TP Benchmark
Transfer Pricing Comparables screening done by AI
What it does
TP Benchmark turns your Orbis screen into a defensible comparables set. It finds each candidate's website, analyses it against your tested party under the OECD 2022 framework, and returns evidence-backed ACCEPT, REJECT and REVIEW calls with an audit-ready Excel export.
Turn your Orbis screen into a defensible comparables set. TP Benchmark discovers each company’s website, analyzes it against your tested party under the OECD 2022 framework, and gives you evidence-backed ACCEPT, REJECT, and REVIEW decisions with an audit-ready Excel export.
TP Benchmark takes your Orbis screen, finds each candidate’s website, and analyzes its real business against your tested party. You get an ACCEPT, REJECT, or REVIEW decision you can defend, backed by archived evidence and a full audit trail. A screen returns hundreds of candidates by industry code and keywords. Proving each one is genuinely comparable, by what the business actually does, is still manual, slow, and hard to defend. A durable workflow runs the read-through and pauses for your judgment at the moments that matter. Create a study and upload your Orbis export. Define the tested party yourself, or let the AI Profile Wizard extract it from your existing TP study document. TP…from tp-benchmark.com
Does the same job
all alternatives →
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com