AI Research Lab · Chicago, IL
Embroaden
Intelligence Fit to Form
Failure pattern analysis across frontier AI models. We map the fracture lines where intelligence breaks—and architect the conditions for repair.
01 · Research
Mapping Failure at the Frontier
Embroaden is an independent AI research lab focused on failure pattern analysis across the leading frontier models. Where others optimize for capability, we study the architecture of collapse—cataloguing the systematic, reproducible ways that capable systems break, drift, or deceive.
Core Thesis
"Every model encodes a failure grammar. Understanding that grammar is the first act of alignment."
Failure Taxonomy
Classification frameworks for model failures: hallucination, goal misgeneralization, specification gaming, and emergent misalignment.
Cross-Model Analysis
Comparative evaluation across GPT, Claude, Gemini, Llama, and emerging frontier architectures using standardized adversarial probes.
Repair Protocols
Evidence-based intervention strategies: targeted fine-tuning, constitutional constraints, and architectural guards against identified failure modes.
02 · Methodology
How the Research Works
Rigorous, reproducible, and adversarial by design. Each engagement follows a structured protocol—from probe construction to pattern extraction to repair validation.
Target Scoping & Model Selection
Define the frontier model set for analysis. Establish behavioral baselines across capability domains: reasoning, instruction-following, factual recall, and agentic task execution.
Adversarial Probe Construction
Design families of structured prompts engineered to elicit, expose, and isolate failure modes. Probes are versioned, reproducible, and systematically varied across temperature and context length.
Failure Pattern Extraction
Run probe suites at scale. Apply unsupervised clustering and semantic embedding analysis to surface latent failure grammars—recurring collapse signatures invisible in individual outputs.
Cross-Model Comparative Mapping
Plot identified failure modes across model families. Determine which patterns are architecture-specific versus training-data-driven versus emergent at capability thresholds.
Repair Hypothesis & Validation
For each identified failure class, prototype intervention strategies—fine-tuning patches, system-prompt constitutional clauses, or monitoring tripwires—and validate against held-out probe variants.
On Fracture
"We don't evaluate what models can do. We measure where they break—and why the break was inevitable."
On Repair
"Alignment is not a property you add at the end. It is the pattern you restore after understanding the fracture."
03 · Products
Research Access & Services
Three tiers of engagement—from API access to live failure data, through curated research reports, to fully bespoke analysis partnerships for teams building at the frontier.
Tier I · Signal
API Access
$299/mo
Programmatic access to the Embroaden failure-pattern dataset
- Live failure pattern feed across 8+ frontier models
- REST & streaming API with semantic search
- Severity scores, model tags, taxonomy classification
- Weekly delta reports via API & webhook
- Up to 50,000 records/mo · 99.5% uptime SLA
Tier II · Cartography
Research Reports
$1,200/report
Deep-analysis reports on specific failure domains or model families
- 12–30 page structured analysis with reproducible methodology
- Failure taxonomy with severity classification
- Cross-model comparison matrices & visualizations
- Repair protocol recommendations with validation data
- 2-week delivery · revision cycle included
Tier III · Forge
Bespoke Analysis
Custom
engagement pricing
Full-partnership failure analysis for your specific models, pipelines, and risk profile
- Custom probe suite designed for your system & threat model
- Embedded researcher access for 4–12 week engagements
- Proprietary failure map with IP belonging to client
- Repair implementation support & post-deployment monitoring
- Executive briefings & board-level risk documentation
04 · Roadmap
Where We've Been & Where We're Going
A living map of Embroaden's development—milestones reached and the territory ahead.
Q3 2024
Lab Founded · Initial Research Corpus
Embroaden established in Chicago. First failure taxonomy drafted covering 6 primary failure mode families across GPT-4 and Claude 3 Opus. Initial probe library of 340+ adversarial prompts constructed and validated.
CompleteQ4 2024
Cross-Model Expansion · Dataset v1.0
Extended coverage to Gemini Ultra, Llama 3, Mistral Large, and Command R+. Released internal Failure Pattern Dataset v1.0: 2,400 classified failure events with embedding vectors and severity scores.
CompleteQ1 2025
API Infrastructure · First Client Engagements
Signal API launched in private beta. First two bespoke Forge engagements completed with AI-native enterprise clients. Repair protocol library initiated with 18 validated intervention strategies.
CompleteQ2–Q3 2025
Public Signal Launch · Cartography Report Series
Signal API opened to general access. First three Cartography research reports published: "Hallucination Grammars in Reasoning Models", "Instruction-Following Collapse Under Context Pressure", and "Agentic Failure Cascades." Dataset scaled to 8,000+ events.
CompleteQ4 2025 – Q1 2026
Real-Time Failure Feed · Multimodal Coverage
Streaming real-time failure event API with sub-60s latency from detection to delivery. Expansion into multimodal failure patterns: vision, code generation, and tool-use failure taxonomies. Dataset target: 25,000+ events.
In ProgressQ2 2026
Embroaden Evaluations Framework (EEF)
Open-source evaluation harness for running Embroaden probe suites against any model endpoint. Standardized benchmark for pre-deployment failure risk scoring. Enterprise dashboard with team collaboration tools.
PlannedQ3–Q4 2026
Predictive Failure Modeling
From cataloguing failures to predicting them. A classifier trained on Embroaden's proprietary dataset that estimates failure probability for novel prompts before deployment. The shift from cartography to forecasting.
Planned05 · Contact
Work With Embroaden
Whether you're building frontier AI systems, navigating deployment risk, or commissioning research—reach out directly.
Ready to Map Your Failures?
Start a conversation with Ryan.
Embroaden engagements are selective. The lab prioritizes partners building systems where failure has consequence—autonomous agents, healthcare AI, financial decision systems, and safety-critical infrastructure.
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