88%
Organizations now use AI in at least one business function.
Why now
AI adoption is broad.
AI adoption is broad.
Reliable scaled value is not.
~1/3
Only about one-third say their companies have begun to scale AI programs.
74%
Companies still report difficulty achieving and scaling value from AI.
Prompt + context
Single-pass generation
Answer
The problem
Most AI systems still reason in one sweep. They draft quickly, but they struggle when evidence must be revisited before a final decision.
- Long document chains and fragmented evidence.
- Conflicting facts, exceptions, and revisable assumptions.
- Human review, compliance, and auditability requirements.
What buyers actually need
- Reasoning that can reopen assumptions when new evidence appears.
- Outputs that settle only after the underlying logic stops shifting.
- An audit trail that shows why an answer changed and what evidence was used.
Rosetta is built to be that control layer: a system that manages how the model revisits evidence before finalizing high-stakes outputs.
Category position
The reasoning layer is structurally open.
Enterprise applications
Healthcare, finance, legal, research
Crowded
Rosetta reasoning layer
Convergent, auditable, multi-pass
Open category
Foundation models
GPT, Claude, Gemini, Qwen, DeepSeek
Crowded
Infrastructure and compute
GPUs, vector stores, pipelines
Crowded
Why this matters
If Rosetta becomes trusted in one regulated workflow, it can expand across multiple products and models without needing to become the base model provider itself.
Rosetta is the control layer between models and workflows: convergent, auditable, and multi-pass.
1. Targeted readPull the next evidence needed.
2. HypothesisForm the current answer state.
3. RevisionUpdate when evidence changes it.
4. ConvergenceStop when reasoning settles.
Entry wedges
Start where review cost, denial risk, and compliance burden are high.
$200B-$340B
Annual value potential in banking from gen AI.
- Strong fit for collections, mortgage servicing, risk, and investment research.
- Long document chains and policy exceptions create review bottlenecks.
- Commercial story: faster, more consistent case resolution with an audit trail.
39 / week
Prior authorization requests per physician, with 13 hours/week spent completing them.
- Clear fit for prior authorization, utilization review, and clinical document intelligence.
- Administrative burden is high enough that even modest workflow gains matter.
- Commercial story: fewer review loops, faster evidence synthesis, and better auditability.
Roadmap
Land with wedge products. Expand into infrastructure.
Pilot products
Launch Rosetta Engine v1 on open models, set benchmark baselines, and run design-partner pilots in healthcare and finance.
Vertical expansion
Convert pilots into annual enterprise subscriptions and extend the same engine into LegalAI and DevForge where the workflow fit is strong.
Reasoning infrastructure
Package the reusable layer as an API and partner distribution story so the company expands beyond any one workflow.
Moat + validation
Built to be benchmarked, auditable, and hard to copy.
Public record
- Foundation papers make the category easier to understand and harder to ignore.
- Benchmark publications increase credibility with customers and investors.
- IP becomes easier to defend when it is tied to a visible framework and loop.
Technical moat
- Order-gap / convergence control layer.
- Model-agnostic reasoning loop above frontier models.
- Evaluation harness for long-context and regulated workflows.
Pilot proof
- Publish benchmark deltas where possible.
- Show review-time and exception-handling improvements.
- Demonstrate auditable reasoning in live regulated workflows.
Contact Team