Confidential

面向消费者的 AI 谈判助手。
技术平权，从每一场谈判开始。

August 2026 · counterwell.ai
二手交易 本地服务 薪酬 Offer 汽车交易
创始人

从科研到产品。

Claire ChenFounder & CEO
  • Mathematics
  • 6 篇已发表论文 · ICML ×3、ICLR ×2、AAAI oral（前 4.7%）
  • 大模型智能体 · 强化学习 · 博弈论
研究合作
MITDavid Simchi‑Levi 美国工程院院士
CornellThorsten Joachims 计算与信息科学学院院长
BerkeleySergey Levine 伯克利 AI 研究中心（BAIR）
BerkeleyPieter Abbeel 伯克利 AI 研究中心（BAIR）
联合主任
从方法到应用

研究主线

① 博弈与学习
多方互动，
如何学会决策？
2篇已发表
ICML ×2

马尔可夫博弈
双时间尺度学习收敛

在投 ×3
多智能体博弈
② 信息与评估
数据有限，
如何评估策略？
3篇已发表
ICLR ×2 · AAAI Oral ×1

安全约束 · 多策略
双重最优策略评估

在投 ×3
策略评估 · 公私信息博弈
③ 谈判策略
如何为买方
争取更好结果？
4篇在投
一对一 · 一对多

多商品 · 多对手
覆盖不同交易结构

以买方收益为奖励
直接训练谈判策略
支撑能力
ICML ×1
形式化数学 · 可验证推理
大规模实时决策 在投
高维决策空间的优化
完整发表列表见 CV · 谈判研究见附录
痛点

个体的利益，就这样拱手让人。

二手车 · $42,900情景示意
买家如果今天成交，$40,500 可以吗？
卖家已经给你最低价了。
$42,200，今天可以直接提车。
买家好，那就 $42,200。
“最低价”是真的吗？买家无法判断对方还有多少空间
卖家每天 · 上百笔交易
买家几年一次 · 一笔交易

在美国，这种不对称在汽车、租房、
薪资等高价交易中尤其明显。

信息与经验的不对称，
让消费者在谈判开始前就处于劣势。

产品场景

从日常交易，到人生大单。

高流通量
二手交易
Marketplace
AI 购物助手按预算精选好物
本地服务
Local
多商家自动代谈报价横向比较
高价值
薪酬 Offer
Offer
薪酬结构拆解谈薪实战演练
汽车交易
Cars
多车商自动代谈落地总价比选
解决方案

自研策略模型 × 多线自动代谈

为经济结果训练

直接优化买方收益，而非对话偏好

报价让步时机
强化学习 · 多智能体博弈

让多个卖家同时竞争你的订单

商家 A
原价 $520
当前$455
第 3 轮
商家 B
原价 $500
当前$425
第 4 轮 · 当前最优
商家 C
原价 $535
当前$448
第 2 轮
当前最优$425
比最低初始报价再降$75
交互示意

你定目标，Counterwell 多线替你谈。

完整谈判流程见附录 →
产品演示

覆盖四类谈判场景

Watch the films at deck.counterwell.aiWATCH THE FILMS
recorded in the live product · watch at deck.counterwell.ai
技术优势

通用模型优化满意度，Counterwell 优化谈判结果。

General-purpose models

optimized for helpfulness

↓ tends to concede

optimized for buyer outcomes
RLVR

↑ counters strategically

Four LLM negotiation papers · Claire Chen, main author
研究

四篇论文，覆盖谈判的四种形态。

一对一谈判 用可验证奖励训练谈判模型
一对多竞争 多买家市场中的策略博弈
多商品，单一对手 多商品的双边谈判
多商品，多个对手 利用 counter offer 竞价

四篇里，模型都是在 不断变强的对手面前训练出来的。

附录：量化谈判表现 →学习谈判策略 →
Claire Chen 为四篇论文的主要作者 · 研究主线见前文 ↓
真实交易测试

最小的模型，最大的折扣

46.27%
Counterwell 30B
our research model
28.35%
GPT-5.4
latest frontier
23.81%
DeepSeek
V3.1 · 671B
19.69%
Humans
our own team
The discount obtained, across 30 real deals on Facebook Marketplace.
sources in the appendix
核心壁垒

每一场真实谈判，都在加深数据壁垒。

1沉淀真实谈判轨迹 多轮、多对手、专有交互数据
2绑定真实经济结果 成交价 · 成交 · 退出
3反哺策略模型 学习什么策略真正有效
4形成复利型数据壁垒 更多谈判 → 更深数据 → 更强策略

不是聊天数据，而是带真实经济结果标签的策略数据。

完整数据与技术壁垒见附录 →

竞争格局

为消费者而战的策略型 Agent。

Strategic outcome
optimization
General assistance
B2B
B2C
RealPageSalesforceHubSpot
ADPWorkday
ChatGPTClaudeGemini
Levels.fyiHoney
MuseInstinct
市场空间

四个市场，一个平台。

每年在谈的钱，全美
$200B+
MarketplaceUS secondhand goods
$842B
LocalHome services
$1.8T
Cars54M vehicles, new and used
$13T
OfferWages and salaries
合计$15.84T
变现模型
$15.84T每年在谈的钱
×
1%经我们谈判
×
~0.1%等效变现率
=
$158M预计年收入
订阅制，不抽佣；变现率为订阅收入 / 谈判金额。
$158M谈判额 $158B市场份额 1%
$317M谈判额 $317B市场份额 2%
$792M谈判额 $792B市场份额 5%
$1.58B谈判额 $1.58T市场份额 10%
早期支持

股权零稀释，已获 $38,000。

$20,000
Ryan Summer Program
$10,000
Bill Gross Prize
$5,000
Tinker credits
THINKING MACHINES
$3,000
Demetriades Prize
发展规划

从产品开发，迈向用户增长

已建成 四个产品全部完成
今天
这轮投放要回答的问题
用户转化率
卖家回复率
市场优先级
平台愿景

让本不会发生的交易发生

固定定价

买家因价格离场
卖方成本 买方预算 固定价 尚未实现的交易收益
未成交双方交易收益为 0

AI Agent 谈判

找到可成交区间
卖方成本 买方预算 谈判成交价 卖方收益 买方收益
达成交易买卖双方均有收益

降低谈判成本，让新增成交创造共同收益。

展望

当交易进入 Agent 时代，谁代表普通人？

通用模型 · Agreeableness

过度迎合，过早让步

若为了达成一致而不断退让，用户就可能让出本可争取的收益。

Agent 时代的风险

策略差距，经济鸿沟

当一方擅长博弈，另一方习惯退让，经济差距可能随交易不断扩大。

Counterwell 的答案

为消费者争取更大价值

  • 懂对手推断动机与底线
  • 有策略把握出价与让步时机
  • 善用信息以需求换取更优条件
让普通人也拥有策略，把信息与需求转化为更好的交易条件。

Thank you.

Appendix

The films, the papers, and where every number came from.
Appendix

The four films, in full.

Watch the films at deck.counterwell.aiWATCH THE FILMS
recorded in the live product · watch them at deck.counterwell.ai
Counterwell Marketplace · live

One button, on the page you are already looking at.

The browser extensionSitting beside the listing. One tap reads the page and writes your opening message — nothing to paste.
The full negotiationEvery round after that: what they said, what to send, and how far you are from your ceiling.
Marketplace
Counterwell Local · live

One sentence in. Five companies out.

Say it, or tap a trade. No form.
Names, ratings, review counts, phones, distance — live from Google, with each company’s own photo.
  • Ranked on the evidence. A rating counts for more when more reviews stand behind it, so a 5.0 from 18 does not beat a 5.0 from 76. Google’s own order settles ties and nothing else.
  • Distance can change it — deliberately. Somebody has to drive to you.
Localworking · live Google data
Counterwell Offer · live

Your offer, broken down —
and what to ask for.

Check the numbers$316,200 in year one, $276,200 every year after — because $40,000 of it lands once. The letter never puts those two numbers side by side.
The planLead with the competing offer because its deadline is sooner. Ask for equity, not base — $60,000 to $115,000, settle at $90,000. Words you can say out loud, with no blanks.
Offerbuilt · running today
Counterwell Cars · live

The lowest price is not the cheapest deal.

Kestrel Toyota’s sticker is $996 cheaper — and costs $2,361 more once the rate and the term are counted.

Every offer, cheapest firstEach card carries its gap to the cheapest.
Or drawn, on one rulerEvery charge split into the same four buckets, so a low price with a big fee stops looking like one.
Cars
附录 · 完整流程

不只生成回复，而是完成整场谈判。

1

读懂情境

上传截图、文件或语音，自动读懂消费者的目标。

2

寻找机会

筛选合适的卖家、报价与交易对象。

3

制定策略

自主研发的策略型智能体，决定每一步的报价与让步。

4

自动代谈

同时面对多个卖家，完成多轮谈判。

5

实战演练

在电话或当面沟通前，模拟对手、提前准备。

不是通用聊天机器人，而是专为多智能体博弈打造的谈判系统。

二手交易 本地服务 薪酬 Offer 汽车交易
返回解决方案 ↑
Appendix

Why we beat ChatGPT and Claude.

1 Our model is trained to win. Theirs are trained to agree. General‑purpose models are rewarded for being agreeable, because that is what everyday help, coding and support need. Winning a negotiation rewards the opposite — and that is different model weights, not a different prompt. trained to win
2 Specialization beats brute‑force scale A whole system that does one job — win‑win deals. 46% off against 28% for GPT‑5.4, from a model a tenth the size. backed by research
3 We automate the whole negotiation ChatGPT and Claude hand the user the next sentence to say. We write to every seller ourselves, run all of those conversations at once, and keep pushing for days. full automation
4 Every deal makes the next one better Because we focus on negotiation, every deal ends in a real outcome — a price agreed, or a walk‑away — that is worth training on. Generic conversation never produces one. data flywheel
Counterwell against the general-purpose models
附录

相关研究进展

① 博弈
Offline Two‑Player Zero‑Sum Markov Games with KL Regularization
ICML 2026
Pessimism‑Free Offline Learning in General‑Sum Games via KL Regularization
NeurIPS 投稿中
Fast Rates in α‑Potential Games via Regularized Mirror Descent
NeurIPS 投稿中
Beyond Pessimism: Offline Learning in KL‑regularized Games
NeurIPS 投稿中
② 信息
Pessimistic Minimax Learning for Public‑Private Information Games under Unilateral Coverage
NeurIPS 投稿中
Breaking the Curse of History in LQG: Polynomial Off‑Policy Evaluation via Marginalized Importance Sampling
在研
公私信息在线学习：信息由私有转向公开时，策略能否收敛
研究方向
③ 谈判
Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards
arXiv 2604.09855
Strategic Bargaining in Multi‑Buyer Markets
arXiv 2607.05863
Learning to Recommend: Multi‑Item Bilateral Negotiation via LLM Sellers
投稿中
Learning to Sell: Multi‑Product Portfolio Allocation via LLM Agents
投稿中
规模化决策
OR‑Transformer: Scaling Real‑Time Decision‑Making to 1,000 Items
arXiv 2609.01933 · 审稿中
Strategic Discovery in Agentic Commerce
研究方向
Buyer‑Private Mechanism Design for Agentic Commerce
研究方向
Claire Chen 主导上述全部研究。
完整发表列表见 CV
附录 · 科学建模

量化谈判表现

谈判空间的拉锯示意图
Buyer bargained ratio
← 返回正文研究页
附录 · 训练过程

学习谈判策略

Reward Buyer bargained ratio First-offer / budget ratio Deal ratio
← 返回正文研究页
Appendix

82% joined the waitlist on the spot.

survey · Caltech students
Appendix

Where those numbers come from.

FigureWhat it isSource
$13.0TUS wages and salaries paid, 2025 Bureau of Economic Analysis, national accounts
$842BUS home services market, 2026 Mordor Intelligence — US home services market definition
$1.8TUS vehicles sold in 2025: 16.3M new × $50,000, plus 37.8M used × $25,730 Cox Automotive for the volumes; Kelley Blue Book for both prices
$200B+US secondhand goods bought in a year — furniture, electronics, tools, sporting goods, clothes. Vehicles are not in it, so the row above is not counted twice. $306.5B projected for 2030 OfferUp / GlobalData, 2025 Recommerce Report
$50Expected revenue per acquired paying customer, blended across the four products and their expected tenures Our own prices and how long each kind of customer stays — worked out on the next slide
134.8MUS households, 2025 — what the percentages under the bars are a share of US Census Bureau
82%49 of 60 students joined the waitlist on the spot — interest, not paid signups Our own survey, asked in person on one campus
$50 is the modeled expected revenue per acquired paying customer, blended across the current plans and their expected tenures. The cash-flow projection is cohort-based: a customer contributes receipts only while they are active. Counterwell takes no percentage of transaction value, so the market figures above do not directly determine our revenue.
sources checked 12 August 2026
盈利模式

按谈判场景，灵活订阅。

日常谈判
二手交易本地服务
$4.99 / 月 · $9.99 / 月
大额谈判
汽车交易薪酬 Offer
$39 / 月 · $49 / 月
全场景覆盖
Counterwell Pro
$59 / 月 · $179 / 年
免费试用，立即开始谈判。月付可随时取消，月内不限使用次数；部分套餐提供年付选项。
Appendix

What a customer pays, and what the AI costs to serve them.

Expected revenue per acquired paying customer
They came forPrice × modeled tenureOf 100Together
Marketplace$4.99 / mo × 8-month modeled tenure = $4050$2,000
Local$9.99 / mo × 2.5 = $2515$375
Cars$39 / mo × 1 month = $3915$585
Offer$49 / mo × 1 month = $4912$588
Counterwell Pro$179 for the year8$1,432
100 paying customers100$4,980
$4,980 ÷ 100 ≈ $50 each. Cars and Offer are bought for one month — that is how long the job takes — and Marketplace is modeled at eight. Sensitivity: if Marketplace customers instead take the $49 annual plan, the blend is about $54.
AI inference cost — per acquired customer, over the same tenure
What they use over that tenureAI costAI-level margin
About 36 negotiations$2.3894.1%
About 6 jobs$1.0795.7%
6 cars looked at, 1 bought$2.3194.1%
3 offers, 6 calls rehearsed$1.8296.3%
All four, heavily$5.1897.1%
Moonshot’s published rates: $0.95 per million tokens in, $4.00 out. A whole car negotiation is 39¢. AI cost applies a full year’s usage allowance over the shorter modeled tenure, which is deliberately conservative.
$50 in, $2.33 of AI cost out — a 95.3% margin at the AI level. Each product serves a different customer need. The $50 is a blended planning assumption across customer types and expected tenure, not an assumption that one person buys several products.
prices checked 14 August 2026
Appendix

$1.3M. Raising now.

Pre-seed, open now
$1.3M
Pre-seed. Eighteen months. The product is built.
Seed round at month twelve, after at least 500 paying customers.
52%People Founder full-time and two engineers
19%First users One campaign at a time, then scale the winner
8%Legal and running costs Incorporation, terms, the car review
3%Office Remote for six months, then San Francisco
2%AI and infrastructure Cheaper than the people by a mile
16%Reserve Held back
Appendix

Where the $1.3M goes.

AmountWhat it pays forOf the round
rounded
$210,000Founder at $140,000 a year for all eighteen months — within the $132–153k seed-stage band16%
$219,000A founding engineer on $175,000 a year from month four. Median base is $195,000; $175,000 is the seed rate, trading cash for equity17%
$120,000A second engineer on $160,000 a year from month ten, once there are users to serve9%
$121,000Payroll tax, benefits and insurance, at 22% of salary9%
$250,000Marketing — paid customer acquisition, modeled at $4 per signup. $4/signup and 8% signup-to-paid conversion are pre-pilot assumptions; actual campaign results will replace both19%
$100,000Incorporation, consumer terms and privacy, the automotive review, accounting and tools8%
$30,000AI and infrastructure — model calls behind every negotiation, quote and letter, plus operating headroom, for all eighteen months2%
$42,000A shared office in San Francisco from month seven, at $3,500 a month — remote until then3%
$208,000Runway and contingency reserve16%
$1.3M = $1,092,000 of planned spend + $208,000 held as reserve. Under the current model the raise acquires roughly 5,000 paying customers over eighteen months, projecting about $216,000 of customer cash receipts. The model assumes six months remote, then twelve in a shared San Francisco office. The next page shows the cash-flow timing.
Appendix

18-month cash flow projection.

Cumulative
customers acquired		Active paying
at period end		 Operating spendCustomer
cash receipts		Net cash burn
Months 1–6750520$235,000$28,000$207,000
Months 7–122,2501,240$377,000$66,000$311,000
Months 13–185,0002,300$480,000$122,000$358,000
Month 18 / 18-month total5,0002,300 $1,092,000$216,000$876,000
Spending is budgeted; customer receipts are projected. Receipts are cohort-based: a customer pays only during the modeled tenure for their product, so cumulative customers acquired and active paying customers differ. Marketing is phased 15% / 30% / 55% across the three periods, as the channel moves from validation to scaling. Some receipts from customers acquired late in the runway fall beyond month eighteen.

Acquisition is modeled at $4/signup and 8% signup-to-paid conversion, both pre-pilot assumptions — together they imply a $50 cost per paying customer acquired. Month-18 customer cash receipts are approximately $21,800.
Appendix

What month twelve looks like.

Milestone one

At least 500 paying customers

Roughly $2,500 a month, recurring.

  • 1,240is what the model on the previous page projects
  • 500is the number we plan to hold
Milestone two

One channel that repeats

All four products tested, then the winner scaled.

  • 45%of the marketing budget spent by month twelve, finding the channel
  • 55%held back for months thirteen to eighteen, to scale what worked

The model’s customer numbers follow from $4 a signup and 8% signup‑to‑paid, both pre‑pilot assumptions the first campaign replaces — so the milestone is set well under them.

the plan, not results
00:00

Say

Cues

Then

Next

Use → ← to move · F fullscreen