
Ramp Labs
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Today, we’re launching Ramp Sheets, the AI spreadsheet editor built to 10x finance teams, operators, and founders. Ramp Sheets plans, executes, and formats automatically. It’s already being used to model budgets, forecast revenue, and reconcile financial statements.
Ramp Labs1,327,472 просмотров • 11 месяцев назад

Introducing Ramp Accounting Bench. We partnered with accounting professionals to create 137 tasks and grading rubrics grounded in everyday accounting workflows. Even with three attempts, the best model achieved only 21% accuracy. Reliable agentic accounting remains an open challenge.
Ramp Labs88,328 просмотров • 1 месяц назад

We’re open-sourcing PorTAL, our framework for shared task representations and cross model LoRA adaptation. It now spans from hybrid attention models to multimodal systems including Gemma 4 E2B, Mistral 7B & Thinking Machines' Inkling. Code: ramp-public/portallib Models: Hugging Face /RampPublic
Ramp Labs171,845 просмотров • 3 месяцев назад

Introducing PorTAL: Portable Task Adapters for LLMs. A novel recipe to cheaply port fine-tuning between models. It matches per task LoRA accuracy at half the cost, lowering the switching overhead of adapting tasks across LLMs. At Ramp, every new model release used to mean retraining our fine-tunes from scratch. PorTAL learns the task once, then efficiently refits it onto any new base model, even across model families.
Ramp Labs173,290 просмотров • 3 месяцев назад

We had early access to NVIDIA AI Nemotron 3.5 Super VL and post-trained it for one of our highest-volume workflows: receipt extraction. One epoch of supervised fine-tuning on 10,000 examples made it competitive with leading open and closed-source models on our receipt benchmark.
Ramp Labs16,168 просмотров • 12 дней назад

AI spend is measured in tokens, model calls, and dollars. However, none of these fields describe the actual work being done. At Ramp, we built a semantic layer that attributes agent spend to objectives and outcomes. This allowed us to go from monitoring AI spend to understanding AI ROI. Here's how we built it 🧵
Ramp Labs44,997 просмотров • 1 месяц назад

Recent KV cache compression research has focused on small models with dense attention. We scaled these techniques to GLM 5.3, a frontier sparse attention model. We adapted KVzip and Attention Matching, reducing KV cache size by 80% while retaining over 90% of full-context accuracy on the QuALITY benchmark.
Ramp Labs22,064 просмотров • 29 дней назад

A finance team just sent us this. They ran a full budget vs. actuals analysis in under 5 minutes. Uploaded their budget P&L and 10 months of GL actuals. Ramp Sheets matched every line item, calculated variances, and flagged problem areas. This used to take half a day.
Ramp Labs141,898 просмотров • 9 месяцев назад

B2B SaaS switching costs collapse during competitor acquisitions. – 15% of their customer base enters consideration – CAC drops 3-5x – Conversion spikes 5-10x Capture a few % = years of compounding LTV. Plug in your competitor's customer count and see the opportunity ↓
Ramp Labs138,780 просмотров • 9 месяцев назад