Ship AI Services Enterprise Customers Will Trust
Service-to-service companies struggle to reach production-ready AI accuracy in regulated industries. Yonvs provides the evaluation infrastructure to systematically reach 70%+ accuracy in weeks, not months.
Built for compliance-heavy industries
Why Most AI Services Never Reach Enterprise
The friction between compliance requirements and LLM limitations creates an invisible barrier to production deployment
- • Interesting demos for early adopters
- • Product teams excited about potential
- • Works in controlled notebooks
- • Cannot sell to enterprise customers
- • Enterprise customers sign contracts
- • Legal and compliance departments approve
- • Reliable performance in production
- • $40K-$120K annual recurring revenue
Service companies work with vast, constantly evolving datasets: legal documents, financial transactions, medical records
LLMs hallucinate. Compliance requirements demand accuracy. But how do you measure improvement when your evaluation data changes daily?
Cannot establish baseline. Cannot prove improvement. Cannot demonstrate compliance to regulators.
Stuck at 40% accuracy with no systematic path forward
How Problems Compound
A single root cause cascades through your development process, creating a chain reaction that prevents production deployment
How Service Companies Reach Enterprise-Ready AI
Snapshot-based evaluation infrastructure that turns unreliable measurements into systematic improvement
Create Stable Baseline
Immutable snapshot with content-addressed hash
Take a snapshot of your evaluation dataset. This creates an immutable copy that cannot change.
Run Baseline Evaluation
Test your agent on frozen data
Establish your starting point with a reproducible measurement. Same data, same result, every time.
Iterate Systematically
Change one variable, measure exact improvement
Make one change to your agent. Test on identical snapshot. The improvement is signal, not noise.
Reach Production Ready
Track progression to 70% threshold
Each iteration runs on identical data. Each improvement is measurable. Production threshold reached systematically.
Why This Enables Service Companies to Sell to Enterprise
Reach 70% in 8-10 Weeks
Systematic improvement path from prototype to production-ready accuracy
Pass Compliance Requirements
Demonstrate to regulators exactly what data produced each result with reproducible evidence
Close Enterprise Contracts
Legal departments approve when they can verify system reliability and accuracy
Zero Infrastructure Overhead
Focus on your AI service, not managing snapshots and evaluation infrastructure
Data Snapshot Versioning
Version your data like you version code, enabling truly reproducible AI evaluations
Git-like Branching
Create immutable snapshots from any point in your data timeline
Guaranteed Immutability
Content-addressed hashes ensure data never changes after snapshot
Parallel Experimentation
Multiple teams run experiments on identical data simultaneously
Zero Storage Overhead
Snapshots store only diffs, not full copies of data
Why Snapshots Solve the Reproducibility Problem
- • Data changes between evaluations
- • Cannot reproduce previous results
- • Improvements vs noise unclear
- • Teams block each other's work
- • Identical data every evaluation run
- • Same hash = same result guaranteed
- • Clear signal of real improvement
- • Parallel experimentation enabled
Real Companies, Real Results
Service-to-service companies using Yonvs to reach production accuracy
40% accuracy on internal benchmark
Reproducible evals to reach 70% before selling to enterprises
Automated code generation
4 weeks infrastructure time saved, 2 months faster to production
42% accuracy in pilot
70% for enterprise sales ($40K-$120K contracts)
Attorney-client contract management
2 months faster time-to-market, enables enterprise contracts
38% accuracy on validation set
70% for publishable results and grant funding
Plasma control predictions for fusion reactors
3x experiment throughput, enables publication
The Pattern
Five Layers of Reproducible Evaluation
Purpose-built infrastructure for systematic AI improvement
End-to-End Data Flow
Data flows from immutable Snapshots through the Evaluation Harness, tracked in the Improvement Dashboard, executed via Serverless Compute, and deployed through Agent Orchestration. Every step is reproducible, traceable, and production-ready.
Install & Evaluate
pip install Yonvs-ai
import Yonvs
snapshot = Yonvs.snapshot.create("eval-data-v1")
result = agent.evaluate(snapshot)Get Started Today
Join enterprise teams already using Yonvs to ship production-ready AI agents faster.