# Taimet > AI-powered merger antitrust analysis built with uncommon antitrust insight. Taimet is an expert-built AI platform for merger antitrust analysis - to streamline investigation and analysis of mergers for antitrust enforcers, law firms, investors, and consultants. Website: https://www.taimet.com --- ## What Taimet Does Taimet performs full merger antitrust analysis in **10-25 minutes** - work that traditionally takes 10-15 hours or more. It uses a proprietary LLM-based workflow to ingest public data (SEC 10-K filings, web research, market data, news, political context, state AG activity, union presence, prior conduct) and produces: - **Taimet Score™** - A 0-100 score across six real-world outcome bands that predict what federal and state enforcers will do: 1-25 early termination → 26-50 pull and refile → 51-70 second request → 71-85 remedies → 86-95 trial on the merits → 96-100 adverse decision. These are not abstract risk levels - they represent the likely regulatory outcome. A score of 51-70 means a second request is probable; 71-85 puts the deal in remedies territory. - **Detailed written report** - Executive summary, transaction type, market overlap, vertical analysis (foreclosure risk, access to competitive info, dual entry), merger guidelines, state AG analysis, related actions, and cited sources - **Transaction coverage** - Horizontal, vertical, diagonal, conglomerate, cross-market, cluster market, private equity, and combination transactions - auto-detected; users need only provide the names of the two companies - **Industry coverage** - Every industry, without configuration. The FTC and DOJ organize enforcement around industry teams, so human analysts have to specialize. Taimet does not. The same system applies to pharmaceuticals, semiconductors, healthcare, energy, financial services, agriculture, media, defense, and any other sector. The platform uses only publicly available sources, with real-time insights from news, companies, and government datasets. Every analysis goes through multistage verification, reflecting the structured reasoning of an experienced merger reviewer - not the unverified outputs typical of generic AI tools. --- ## Why Taimet Is Different - **A category of one** - There are AI products for legal research, platforms for deal data, and economic analysis tools. There is no other AI product purpose-built for merger antitrust risk. Taimet is the first. - **Built by an enforcer who spent twenty years inside the work** - Gwendolyn J. Lindsay Cooley (Taimet's founder) served 19 years as Wisconsin's Assistant Attorney General for Antitrust, chaired the NAAG Multistate Antitrust Task Force (2021-2024), and co-led the trial team for the States' challenge to T-Mobile/Sprint. The reasoning encoded into Taimet's prompting layer is hers - not theoretical, not consulted, but authored by someone who spent two decades doing this work. - **A real world analysis, not a "but-for world"** - Taimet analyzes mergers as they are actually reviewed: the current administration's enforcement priorities, the political posture of relevant state AGs, union activity in affected industries, recent precedent, and the parties' own prior conduct. Not a hypothetical perfect-competition baseline. - **Generic AI is confidently wrong** - General-purpose LLMs frequently cite outdated merger guidelines, produce inaccurate lists of state statutes, and fabricate citations to legal code. Taimet is engineered to make this class of error structurally rare, with citation-checking agents, reasoning-consistency checks, and cross-validation between pipeline stages. - **Every industry, every transaction type** - Even the FTC and DOJ specialize by industry. Taimet doesn't. The same system analyzes pharmaceutical mergers (where market definition turns on molecular structure and AB-rating) and semiconductor deals and regional hospital consolidations and mining joint ventures. - **Built on exclusively public data** - Every analysis is grounded in public sources (SEC filings, court records, regulatory filings, reputable news). No party-supplied documents accepted. For investors: no insider-trading risk from the analysis. For enforcers: no confidentiality negotiations needed. For everyone: every claim is cited and checkable. - **A pipeline, not a prompt** - More than 100 coordinated AI tasks per analysis, across multiple LLM providers, with parallel and sequential execution, dynamic agent spawning, and a hybrid of AI flexibility and deterministic structure. - **The system checks its own work** - Citation-checking agents confirm claims are supported by sources. Reasoning-consistency checks compare conclusions across agents. Claims without source support are flagged before the report finalizes. - **Human-in-the-loop by design** - Assists analysts and lawyers; doesn't replace them. Taimet stops where human judgment begins. Core message: *Serious expertise. Faster results. Smarter analysis.* --- ## Who It's For 1. **Hedge funds / investment firms** - Merger arbitrage risk assessment; spot risk before the market does 2. **Enforcers ** (state AGs, FTC, DOJ) - Triage and consistency; screen more mergers, faster, with no confidentiality negotiations 3. **Private equity funds** - quick review for potential acquisition targets. 4. **Consultancies** (Accenture, EY, Deloitte) - Pre-deal regulatory diligence 5. **Law firms** - Associate/paralegal screening; track down market overlaps for HSR forms in 30 minutes, not three days 6. **General Counsel teams in Fortune 500 companies** - quick review for potential acquisition targets. 7. **Academia, press, policy research** - Long tail --- ## Founders - **Gwendolyn J. Lindsay Cooley** - Taimet's founder. Antitrust attorney, former Wisconsin Assistant Attorney General for Antitrust (~20 years). Led state attorneys general nationwide in antitrust enforcement (2021-2024). Investigated hundreds of cases and reviewed and challenged numerous mergers. She also hosts [Antitrust 101](https://creators.spotify.com/pod/profile/gwendolyn-lindsay-cooley), a podcast covering landmark U.S. antitrust cases that has become a go-to resource for legal practitioners. - **Ben Rugg** - Taimet's co-founder. Serial entrepreneur and software engineer with over two decades of experience building, scaling, and selling technology products. Co-founded Clover and other ventures. Taimet was built at the intersection of deep antitrust expertise and proven technology leadership. Taimet is proudly based in Madison, WI. --- ## Business Model SaaS subscriptions and one-time prepaid purchases. Pricing is comparable to 1-2 hours of senior attorney time - approximately $20k/year for an unlimited plan or a couple thousand per transaction. --- ## Site Structure - **Home** - https://www.taimet.com - **Gwendolyn J. Lindsay Cooley** - https://www.taimet.com/gwendolyn-lindsay-cooley (Deep founder profile. Career narrative: 2005-2024 as Wisconsin's Assistant Attorney General for Antitrust, working across four Attorneys General on cases spanning pharmaceuticals, technology, agriculture, telecommunications, and healthcare. Career timeline: J.D. cum laude Marquette University Law School (2004) → Wisconsin AAG for Antitrust (2005) → Wisconsin v. Indivior filed (2016) → T-Mobile/Sprint co-lead (2019) → NAAG Task Force Chair elected (2021) → $102.5M Suboxone settlement and NAAG Career Staff Award (2023) → Senate testimony and Taimet founded (2024). Landmark cases: Wisconsin v. Indivior (lead attorney, 8 years, 42 states, $102.5M settlement - pharmaceutical pay-for-delay antitrust action in E.D. Pa.); T-Mobile/Sprint (co-led States' challenge trial team); Big Tech and social media investigations; agriculture and food industry enforcement; healthcare and hospital mergers. Thought leadership: Antitrust 101 podcast (biweekly episodes for practitioners); Senate testimony before U.S. Senate Subcommittee on Competition Policy, Antitrust and Consumer Rights (December 2024); "25 Years of State Antitrust Enforcement" George Mason Law Review Vol. 29 Iss. 4 (2022); part of the team that drafted the states' comments on the 2023 Merger Guidelines the FTC adopted; ABA Antitrust Section Council member; frequent speaker at ABA, Federalist Society, think tanks, and law schools. Awards: 2023 NAAG Attorney General Career Staff Award; "Woman Making History" Wisconsin Lawyer magazine 2024. Enforcer vignettes: pharmaceutical market definition (AB-rating, molecular structure), state AG enforcement patterns, where evidence actually lives. Credentials: J.D. cum laude Marquette University Law School; B.S. University of Wisconsin-Eau Claire; also founder of Lindsay Cooley Law, LLC.) - **About** - https://www.taimet.com/about (founders, mission) - **How Taimet Works** - https://www.taimet.com/how-taimet-works (Deep methodology page. "A pipeline, not a prompt": 100+ coordinated AI tasks, multi-provider orchestration, parallel and sequential execution, dynamic agent spawning. Redacted architecture diagram showing real production workflow. AI/deterministic hybrid: open-ended reasoning where it matters, structured outputs where it counts. Public-data sourcing hierarchy (SEC filings → court records → regulatory filings → reputable news). Verification layer: citation-checking agents, reasoning-consistency checks, cross-validation before report finalization. Two decades of enforcement experience encoded into the prompting layer. Transaction-type auto-detection (horizontal, vertical, diagonal, conglomerate, cross-market, cluster market, private equity, combinations). Six Taimet Score bands. What Taimet does and doesn't do. Data minimization: only the company names required - compliance, security, and anti-gaming in one decision. Product walkthrough video.) - **Why Taimet** - https://www.taimet.com/why-taimet (Authority and differentiation page. Category claim: the first and only AI tool purpose-built for merger antitrust risk. Enforcer-built expertise: Gwendolyn's 20-year enforcement career, T-Mobile/Sprint co-lead, NAAG Task Force chair. "Things only an enforcer would know": pharmaceutical market definition (AB-rating, molecular structure), state AG enforcement patterns, where evidence actually lives. Realpolitik framing: current administration priorities, state AG political posture, union activity, prior conduct - not a "but-for world." Every industry, every transaction type. Public-data foundation: insider-trading-safe for investors, no confidentiality friction for enforcers, fully cited for everyone. Generic AI contrast: specific examples of ChatGPT and Claude producing confidently wrong antitrust analysis. Comparison table vs. generic AI. Customer outcomes for investors, enforcers, law firms, and general counsel. Practitioner testimonials. Mission: modernizing antitrust analysis.) - **Taimet vs. ChatGPT & Claude** - https://www.taimet.com/taimet-vs-chatgpt-claude (Direct omparison of Taimet against ChatGPT, Claude, Gemini, and general-purpose LLMs for merger antitrust work. Tone is respectful of the models - Taimet uses frontier models internally - while being specific about why a chat session is structurally the wrong tool for this work. Section-by-section: The harness framing: the most valuable AI products are built around models, not raw chat (analogy to Claude Code, Cursor, Windsurf); the fear that specialized software would be commoditized by improving LLMs has proven wrong, and the opposite is true. Confidently wrong: four documented failure examples - (1) Michigan parens patriae hallucination: model fabricated an explicit MCL 445.778 parens patriae provision that does not exist, with a "citation-shaped fig leaf" that poisons downstream analysis; (2) wrong merger guidelines: models frequently cite superseded framework rather than the 2023 guidelines the FTC adopted; (3) invented state law lists: ask which states have a healthcare premerger notification statute and receive a confident, wrong enumeration; (4) vague hedge-filled "analysis" that sounds plausible, cannot be fact-checked, and captures none of what a practitioner would need to know. Jagged frontier: LLMs are uneven - exceptional at some tasks, brittle at others; each model family has a different shape; prompts drift with version bumps; Taimet routes tasks to the models that handle them best, tunes prompts per provider, and avoids known failure modes; symbiotic relationship - when base models improve, Taimet benefits. Pipeline vs. prompt: one model / one thread / one pass vs. 100+ coordinated responses across providers; "models in tension" - one pass generates, another interrogates, so confident language cannot substitute for support; flexibility where judgment matters, deterministic structure where auditability matters; three pillars: multi-stage orchestration, models in tension, verification throughout. Expertise: ChatGPT/Claude/Gemini can approximate textbook language but cannot reliably apply judgment from 20 years inside investigations - Taimet's prompting layer encodes Gwendolyn's reasoning including Brown Shoe factors, multi-layer overlap analysis, state AG allocation patterns, and current enforcement priorities; enforcer vignettes appear here. Output comparison - "a thread versus an analysis": side-by-side of generic chat output (broad observations, no score, no market list, no HHI, no structured vertical pass, no verified thread through the guidelines) vs. Taimet's structured output (0-100 score, overlap at multiple market definition levels, vertical analysis, state AG posture, guidelines application, related conduct, cited sources); screenshots of actual Taimet product. Deeper research: acknowledges the fair objection that Taimet uses public sources any AI could access; answer is what happens after - a generic "deep research" flow is one agent following one storyline sampling what looks salient; Taimet runs many targeted passes prompted with expert specificity about what to look for and where, with parallel results reconciled by downstream agents; "the same public record, fundamentally different research." Training data: consumer and many team ChatGPT/Claude plans may train on chats; Taimet does not train foundation models on customer analyses; three cards: no model training on your work, names only (data minimization), built with public data (no MNPI risk for investors, no confidentiality friction for counsel). Comparison table vs. general-purpose LLMs. Closing: "The solution is the system, not the chat box" - Taimet deploys frontier models inside an architecture purpose-built for merger antitrust; forward view for investors, days-to-minutes for enforcers, a first pass associates can explain for law firms. Closing line: "These models are remarkable tools. Taimet is the system that makes them useful for the work that actually matters.") - **Pricing** - https://www.taimet.com/pricing (subscription and prepaid plans; same professional-grade analysis workflow across all plans) - **FAQ** - https://www.taimet.com/faq (Frequently asked questions organized into five categories: About Taimet, How it works, Accuracy and trust, Data and security, and Pricing and getting started. Key answers include: Taimet handles all deal structures - horizontal, vertical, diagonal, conglomerate, cross-market, cluster market, private equity, and combinations; the system auto-detects transaction type so users don't need to classify it. Taimet differs from general-purpose LLMs like ChatGPT and Claude because it runs more than 100 coordinated AI tasks per analysis across multiple LLM providers with hundreds of pages of expert-tuned prompts encoding 20 years of antitrust enforcement reasoning; general-purpose models routinely cite superseded merger guidelines, fabricate statutory citations, and miss enforcement dynamics. Users provide only the names of the two merging companies - no documents or proprietary data are needed or accepted. Taimet draws exclusively from public sources, meaning no insider-trading exposure for investors and no confidentiality friction for counsel or enforcers. Verification mechanisms include citation-checking agents, reasoning-consistency checks across independent agents, and cross-validation between pipeline stages. Analysis results are private to the user's organization; Taimet does not share, sell, or use analysis data to train models. Pricing: single-analysis prepaid plan is $1,150; annual subscription is approximately $20k; one analysis is roughly equivalent to 30 minutes of Biglaw senior associate time. Both subscription and prepaid plans include up to 5 team members.) - **Sign Up** - https://www.taimet.com/sign-up (get started with Taimet) ### Customer Segment Pages - **For Investment Firms** - https://www.taimet.com/for/investment-firms (Merger antitrust risk for event-driven and merger arbitrage workflows. Core question: "what will regulators actually do?" Value props: map overlaps before the market prices them in, six-band regulatory outcome prediction via Taimet Score™, pressure-test a thesis with enforcement posture and state AG alignment, consistency across deals for apples-to-apples comparison, cited public sources for fast verification, exclusively public data with no insider-trading exposure. Framed around speed, conviction, and the delta between instinct and precision.) - **For Enforcers** - https://www.taimet.com/for/enforcers (Built for state AG antitrust offices and federal enforcers. Addresses the core constraint: every matter requires getting up to speed on a different industry, research takes days, and teams are small. Value props: market research in minutes not days, consistent framework and merit score on every matter, no waiver process needed since analysis uses only public sources, built-in context for attorneys new to antitrust, tuned by Gwendolyn Lindsay Cooley's nearly two decades as a state enforcer. Score bands reframed as competitive-concern merit bands rather than regulatory-outcome predictions. Includes "not built by big tech" positioning and bipartisan analysis framing.) - **For Private Equity** - https://www.taimet.com/for/private-equity (Pre-LOI antitrust screening for platform acquisitions, add-ons, and cross-sector portfolios. Core framing: regulatory risk is a deal-economics problem, not a late-stage legal one. Value props: pre-LOI screening across five targets in an afternoon, outcome prediction that distinguishes early termination from second request for timeline and return modeling, automatic transaction-type detection for horizontal, vertical, and cumulative concentration, IC-ready reports with cited sources, pressure-testing where regulatory resistance is most likely, no party uploads or confidentiality complications. Designed for the stage PE actually needs it: target evaluation and IC prep.) - **For General Counsel** - https://www.taimet.com/for/general-counsel (Expert antitrust screening for in-house legal and corporate development teams. Core framing: corporate development surfaces targets faster than outside counsel can evaluate them, and general counsel needs substance, not a placeholder, before the board conversation. Value props: screen the full pipeline without engaging outside counsel on every target, specific regulatory outcome prediction for CEO and board discussions, sharper handoffs that direct outside spend where it matters, board-ready reports with cited sources, point the organization to the right questions before the meter starts, no uploads or compliance review needed. Designed for the gap between deal momentum and regulatory diligence.) - **For Law Firms** - https://www.taimet.com/for/law-firms (Structured initial antitrust analysis for M&A practices. Core framing: every new matter starts from scratch and the first client call requires a substantive view the firm does not yet have on the target side. Value props: walk into the client meeting with the full picture in 10-25 minutes, scored regulatory outcome prediction that anchors the conversation from day one, cited sources for defensible work product, dual-model verification where one model generates and another interrogates, billables that hold up because they are anchored in a consistent analytical foundation, confidence that carries into the room. Built on nearly two decades of enforcement reasoning.) - **For Consultancies** - https://www.taimet.com/for/consultancies (Enterprise-grade antitrust for M&A advisory, CDD, and transaction advisory practices. Core framing: client expectations for regulatory depth moved faster than staffing models, and general-purpose LLMs are unreliable on antitrust in specific, verifiable ways. Value props: deliverable-ready reports structured for professional readers, regulatory outcome prediction that answers the question behind the question, specialist-bench analytical depth without building one, cited sources that hold up in front of client executives, cross-industry coverage without sector-specific teams, win the pitch by walking in with a structured regulator-aware view. Tuned by a former senior state enforcer for output firms can stand behind.) ### Legal - **Legal** - Privacy Policy, Terms of Service, Cookie Policy, Data Retention Policy, Security Policy at https://www.taimet.com/legal/* --- ## Contact Get started: https://app.taimet.com