Pay frontier prices only when the answer earns it.
ThermoAgent is a patent-pending AI economic-control layer that selects across leading models, verifies results, escalates when stronger intelligence is needed, and controls premium-model spend — so you optimize cost per acceptable result, not just cost per API call.
Cross-provider routing • verification • escalation • spend control • provenance
The cheapest answer can be the most expensive one.
A low-cost response is not a savings if it fails, gets retried, needs manual review, or eventually has to be rerun through premium compute. ThermoAgent is built around the economics of the completed result.
Routing is only the first decision. ThermoAgent is designed to control the path from prompt to acceptable result — including which model runs, whether the returned answer is good enough, whether stronger compute is worth paying for, and what actually happened.
Protect quality while cutting spend
Use lower-cost models where they are sufficient, verify the returned result, and escalate when the evidence says the cheaper route is not good enough.
Make premium compute justify itself
ThermoAgent's premium-spend control is designed to avoid paying a large model premium when the expected quality gain is too small to justify the cost.
Know what actually served the request
Track requested, selected, and served-model provenance together with cost, latency, tokens, routing state, and recovery behavior.
Keep your existing model providers
ThermoAgent is designed to sit above the AI stack you already use. Bring your existing provider relationships and add an economic-control layer rather than a rip-and-replace migration.
Up to 98.7% lower raw API cost has been observed in maximum-savings testing.
That aggressive configuration also produced a material quality tradeoff. We treat it as an economic boundary test — not a customer promise. The commercial objective is reliable savings inside a defined quality requirement.
Two founders under 25. $0 outside capital. A working AI control engine.
ThermoAgent reached its current technical stage through a small, founder-funded development setup — before an institutional engineering organization or venture-funded operating budget existed.
The core technology was built lean. The next phase is to scale the evidence, product, and distribution.
Today's results were produced before outside funding, a scaled engineering organization, large customer-derived routing datasets, or dedicated production infrastructure. We believe meaningful optimization headroom remains as ThermoAgent gains larger evaluation sets, customer-specific performance data, broader model coverage, production telemetry, and additional engineering resources.
We do not publish a hypothetical improvement percentage. Future gains should earn their way onto this site the same way current results did: through controlled measurement of cost per acceptable result.
Don't take our percentage. Measure yours.
Your workload, providers, prices, quality requirements, and failure costs determine the real ROI. We define the baseline and acceptance criteria first, then measure cost per quality-accepted result.
Physics-Grounded AI Control
Thermodynamic optimization at the core — combined with verification, escalation, learned quality evidence, and auditable execution.
Full mathematical specifications, implementation details, and parameter values are reserved for enterprise evaluators, investors, and technical partners under NDA. What's shown below is the conceptual overview.
A physics-grounded decision engine sits at ThermoAgent's core.
ThermoAgent's patent-pending thermodynamic optimization framework is designed to balance model economics against observed quality evidence. That core works together with classification, verification, escalation, learning, recovery, and premium-spend controls to govern how AI work is executed. We describe the architecture publicly at a high level while reserving the underlying formulation, parameterization, and implementation details for technical diligence under NDA.
Thermodynamic Selection
Patent-pending selection logic combines inference economics with learned and verified quality evidence while keeping the underlying formulation confidential.
Adaptive Routing State
Routing state can adapt to observed performance, cost, latency, and quality evidence instead of treating every request as an isolated one-time choice.
Scale-Normalized Free-Energy Selection
Patent-pending thermodynamic optimization dynamically balances inference economics with verified response quality to determine when premium compute is justified.
Verification, Recovery & Escalation
Failure-aware recovery and quality escalation are built into the execution path so a cheaper first choice does not have to become the final answer.
Execution Telemetry & Provenance
Track cost, latency, token use, requested/selected/served model identity, and routing behavior. Energy and carbon metrics are labeled according to the telemetry or estimation method available.
Multi-Provider Abstraction
Six major providers have been integrated and exercised during development — OpenAI, Anthropic, Google, Groq, Mistral, and xAI — with an abstraction layer designed for additional compatible providers.
Reliable Intelligence Per Dollar
Where we think this category is headed — our thesis, not a benchmark result
Compute is becoming an investable asset
If that shift plays out, how intelligently that compute gets allocated directly affects the return on it. We're building ThermoAgent to be the control layer that decides which model, provider, or compute resource is actually worth using for a given workload — and when premium compute genuinely justifies its cost, rather than being spent by default.
Built to adapt, not stay fixed
The control layer we're building is the base optimizer — but it shouldn't be a static one. It's designed to keep adapting as model cost, quality, and latency shift, and as the behavior of everything else around it changes too.
Autonomous science needs autonomous evaluation
Running experiments around the clock is only half the equation. Once science runs 24/7, the real bottleneck becomes deciding which experiments deserve compute, money, and human attention — and verifying what comes back before anyone acts on it. 10x more discovery needs 10x better evaluation infrastructure behind it, not just more compute.
AI factories still need someone deciding how capacity gets used
If AI factories become productive infrastructure at scale, that makes the control layer more valuable, not less — someone still has to decide how each workload actually uses that capacity. That's what we're building ThermoAgent to do across models and providers today, and broader compute over time: optimizing for quality, cost, latency, and reliability instead of treating every unit of compute the same.
Solutions
Enterprise deployment and government research partnerships
Enterprise Solutions
Deployment built around cost per acceptable result — combining savings controls, verification, escalation, and provenance so lower-cost routing does not have to mean blindly accepting the first answer.
Deployment Options:
- Cloud SaaS (managed service)
- Self-hosted (your infrastructure)
- Hybrid deployment
- Custom integration
Government & Research
SBIR/STTR qualified. Novel R&D in AI evaluation and thermodynamic optimization for defense applications.
Research Focus:
- Black-box AI evaluation (TDAC/LWI)
- Energy-efficient edge computing
- Multi-model consensus verification
- Adversarial resilience testing
Benchmark Evidence
Different tests answer different questions. We label the baseline, quality condition, and scope instead of collapsing unlike results into one savings percentage.
The commercial question
Can ThermoAgent lower the cost of serving a workload while keeping the result inside an agreed quality requirement? That is the metric we want design partners to validate on real production-like traffic.
Maximum-Savings Boundary Test
This test establishes an economic ceiling, not a commercial operating point. We do not present 98.7% as quality parity or as an expected customer savings rate.
Quality-Aware Controlled Results
Results varied across repeated runs, so the best result and the repeated-run average are reported separately. The stable finding across the five-run series was that every run remained inside the preregistered quality tolerance.
All-Frontier Control
The study demonstrated a substantial cost-quality tradeoff, but frontier-quality noninferiority was not established. We therefore do not describe this result as “same frontier quality for 54.5% less.”
Direct Head-to-Head vs. OpenRouter Auto Router
OpenRouter Auto did not escalate on any of the 15 queries in this specific test; ThermoAgent did on the difficult cases where its control logic judged stronger compute necessary. This is a small direct head-to-head, not a universal superiority claim.
V10 Routing Latency — Local and Deployed
We measure the routing engine separately from the surrounding network path so buyers can see where time is actually spent.
| Measurement | Runs | P50 | P90 | P95 | P99 |
|---|---|---|---|---|---|
| AWS local full routing decision | 10,000 | 0.269 ms | 0.297 ms | 0.305 ms | 0.350 ms |
| V10 routing compute inside deployed HTTPS requests | 2,000 | 0.454 ms | 0.572 ms | 0.613 ms | 1.929 ms |
| Public HTTPS round trip (client → TLS/nginx → V10 → client) | 2,000 | 64.129 ms | 71.267 ms | 81.751 ms | 431.943 ms |
ea1a088449593efa47b4173afaa11d1957b42585a5738eac8749398283ab1243.
RouterArena — External Benchmark Workload
This gives ThermoAgent a reference point on a workload we did not design. It is not presented as independent reproduction of every ThermoAgent claim.
Historical Multi-Provider Benchmark — 225 Queries
This early study used a fixed $0.03/query premium-model baseline rather than a simultaneous live premium-control arm. It demonstrated routing economics and execution reliability, but did not establish premium-model quality parity.
How historical savings varied by workload
| Workload | Historical cost-savings range |
|---|---|
| Simple factual / math | 98–99.7% |
| Writing / creative | 97–99.8% |
| Coding / technical | 57–99% |
| Reasoning / logic | 97–99.5% |
| Science / technology | 82–99% |
| Business / finance | 80–99% |
| Analysis / summarization | 88–99% |
| Complex / domain reasoning | 60–91% |
Validation Discipline
Stronger controls have invalidated older headline interpretations and exposed defects that were then fixed. We consider that part of the evidence, not something to hide. Formal independent reproduction remains a next step.
FAQ
How ThermoAgent fits into your AI stack
How much could ThermoAgent reduce my AI inference costs?
Savings depend on workload and model mix. In controlled live testing, ThermoAgent observed up to about 60% lower cost while staying within a preregistered quality tolerance. In a separate isolated control test, the premium-spend savings gate reduced cost 56.7% with no measured quality loss. For a design partner, we benchmark against your actual workload and agreed baseline rather than assume one universal savings percentage.
Why not send every request to the strongest frontier model?
Because not every request needs frontier-level compute. ThermoAgent is designed to reserve premium models for the workloads where their additional capability is justified, while verifying lower-cost responses and escalating when quality demands it.
What makes ThermoAgent different from a normal AI router?
A basic router primarily decides which model receives a request. ThermoAgent is designed as a broader AI economic-control layer: analyze the workload, select a model, verify the returned result, recover or escalate when needed, control premium spend, and preserve provenance for the decision.
What happens when a lower-cost model is not good enough?
That is what the verification and recovery layer is built for. In live testing, ThermoAgent detected a failed lower-cost answer and escalated to stronger compute, producing the largest measured quality rescue in that testing series.
Do we have to replace our existing applications, models, or cloud stack?
No. ThermoAgent is designed to sit between your application and the models or providers you already use. The goal is to improve the economics, reliability, and auditability of your existing AI investment — not force a rip-and-replace.
Can we test ThermoAgent before committing to a larger deployment?
Yes. We are seeking design partners for bounded validation projects that compare ThermoAgent against an agreed baseline on production-like workloads. Cost, quality, and operational success criteria are defined before the evaluation so the result can support a real deployment decision.
How does ThermoAgent compare with a cost-only router?
A cost-only router can win when the only objective is the cheapest possible API call. ThermoAgent is built for organizations that care about cost per acceptable result. In a blind-judged head-to-head with OpenRouter Auto Router, overall quality was a statistical tie, while ThermoAgent won the complex-query tier by escalating when the cheaper route was not sufficient.
How does ThermoAgent decide when premium compute is worth paying for?
ThermoAgent uses a patent-pending thermodynamic optimization framework plus verified quality evidence to balance inference economics against expected response quality. The system is designed to pay for stronger compute when the quality gain justifies it and avoid unnecessary premium spend when it does not.
How do I know which model actually served my request?
ThermoAgent tracks requested, selected, and served-model provenance alongside cost, latency, and token usage. During development, that instrumentation surfaced a live case where the provider-served model differed from the requested model.
Which providers can ThermoAgent work with?
The platform is model- and provider-agnostic. Six major providers have been integrated and tested during development — OpenAI, Anthropic, Google, Groq, xAI, and Mistral — and the architecture is designed to support additional providers through a unified abstraction layer.
What deployment options are available?
ThermoAgent is being built for managed cloud, self-hosted, and hybrid deployment patterns. Self-hosted components can remain inside your infrastructure; calls to external AI providers follow those providers' normal data paths. Fully disconnected configurations require local or on-premises model endpoints.
Who is ThermoAgent built for?
ThermoAgent is a strong fit for AI-native software companies, enterprise AI teams, agent and workflow products, support automation, research programs, and government or defense programs that use multiple models and care about both inference economics and output reliability.
Is the physics just marketing?
No. ThermoAgent uses a working, patent-pending thermodynamic optimization framework. The public site describes the high-level idea; the underlying formulation, parameterization, and implementation details are reserved for technical diligence under NDA.
How has ThermoAgent been tested?
The platform has been exercised across thousands of documented benchmark executions, repeated controlled live runs, a direct OpenRouter head-to-head, and an 809-query RouterArena benchmark workload. Formal independent reproduction is a separate next step, and qualified evaluators can request deeper methodology and benchmark materials.
Is the technology patent-pending?
Yes. ThermoAgent has four U.S. provisional applications filed to date covering core routing, adaptive learning, verification/orchestration, and newer thermodynamic optimization and premium-spend control mechanisms.
What does the 98.7% savings figure mean?
It is the maximum raw API-cost reduction observed in an aggressive live premium-control test. That configuration also produced a material quality tradeoff, so we treat 98.7% as an economic boundary test — not a quality-parity result or a customer savings promise.
How much latency does ThermoAgent add?
In a V10 deployed benchmark, the routing computation itself measured 0.572 ms at P90 inside 2,000 HTTPS requests. The complete public HTTPS round trip measured 71.267 ms at P90. We report both because routing compute is only one part of the deployed network path; the 0.572 ms figure should not be interpreted as total gateway overhead.
What stage is ThermoAgent at today?
ThermoAgent is in commercial validation. The routing, verification, recovery, provenance, and savings-control layers are implemented and have been exercised in live testing. We are now seeking design partners and pilot customers to validate the system on real production-like workloads.
Talk to ThermoAgent
Benchmark your workload, discuss a design-partner pilot, or request technical and investor materials.
📋 Ways to Engage
• Baseline definition
• Quality acceptance criteria
• Cost-per-accepted-result analysis
• Production-like workload
• Provider/model comparison
• Deployment planning
• Benchmark evidence
• Patent / IP overview
• Investor materials
• Technical capability discussion
• Research collaboration
• Contracting materials
Submit Your Request
Some requested materials may be proprietary or confidential. Access to sensitive technical or IP materials is granted at ThermoAgent's discretion and may require an NDA.
🤝 Direct Engagement
Bring us the workload, opportunity, or problem.
ThermoAgent is in commercial validation. If you're evaluating multi-model AI economics, exploring a pilot, considering a partnership, investing, recruiting, or looking at government collaboration, reach the founding team directly.
Benchmark your workload
Bring your current model strategy, workload, provider mix, and quality requirements. We'll evaluate where ThermoAgent may be able to reduce unnecessary premium-model spend without hiding the tradeoffs.
Strategic conversations
For investment, cloud and AI ecosystem partnerships, technical diligence, channel relationships, and strategic collaboration, contact the founding team directly.
Email the Founding TeamMission and research collaboration
For government programs, federal contracting, research collaboration, evaluation opportunities, and mission-focused AI infrastructure discussions.
Contact ThermoAgentHelp build the company
We're currently recruiting founding revenue leadership and enterprise sales partners who want to help commercialize a working AI infrastructure platform.
Founding team access, not a support queue.
Early customers, partners, investors, and qualified candidates can contact ThermoAgent directly at brody@thermoagent.ai.
brody@thermoagent.aiHelp build the economic control layer for enterprise AI.
ThermoAgent is entering commercial validation. We are building the revenue organization around people who can open enterprise doors, translate technical differentiation into business value, and help turn a working platform into a durable company.
Founding Chief Revenue Officer (Co-Founder)
Own go-to-market, enterprise revenue, partnerships, pilot acquisition, and the buildout of ThermoAgent's commercial organization alongside the Founder.
Founding Enterprise Sales Partner
Originate and close enterprise opportunities with uncapped commission economics and an early-partner residual program subject to the contractor agreement.
Why now?
The product exists. The patent portfolio is underway. The benchmark program is substantial. The next proof is commercial: customers, deployments, recurring revenue, and a repeatable enterprise sales motion.
Founding Enterprise Sales Partner
Build the future of enterprise AI as an early 1099 business-development partner for ThermoAgent™.
This isn't another sales job.
We're not looking for order takers. We're looking for experienced enterprise business developers who know how to open doors, build executive relationships, and close complex B2B technology opportunities. If you're entrepreneurial, self-driven, and excited about helping build a company from the ground up, we'd like to hear from you.
What You'll Sell
ThermoAgent™ is a patent-pending enterprise AI infrastructure and economic-control platform designed to help organizations optimize how they deploy, route, verify, and govern AI across multiple models.
- Optimize AI routing across multiple large language models
- Improve decision quality through verification and escalation
- Reduce unnecessary AI operating cost and premium-model spend
- Improve governance, provenance, and execution oversight
- Increase operational efficiency across multi-model AI deployments
Potential markets: Fortune 1000 companies, technology, manufacturing, aerospace & defense, chemical and industrial companies, energy & utilities, government contractors, and other large enterprise organizations. Regulated sectors such as healthcare and financial services are pursued only where deployment, data-handling, and compliance requirements are appropriate for the engagement.
Compensation
Year One
25% commission on first-year recognized revenue for qualified accounts you personally originate and close, subject to the Independent Contractor Agreement.
Account Management
Sales Partners who continue managing customer relationships may qualify for a 5% annual recurring commission on qualifying renewal revenue, subject to the Independent Contractor Agreement.
Founding Partner Residual Program
Early Sales Partners who originate qualifying customer accounts and later leave ThermoAgent in good standing may remain eligible for a 1% Founding Partner Residual on qualifying customer revenue for as long as those customers remain active, subject to the signed agreement and program terms.
- No commission caps
- No earning limits
- Exclusive territory opportunities may be available for qualified representatives, subject to agreement
Your Responsibilities
- Identify and prospect enterprise organizations
- Build relationships with executive decision-makers
- Generate qualified meetings and conduct discovery
- Present ThermoAgent™ and coordinate technical demonstrations with leadership
- Develop business cases, negotiate enterprise opportunities, and close new business
- Develop long-term customer relationships and account expansion opportunities
What We Provide
- Product and market training
- Sales presentations and enterprise messaging
- Founder and technical support during qualified customer meetings
- Technical demonstrations
- Marketing and sales-enablement materials
- Patent and research context appropriate for customer discussions
- Competitive positioning and benchmark evidence
Who We're Looking For
Ideal candidates have experience selling enterprise software, SaaS, artificial intelligence, cloud computing, cybersecurity, manufacturing technology, industrial automation, digital transformation, engineering solutions, or other complex enterprise technologies.
- Enterprise B2B sales experience
- Experience selling to CIOs, CTOs, engineering, manufacturing, operations, AI teams, or executive leadership
- Strong prospecting and relationship-building skills
- Excellent communication and presentation abilities
- Self-motivated, entrepreneurial operating style
- Existing executive relationships are a plus
Why Join ThermoAgent?
You'll be joining at the beginning. This is an opportunity to help shape the commercial growth of an enterprise AI infrastructure company while building a book of business with uncapped commission potential. We're looking for Founding Enterprise Sales Partners who want to help create the sales motion, not simply inherit one.
How to Apply
Include your resume or LinkedIn profile, a brief overview of your sales experience, industries you've sold into, the largest enterprise deal you've personally closed, the geographic territory you'd like to represent, and why ThermoAgent interests you.
Apply for Founding Sales PartnerPHYSICS > HEURISTICS.
Enterprise AI infrastructure. Engineered for smarter decisions.
Founding Chief Revenue Officer (Co-Founder)
Enterprise AI & SaaS • Own the commercial buildout of ThermoAgent from early validation through repeatable enterprise revenue.
Company
ThermoAgent Inc. has developed an enterprise AI middleware and economic-control platform that intelligently routes requests across multiple leading large language models to optimize inference economics, latency, and response quality. The architecture combines patent-pending thermodynamic optimization with adaptive routing, verification, escalation, provenance, and premium-spend control.
ThermoAgent has a working multi-model platform and a current V10 enterprise routing engine undergoing commercial validation. The company has filed multiple U.S. provisional patent applications protecting core innovations and is pursuing enterprise customers, government opportunities, strategic partnerships, and venture investment.
Why Join ThermoAgent?
- Join as a true founding commercial executive with meaningful equity opportunity
- Commercialize technology that has already been built and benchmarked — not just a concept
- Help define the market position for an emerging AI infrastructure control layer
- Work directly with the Founder on company strategy, customers, fundraising, and product direction
- Build and lead the revenue organization from the ground up
- Shape commercial and government go-to-market strategy as the company matures
The Role
This is not a traditional late-stage CRO seat. We are looking for an entrepreneurial commercial leader who wants to build the company alongside the Founder. You will own enterprise sales, business development, strategic partnerships, customer acquisition, pricing strategy, fundraising support, and go-to-market execution while translating a deeply technical platform into clear enterprise value.
Responsibilities
- Develop and execute ThermoAgent's go-to-market strategy
- Build and manage the enterprise sales pipeline
- Identify, structure, and close pilot/design-partner customers
- Build a path from pilot economics to recurring enterprise revenue
- Develop relationships with Fortune 1000 companies and strategic customers
- Develop partnerships with cloud providers, AI ecosystem companies, integrators, and channel partners
- Support government business development and federal contracting opportunities
- Develop pricing, packaging, and commercialization strategy
- Assist with investor presentations, fundraising strategy, and investor relationships
- Recruit and lead the future revenue organization
- Represent ThermoAgent in customer, partner, investor, and industry meetings
- Translate customer feedback into commercial product priorities with engineering
Required Qualifications
- 10+ years of enterprise software, AI, cloud infrastructure, developer platform, or SaaS sales/business development experience
- Demonstrated ability to close complex enterprise B2B technology agreements
- Experience selling to CIOs, CTOs, AI teams, engineering organizations, operations leaders, or executive leadership
- Strong understanding of enterprise software, APIs, cloud platforms, AI infrastructure, or developer technologies
- Entrepreneurial mindset and comfort operating with limited structure in an early-stage company
- Outstanding communication, presentation, negotiation, and executive relationship skills
- Self-directed, accountable operating style
- Authorized to work in the United States
Preferred Qualifications
- Startup, founder, or early executive experience
- Enterprise AI or generative-AI sales
- Large Language Model ecosystem knowledge
- AWS, Microsoft Azure, or Google Cloud experience
- Government, defense, or federal contracting experience
- Venture-backed startup experience
- Existing executive relationships within enterprise technology
Compensation
- Meaningful founding equity opportunity, subject to definitive company agreements, vesting, and board/company approval as applicable
- Cash executive compensation as funding and revenue milestones support it
- Opportunity to become a long-term executive leader of ThermoAgent
Preferred Skills
Enterprise Sales • Artificial Intelligence • Enterprise Software • SaaS • Cloud Computing • AWS • Azure • GCP • LLMs • AI Infrastructure • APIs • Enterprise Architecture • Business Development • Strategic Partnerships • Go-to-Market Strategy • Government Contracting • Startup Leadership • Venture Capital • Executive Leadership • Sales Operations
Build the commercial engine with us.
If you've successfully sold enterprise software, AI platforms, cloud technologies, or developer infrastructure — and you want to help build a company from the ground up — we'd like to talk.
Apply for Founding CROLeadership Team
Built by the people who invented and engineered the system
Brody Brooks
Brody Brooks is a 20-year-old self-taught inventor and developer from Tennessee and the creator of ThermoAgent's core patent-pending technology. After earning a full academic scholarship to a senior military college, a knee injury during initial military training led to a medical withdrawal. During rehabilitation, he redirected that period into independent research and engineering, developing patent-pending AI and secure embedded-system technologies.
His work spans multi-model AI systems, thermodynamic and information-theoretic optimization, Python/ML development, quantum optimization, cryptography, and embedded hardware. At ThermoAgent, Brody leads product vision, mathematical architecture, intellectual property, and technical research.
Michael Ng
Michael Ng is a software engineer and technical co-founder focused on turning ambitious technical concepts into reliable, usable software. As CTO of ThermoAgent, he works alongside Brody Brooks to translate the company's research and mathematical architecture into deployable systems, scalable infrastructure, and customer-facing technology.
His background spans backend engineering, systems programming, web technologies, cloud deployment, interactive applications, performance optimization, and end-to-end product development. His award-winning interactive and game projects further sharpened the architecture, performance, user-experience, and rapid-iteration skills he brings to ThermoAgent's engineering stack.
Research meets execution.
Brody develops ThermoAgent's core optimization concepts, technical direction, and IP; Michael turns those concepts into working systems, infrastructure, and deployable software. Together, they have taken ThermoAgent from independent research to a live multi-provider platform, a controlled benchmark program, a growing patent portfolio, and commercial validation.
ThermoAgent, Inc.
Live Demonstration
See the deployed ThermoAgent demo and live multi-provider routing interface
Public demo deployment • Live multi-provider routing • Benchmark results are labeled separately by engine version