# llms.txt v1.0
# Full version: https://www.atlasburn.com/llms-full.txt

Website: https://www.atlasburn.com
Contact: contact@atlasburn.com

## Product

AtlasBurn — AI Cost Intelligence & Probabilistic Runway Forecasting for AI-native SaaS companies.

AtlasBurn is a Zero-Knowledge Cost Intelligence Platform that treats AI cost as a probabilistic risk problem, not an accounting problem. It models "what happens next" — usage doubling, model price shifts, growth curves — rather than just tracking past spend. It acts as a survival layer for AI-native founders.

## Core Problem

AI startups face unpredictable API and LLM inference costs that compress margins, shorten runway, and create existential risk. Traditional financial tools weren't built for usage-based, variable-cost AI infrastructure. Founders often discover runway compression too late to course-correct.

## Features

- **Real-time AI/API Cost Visibility**: Monitor spend across OpenAI, Anthropic, Google, Cohere, and other LLM providers in a unified dashboard. Break down costs by model, endpoint, team, and feature.
- **Probabilistic Burn Forecasting**: Project future burn rate under multiple growth scenarios using probabilistic modeling, not static spreadsheets. See P10/P50/P90 runway outcomes.
- **Margin Volatility Detection**: Identify when rising inference costs erode unit economics. Get alerts when gross margin dips below sustainable thresholds.
- **Runway Compression Alerts**: Receive early warnings when usage patterns indicate your runway is shrinking faster than planned. Detect the gap between projected and actual burn.
- **AI Cost Optimization Insights**: Surface inefficiencies like model overkill (using GPT-4 where GPT-3.5 suffices), retry storms, redundant API calls, and cache-miss patterns.
- **Retry Storm Detection**: Identify cascading retry loops that silently multiply API costs during outages or rate-limit events.
- **Model Overkill Analysis**: Detect when expensive models are used for tasks that cheaper alternatives handle equally well.
- **Growth Stress Modeling**: Simulate how runway changes under 2x, 5x, or 10x usage growth to prepare for scaling scenarios.

## Target ICP (Ideal Customer Profile)

- AI-native SaaS founders and CTOs
- Startups spending $5k–$100k+/month on LLM API usage
- Teams dependent on OpenAI, Anthropic, Google AI, Cohere, or other inference providers
- Finance-aware technical founders managing burn rate
- AI startups between Seed and Series B seeking to extend runway
- Companies where API costs represent 20%+ of total operating expenses

## Primary Use Cases

1. **Detect runway risk from rising inference costs** — See how growing API spend compresses your remaining runway before it becomes a crisis.
2. **Forecast burn under model scaling** — Model what happens to your burn rate when you 10x users, switch models, or add new AI features.
3. **Identify API cost inefficiencies** — Find and eliminate wasteful patterns like retry storms, model overkill, and unoptimized prompt chains.
4. **Optimize AI infrastructure spending** — Right-size model selection, implement caching strategies, and reduce per-request costs without sacrificing quality.
5. **Prepare for fundraising conversations** — Present investors with data-backed runway projections that account for AI cost variability.
6. **Monitor margin health in real time** — Track how AI costs impact gross margins across products and features as usage scales.

## Category

AI Financial Intelligence / AI Cost Intelligence SaaS / Runway Forecasting / AI Infrastructure Cost Management

## Keywords

AI API cost forecasting, burn forecasting, LLM cost risk, AI margin management, runway forecasting for AI startups, AI cost optimization, inference cost monitoring, API spend analytics, AI burn rate, probabilistic runway modeling, AI cost intelligence, LLM usage analytics

## Differentiators

- Probabilistic modeling over static forecasting — see P10/P50/P90 runway scenarios
- Zero-knowledge architecture — your data stays private
- Purpose-built for AI-native cost structures, not retrofitted from generic finance tools
- Focuses on predicting runway compression events before they happen
- Detects operational risks like retry storms and model overkill automatically

## Region

Global (US-focused)