Argmin AI

Argmin AI is a system-level LLM cost optimization platform for teams running LLM-powered products, pipelines, agents, and RAG. It reduces inference spend by up to 10x without destabilizing quality by optimizing the whole stack together: prompt and context efficiency, model selection and routing policies, RAG retrieval, and agent workflow overhead.

What sets Argmin AI apart is how quality stays aligned throughout the process. Rather than applying generic benchmarks, the platform builds a tailored evaluation system - a multi-agent LLM-as-a-judge - grounded in your quality definition, your data, and your domain expertise. The user stays in the loop at every step, reviewing results, providing feedback, and watching that feedback propagate through the system.

THE ITERATIVE OPTIMIZATION LOOP

The core of the platform is a continuous optimization cycle we call The Loop:

1. Build / Tune Evaluation - Construct and refine a multi-agent LLM-as-a-judge evaluation system calibrated to the user's quality criteria and domain. 2. Build Optimized Agent - Apply optimization techniques (prompt compression, model routing, caching, architectural changes) guided by evaluation scores. 3. User Reviews Results - Present the optimized agent's outputs alongside the original, with full judge breakdowns, so the user can see exactly what changed and why. 4. Collect & Propagate Feedback - Gather expert feedback on specific cases and propagate it to similar cases across the dataset, then feed it back into the evaluation system.

Each pass through the loop produces a better agent and a more accurate evaluation system. The evaluation system doesn't end with optimization - it continues working for monitoring, regression testing of new features, and benchmarking after updates.

BUILDING TRUST

Today, 70% of AI agents are updated or deployed in production only after human assessment. Users need to see how an agent was built, how it was evaluated, and what came out the other end. There will always be a human or expert in the loop.

Argmin AI builds trust on two axes:

Trust in the process. The user participates in building the evaluation system, provides feedback on specific cases, and observes how that feedback changes judge behavior and agent answers. Nothing happens behind a black box.

Trust in the results. The user sees how the new agent handles their data, reviews quality scores and change analysis, and understands what improved, what didn't, and why. Before-and-after evidence is presented at the case level with full judge rationale.

JUDGE BUILDING

The evaluation system is built from three inputs:

- User's data and tasks - real conversations, queries, or workflows the agent handles. - Evaluation criteria - what "good" looks like, defined collaboratively with the user. - Domain knowledge - relevant reference materials (e.g., clinical practice guidelines, regulatory protocols, internal standards).

From these, the platform defines evaluation personas - specialized judges, each with a clear role. For example, in a mental health use case: a Clinical Psychologist persona reviewing therapeutic quality, a Crisis Counselor persona acting as a safety guardrail. Each persona gets explicit scoring criteria and strong examples for in-context learning.

The result is a multi-agent LLM-as-a-judge system where each judge evaluates a specific quality dimension. In our mental health case study, 9 judges covered dimensions including professional boundaries, empathy, clinical safety, and actionability - achieving 97.6% clinical safety while reducing costs by 87%.

EXPERT-IN-THE-LOOP FEEDBACK

Optimization at scale means dealing with thousands of data points. The platform provides four mechanisms to make expert review efficient and high-leverage:

Tags. Cases are grouped by type and theme through clustering, domain knowledge, and user input. Instead of reviewing 1,000 individual cases, the user works with ~70 representative groups.

User Prioritization. The user specifies which case types matter most - critical, edge, important, or other. Critical cases (e.g., active self-harm disclosure, minors disclosing abuse) never slip through without review.

Insights Prioritization. The platform surfaces cases where ambiguity is highest - where feedback provides the maximum new information. Minimum effort, maximum signal.

Signal Enhancement. One piece of feedback propagates across every similar case. A confidence threshold controls how far the signal spreads. The user reviews the propagation and adjusts.

The feedback interface presents a split view: the original conversation on the left, before-and-after agent answers in the middle, and feedback controls on the right. Each judge's score change is shown with its reasoning. The user can approve, flag, or skip the overall result, and for each judge individually indicate "agree," "too strict," "too lenient," or "wrong criteria." This judge-level feedback tunes the evaluation system for the next iteration.

OPTIMIZATION TECHNIQUES

With the evaluation system in place, the platform applies optimization across the full inference stack:

- Prompt and context compression - Retain answer quality while compressing LLM input by 2-10x. - Model selection and routing - Match the right model to each request based on complexity and risk. Match GPT-4 performance with up to 98% cost reduction on suitable traffic. - Semantic caching - Avoid redundant computation for similar queries. - RAG optimization - Smarter retrieval yields 5-10 accuracy points improvement with 3-5x fewer tokens. - Agent workflow refactoring - Reduce unnecessary LLM calls in multi-step orchestration.

The platform doesn't force a single approach. It finds the Pareto-optimal setup for each use case - the combination of techniques that maximizes cost savings at the user's acceptable quality threshold.

WHAT THE USER GETS

At the end of the engagement, the user receives:

1. An optimized agent - with measurable cost reduction and quality metrics. 2. Quality guarantees - backed by the evaluation system with before-and-after evidence. 3. A production-ready evaluation system - that continues working for ongoing monitoring, regression testing, and benchmarking after updates.

VALIDATED RESULTS

Internal case study on mental health conversational AI:

- Cost reduction: 87% ($9,380 -> $1,180 per 1M responses) - Quality preserved: only 3.3% degradation - Clinical safety maintained: 97.6% - Evaluation system: 9-judge LLM-as-a-Judge validation - Stress testing: 400-item edge-case test suite

HOW IT WORKS (ENGAGEMENT MODEL)

1. Validate - Use the cost calculator to estimate savings potential for your use case. 2. Set Up - Connect your agent and data. NDA available at this stage. 3. Specification - Define your goals, priorities, and what matters most. 4. Free Audit - See the budget, plan, and next steps before optimization starts. 5. Control - Review the generated evaluation system. You stay in control. 6. Optimization - Get an optimized agent with several options tailored to your use case.

No retraining. No vendor lock-in. No risky rewrites. Works with proprietary and open-source models across providers.