AI for Code Generation

Emissary

AI Infrastructure for Code Generation

Build language models that generate syntactically correct, semantically valid code. Train with compiler feedback, unit tests, and specialized retrieval systems for your programming languages and codebases.

Compiler & Test-Driven Model Training

Train models for specialized programming languages using RL/GRPO with compiler-driven syntactic feedback and unit test-driven semantic validation. Generate code that actually compiles and passes tests.

Syntactic correctness via compiler feedback loops
Semantic validity through unit test execution
Domain languages - COBOL, Verilog, CUDA, proprietary DSLs
GRPO optimization for multi-objective code quality metrics

Intelligent Tool-Calling & Agent Selection

Build better tool-calling and sub-agent selection models using our proprietary classification LM retraining. Make reliable decisions about when and how to use tools, APIs, and specialized agents.

Tool selection accuracy - Know which tool to use when
API parameter generation - Correctly formatted calls every time
Agent orchestration - Route to specialized models optimally
Confidence scoring - Know when to escalate or retry

Customized Code Context Retrieval

Train retrieval models that understand your codebase structure, dependencies, and patterns. Surface the right context at the right time for more accurate code generation.

Semantic search across entire codebases and documentation
Dependency-aware context selection
Cross-file and cross-repository understanding
API usage pattern recognition and examples

Specialized Code Embedding Models

Build embedding models that capture code semantics, not just syntax. Understand function behavior, design patterns, and architectural concepts for superior similarity search and clustering.

Function-level semantic understanding
Cross-language code similarity detection
Design pattern and anti-pattern recognition
Vulnerability and code smell detection

Measurable Code Quality Metrics

Real performance data on syntactic correctness, test pass rates, and code quality

Compilation Rate

Track syntactic correctness with compiler validation - know your model's success rate

Test Pass Rate

Measure semantic correctness through automated test execution

Quality Metrics

Cyclomatic complexity, maintainability index, and custom metrics

Specialized Use Cases

Legacy Language Modernization

Train models on COBOL, Fortran, or proprietary languages with limited training data. Use compiler feedback to ensure generated code maintains business logic.

Hardware Description Languages

Generate Verilog, VHDL, or SystemVerilog with synthesis tool feedback. Ensure timing constraints and resource utilization targets are met.

Domain-Specific Languages

Build models for your proprietary DSLs, configuration languages, or query languages with custom parser and validator integration.

Test Generation

Create models that generate comprehensive test suites with coverage feedback, property-based tests, and fuzzing inputs.

Enterprise-Ready Code AI Infrastructure

Multi-Language

Support for 50+ languages including proprietary and legacy

Continuous Learning

Models improve from code reviews and CI/CD feedback

Secure Deployment

On-premise or VPC deployment for code privacy

IDE Integration

VSCode, JetBrains, Vim, and custom editor plugins

What Engineering Teams Achieve

95%+

Syntactic correctness

80%+

Test pass rate

Developer velocity

50%

Reduction in bugs

Advanced Training Techniques

State-of-the-art methods for building production-grade code generation models

RL/GRPO Training

Reinforcement learning with compiler and test feedback for multi-objective optimization

Classifier Retraining

Transform LLMs into true classifiers for reliable tool and agent selection

Contrastive Learning

Build embeddings that understand code semantics beyond surface syntax

Ready to Build Code AI That Actually Works?

Join engineering teams deploying models that generate correct, tested, production-ready code.