most production systems run at
a fraction of what
they could deliver.
Inference costs outpace revenue. Agents burn tokens on retries. Code paths leave hardware idle. The optimization layer between your model, your code, and your hardware is too large to search by hand. Artemis searches it for you. Every change is validated against your actual workload before it ships.
One platform. Wherever
optimization makes a difference.
Real results from real workloads — inference, agents, latency-critical infrastructure, and beyond.
Lower cost, faster
response, no accuracy loss.
Optimization across model serving, runtime, and hardware. Artemis searches the configuration space too large for manual tuning and validates every change against your real workload.
Throughput on vLLM
accuracy maintained • Qwen3-4B-AWQ Intel XEON CPUs
up to
+
36
%
Agents that finish more
tasks for less spend.
Optimization across agent configurations where the search space is large and interdependent.
Higher task success
29% faster execution
Code agents • ALE-Bench
+
13.6
%
Lower token spend
10× faster than manual
CrewAI multi-agent
-
36
%
Throughput and latency where every microsecond counts.
Code performance optimization
across production codebases — runtime, memory, energy, cost.
across production codebases — runtime, memory, energy, cost.
Runtime speed-up
QuantLib C++ • open-source
quantitative finance library
quantitative finance library
+
32.7
%
Better decisions where
the cost of being wrong compounds.
Multi-objective optimization across planning, scheduling, and resource allocation problems.
Throughput improvement on Vehicle
Routing Solver • Est. multi-million savings
• Higher van utilization
• More orders completed
~
3
%
What Artemis delivers
Optimization is where AI
investment becomes AI return.
Artemis continuously finds and validates the highest-value improvements across your stack, based on your objectives — so you know what's worth shipping, and what isn't.
Repeatable Process
Optimization becomes a repeatable capability, not a one-off effort.
Artemis replaces fragmented, manual optimization with a structured system: define objectives and baselines, explore candidate improvements, and validate every result against real workloads before it ships. The same workflow runs consistently across teams and projects turning optimization into a scalable organizational capability.
Speed at Scale
Move faster by focusing only on what actually delivers results.
Artemis runs multiple experiments in parallel across your stack, identifying and validating which changes will deliver meaningful gains and which won't. Teams get clear, evidence-backed answers in hours, not weeks, so engineering effort is focused where it drives real impact, not wasted on trial and error.
Built-in Governance
Every change is validated, traceable, and ready to defend.
Every candidate is benchmarked against your real workload, every trade-off is made explicit, and every result is validated before deployment. Artemis captures the full path from objective to outcome with complete traceability — so every decision stands up to engineering, leadership, and the business.
How Artemis finds your optimum.
With Artemis, what used to take weeks of manual work can now be done
in hours — with a structured process, accelerated experimentation, and the evidence to make the right call
in hours — with a structured process, accelerated experimentation, and the evidence to make the right call
01 Define
Define what good looks like
Bring your workload. See how the system performs today and define the optimization objectives that matter - throughput, latency, cost, accuracy. This is your baseline.
Real workload
Multi-objective
02 Search
Explore every path. Validate what works.
Artemis searches a configuration space too large for manual tuning. Generate and run optimization candidates in parallel. Promote only the ones that perform.
Thousands of candidates
In parallel
03 Improve
Ship the best result. Keep improving.
Ship the best result. Artemis tracks performance and proposes the next cycle before it degrades. Each cycle compounds - the platform learns your environment, and gets better every time.
Continuous
Learns your environment
Case study · AI Inference
Up to +36% inference throughput. Same model and hardware. Same accuracy.
Artemis optimized vLLM for Qwen3-4B AWQ on Intel XEON CPU, unlocking significantly faster AI inference from the same infrastructure. By applying hardware-aware kernel optimization to the AWQ inference path, Artemis improved throughput and efficiency while preserving model accuracy and passing validation gates.
-26%
Time per output token (75.3 ms -> 55.7ms)
up to +36%
Throughput (380 tok/s -> 517 tok/s)
-26%
Cost per token
Built for teams running
measurable systems in production.
Artemis works wherever performance is measurable in cost, latency, throughput, reliability, or quality.
You'll get the most from Artemis if
Your AI or infrastructure spend is growing faster than the value it delivers
You run code, AI systems, or infrastructure in production where outcomes are measurable in money, time, or risk
You need to optimize multiple objectives at once — cost, latency, and accuracy together — not just one
You've already tried the obvious things — runtime tuning, quantization, hardware upgrades, code refactors — and you're hitting the limit of manual optimization
You're being asked to prove ROI on AI investments and need defensible numbers from real workloads, not benchmarks built in isolation
You probably don't need Artemis (yet) if
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You're pre-product-market-fit and your infrastructure costs aren't material
Your bottleneck is research-stage model quality, not production efficiency
Your optimization problem is small enough to solve with a benchmark script and a few iterations
You're optimizing for a single metric in isolation, with no competing trade-offs to balance
Common questions.
What does Artemis actually optimize?
Any measurable AI and software system. Optimization is fundamentally a data science and engineering problem — global search across large, interdependent configuration spaces with multiple competing objectives. Today, Artemis applies that capability across four domains: AI Inference, Agentic Software, Latency-Critical Infrastructure, and Planning & Scheduling engines.
How is this different from existing optimization tools?
Most optimization tools solve one problem in isolation: an inference serving layer, a kernel autotuner, a scheduler. Each is built for one workload at a time. Artemis is the optimization layer above them. It searches for the best combination of these layers (and others) across your workloads and your competing objectives, then validates every choice against your real system before anything ships.
How is this different from coding agents like Claude Code or Codex?
Coding agents are excellent at writing and refactoring code. But optimization isn't a code-generation problem — it's a data science and engineering problem. Improving a probabilistic, multi-objective system requires experiment design, trade-off management, and the engineering to validate results in production. Most teams have one. Almost none have both. A coding agent can suggest a configuration. Artemis provides a controllable, systematic path to an optimal state — searching across model, runtime, hardware, and code dimensions, validating every step against your real workload.
What does a typical engagement look like?
Days to weeks, not months. Artemis benchmarks your system, searches the configuration space, and returns validated configurations you can deploy.
How do you validate that a change is safe to ship?
Artemis is designed to reach an optimal state with the minimal set of changes. The smaller the diff, the lower the risk. Every candidate is benchmarked against your real workload, tested against functional and security constraints, and tracked through a complete audit trail. 100% of changes are validated before merge.
Do we keep control over our data, code, and models?
Yes. Artemis is built for enterprise control: deploy on-prem, in your private cloud, or in your VPC. It optimizes around your system rather than ingesting it, so your data, code, and model weights never leave your environment. Your IP stays yours.
Discover the ROI
hiding in your stack.
Bring a workload, a system, or a slow pipeline. We'll tell you what Artemis would search and what it's likely to find.
Technical call. No sales pitch.