Overview
Enterprise RLVR Training Platform for Next-Gen AI Agents
Build, train, and deploy sophisticated AI agents with our production-ready Reinforcement Learning infrastructure, MCP server orchestration, and advanced trajectory management system.
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- 350+Enterprise Data Sources
- 15%+Reasoning Performance Gain
- 10-50sFull Trajectory Execution
- SOC 2Type II Certified
Reinforcement Learning with Verifiable Rewards
Move beyond preference optimization to objective correctness. Our RLVR platform enables frontier models to master complex reasoning, planning, and tool-calling tasks.
Why RLVR Over Traditional RLHF?
While RLHF excels at subjective preference alignment, complex reasoning tasks demand objective correctness. RLVR provides clear, binary reward signals based on verifiable outcomes—perfect for code generation, mathematical proofs, and multi-step planning where there's a definitive right answer.
Traditional RLHF/DPO
- Subjective human preferences
- Good for tone and style
- Requires extensive human comparison
- Ambiguous "better" criteria
Our RLVR Approach
- Objective correctness signals
- Perfect for logic & reasoning
- Automated verification at scale
- Binary correct/incorrect feedback
Verifiable Reward Systems
Custom verifier and solver programs provide objective, automated reward signals for training on tasks with clear correctness criteria.
- Binary correct/incorrect signals for objective tasks
- Automated verification for code generation
- Mathematical proof validation
- Complex planning verification
- SQL query correctness checking
- Multi-step reasoning validation
Systematic Prompt Generation
Create vast sets of diverse training data through our proven methodology that varies linguistic, structural, and parametric dimensions.
- Structure variation (paragraphs, bullets, tables)
- Tone & phrasing diversity (formal, casual, technical)
- Syntax variations ($ vs USD, 14:00 vs 2pm)
- Constraint presentation strategies
- Information ordering permutations
- Placement of critical data points
Trajectory Management Suite
Advanced tools for creating, editing, and annotating agent trajectories with built-in quality assurance and performance optimization.
- Multi-step reasoning path editor
- Tool call sequence optimization
- Message-level classifications
- Global trajectory evaluation
- Error pattern identification
- Performance bottleneck analysis
Model Optimization Engine
Leverage improved trajectories for prompt optimization and model fine-tuning with proven performance gains.
- MIPRO prompt optimization
- Trajectory-based fine-tuning
- A/B testing framework
- Multi-model comparison
- Performance regression detection
- Automated hyperparameter tuning
Supported Training Algorithms
PPO (Proximal Policy Optimization)
Industry-standard RL algorithm optimized for stable training on complex reasoning tasks.
ReLoRA
Efficient parameter updates using Low-Rank Adaptation for faster convergence.
DPO + RLVR
Direct Preference Optimization enhanced with verifiable rewards for objective tasks.
Custom Reward Functions
Design domain-specific reward signals tailored to your unique use cases.
Enterprise RLVR Training Process
Domain Definition
Collaborate to identify critical reasoning domains your models need to master
Prompt Generation
Create diverse problem variations with systematic linguistic and structural diversity
Trajectory Creation
Generate and refine agent trajectories with expert human-in-the-loop annotation
Verification & Rewards
Apply automated verifiers to provide objective reward signals for RL training
Model Optimization
Fine-tune models and optimize prompts using validated trajectory data
Evaluation & Deploy
Comprehensive testing across models before production deployment
Enterprise MCP Server Infrastructure
Self-hosted, secure, and scalable Model Context Protocol servers connecting your LLMs to 350+ enterprise data sources with SQL-based universal access.
The Universal Action Layer for Enterprise AI
We package every external system—Salesforce, NetSuite, Jira, and 350+ more—into dedicated, SQL-native MCP Servers that your LLM can query or mutate through a single, standardized interface. Think of it as a universal "action layer" that gives your AI agents the power to act on your business data.
Production-Ready MCP Orchestration
Our platform provides complete infrastructure for deploying and managing MCP servers at enterprise scale. From Docker orchestration to API key management, we handle the complexity so you can focus on building intelligent agents.
Self-Hosted MCP Architecture
How MCP Servers Work
LLM Issues MCP Call
Your chosen LLM (GPT-4, Claude, etc.) determines it needs to access external data and issues an MCP call with specific parameters.
Gateway Routes Request
MCP Gateway receives the request and routes it to the appropriate containerized MCP Server based on the target system.
Server Translates to Native API
MCP Server translates the standardized call into native SQL/REST operations against your source system (Salesforce, Jira, etc.).
Results Flow Back
Query results are formatted and returned to the model. The entire dialogue and tool payloads are logged for RL replay and analysis.
Technical Implementation Details
Container Management
- Immutable Docker images per task
- AWS ECR or private Docker registry
- Version-controlled deployments
- Automated image building pipeline
Security Architecture
- Encrypted secrets management
- User-level permission enforcement
- OAuth/SSO/Kerberos support
- Audit trail for all operations
Performance Optimization
- Query pushdown to source systems
- Parallel paging for large datasets
- Connection pooling
- Response caching strategies
Universal SQL Layer
- Consistent SQL interface
- Automatic schema discovery
- Type-safe operations
- Cross-system JOIN support
350+ Supported Enterprise Systems
CRM & Sales
Salesforce, HubSpot, Pipedrive, Microsoft Dynamics, and more
ERP & Finance
NetSuite, SAP, Oracle, QuickBooks, Workday, and more
Project Management
Jira, Asana, Monday.com, Linear, Trello, and more
Communication
Slack, Microsoft Teams, Gmail, Outlook, Discord, and more
Analytics & BI
Tableau, Power BI, Looker, Google Analytics, and more
Cloud Infrastructure
AWS, Azure, GCP, Kubernetes, Docker, and more
Advanced Trajectory Editor
Create, edit, and optimize agent trajectories with our multimodal chat editor designed specifically for training and evaluating AI agents.
Purpose-Built for Agent Training
Our trajectory editor, powered by Labelbox's Multimodal Chat Editor, provides a comprehensive environment for creating and refining the exact behaviors you want your agents to learn. Every tool call, reasoning step, and response can be meticulously crafted and evaluated.
Classifications
Core Editing Capabilities
Step-by-Step Refinement
Edit each reasoning step, tool call, and observation in your agent's trajectory. Optimize decision-making paths and improve overall performance.
Message Classifications
Apply granular classifications at the message level: planning errors, tool call errors, and custom evaluation criteria for your specific use case.
Global Evaluations
Assess entire trajectories for exploration depth, factual accuracy, safety compliance, and task completion quality.
Performance Analytics
Track improvements across iterations with detailed metrics on reasoning quality, tool usage efficiency, and task success rates.
A/B Testing Framework
Compare different trajectory approaches side-by-side. Test variations in reasoning strategies and tool selection patterns.
Expert Annotation Network
Leverage our global network of expert AI trainers for high-quality trajectory annotation and refinement at scale.
Comprehensive Evaluation Framework
Global Trajectory Metrics
- Task completion rate
- Reasoning coherence score
- Tool usage efficiency
- Safety compliance rating
- Response quality assessment
Message-Level Analysis
- Planning error detection
- Tool call parameter validation
- Reasoning step classification
- Information accuracy check
- Context relevance scoring
Custom Rubrics
- Domain-specific criteria
- Business logic compliance
- Brand voice consistency
- Regulatory adherence
- Performance benchmarks
Seamless Workflow Integration
Import Trajectories
Load existing agent conversations or generate new ones using your current models
Edit & Annotate
Refine reasoning paths, optimize tool usage, and add evaluation labels
Train Models
Use improved trajectories for RLVR training and prompt optimization
Measure Impact
Track performance improvements and iterate on your training data
Platform Key Capabilities
End-to-end RL infrastructure designed for enterprise-scale agent training and deployment
Advanced RL Training
Trajectory-Aware Training
Reward correct reasoning chains through sophisticated trajectory analysis. Our system evaluates and optimizes multi-step agent behaviors to ensure reliable task completion.
LLM-as-Judge Rubric Evals
Fast iterative prompt and policy optimization using advanced LLM evaluation techniques. Automated scoring accelerates the development cycle while maintaining quality standards.
Verifiable-Reward RL
Objective reward signals for tasks with clear correctness criteria including code generation, mathematical proofs, and SQL queries. Binary verification ensures accurate learning.
Multi-Algorithm Support
Comprehensive support for PPO, ReLoRA, and DPO training methods, followed by RLVR optimization. Recent deployments achieved 15% improvement in agentic task success.
Enterprise MCP Infrastructure
Instant Connectivity
Spin up MCP servers via Docker & Modal in seconds. Our optimized container orchestration ensures rapid deployment and scaling for any workload.
Secure by Design
SOC 2 Type II, ISO-27001, and GDPR compliant infrastructure with fine-grained user permission scopes. Enterprise-grade security without compromising functionality.
Full Read/Write Access
Agents can execute read/write SQL operations, stored procedures, and trigger complex workflows across 350+ enterprise systems through a unified interface.
Universal Tool Schema
Single ReAct tool signature regardless of data source, simplifying RL reward design and enabling consistent agent behavior across all connected systems.
Data Generation & Evaluation
Prompt Diversification
Systematic variation of structure, tone, ordering, and constraints generates thousands of distinct planning challenges for comprehensive agent training.
Expert Trajectory Labeling
Using Labelbox Multimodal Chat Editor, expert annotators grade each reasoning step, tool call, and final answer for correctness, safety, and completeness.
Continuous Evaluation
Self-hosted MCP Eval platform re-runs historical tasks across new model versions, automatically surfacing regressions before production deployment.
Reproducible Testing
Each task pinned to immutable Docker images guarantees replayable evaluation across model versions, ensuring consistent benchmarking and comparison.
Technical Specifications
Enterprise-grade infrastructure built for scale, security, and reliability
Infrastructure
- Self-hosted deployment options
- AWS ECR Docker registry
- Modal.com VM orchestration
- TypeScript backend architecture
- RESTful API endpoints
- Horizontal scaling support
Security & Compliance
- SOC 2 Type II certified
- ISO/IEC 27001:2022 compliant
- GDPR compliant
- OAuth, SSO, Kerberos support
- User-level permission enforcement
- Encrypted API key storage
Performance
- 10-50s full trajectory execution
- Query pushdown optimization
- Parallel paging support
- Streaming mode for large datasets
- Bulk operation capabilities
- Rate limiting protection
Integration
- 350+ enterprise data sources
- Universal SQL access layer
- Read/write operations
- Stored procedure support
- LiteLLM model abstraction
- Python evaluation scripts
Training Features
- RLVR objective rewards
- MIPRO prompt optimization
- Trajectory fine-tuning
- Multi-model evaluation
- Expert annotation network
- Automated verification
Monitoring
- Real-time trajectory tracking
- Performance analytics
- Error logging and debugging
- Usage metrics and reporting
- Custom alert configuration
- Audit trail capabilities
Proven Enterprise Use Cases
Real-world applications delivering measurable business impact
Complex Financial Analysis
A major investment firm uses our platform to train agents that analyze market data across multiple systems, perform complex calculations, and generate investment recommendations with verifiable accuracy.
Result: 15% improvement in analysis accuracy
Manufacturing Process Optimization
Global manufacturer deployed agents trained on our platform to optimize supply chain decisions by reasoning across ERP, inventory, and logistics systems in real-time.
Result: 22% reduction in planning time
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Healthcare Workflow Automation
Healthcare provider trained agents to navigate complex patient data systems, insurance databases, and clinical guidelines to streamline administrative workflows.
Result: 40% faster claim processing
E-commerce Personalization
Retail giant uses RLVR-trained agents to analyze customer behavior across channels, inventory systems, and marketing platforms for hyper-personalized recommendations.
Result: 28% increase in conversion rate
Legal Document Analysis
Law firm trained agents to reason through complex legal documents, case databases, and regulatory systems with verifiable citation accuracy.
Result: 60% reduction in research time
R&D Knowledge Synthesis
Pharmaceutical company deployed agents to synthesize research across internal databases, clinical trials, and scientific literature with objective verification.
Result: 3x faster literature reviews
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