technology
Scaling software with hybrid intelligence.
WHAT IS A DLM?
DLM (Deterministic Language Model) is Byggr’s proprietary technology designed to generate a fully structured, production-ready code base: zero hallucinations, predictable code generation, built-in compliance.
HOW OUR DLM WORKS
- SYSTEM MODEL-BASED AS A PROMPT
Ingests business logic, database schemas, UI model, and architecture blueprints to write code.
- DETERMINISTIC EXECUTION
Given the same input, DLM always produces the exact same correct output.
- BUILT-IN SECURITY & COMPLIANCE
Ensures compliance with internal/industry standards, and enterprise best practices.
- PRE-VALIDATED CODE OUTPUT
Ensures every function, class, and module follows SOLID, DRY, and scalable architecture principles.
1class PaymentProcessor:
2 def __init__(self, gateway):
3 self.gateway = gateway # ✅ Uses predefined, validated class
4
5 def process_payment(self, amount, card_details):
6 secure_token = self.gateway.encrypt(card_details) # ✅ Valid function
7 return self.gateway.charge(secure_token, amount)No hallucinated functions – Uses only defined methods and structures
Well-structured, modular, and maintainable
Follows dependency injection & encapsulation best practices
DLM VS. LLM
LLMs guess, so results can vary and errors are common. Byggr’s Deterministic Language Model generates precise, consistent, production-ready code, using LLMs only where they add value.
features
dlm (byggr hybrid ai)
llm (gpt-40, claude, gemini, etc.)
processing logic
Uses structured, rule-based execution
Uses probabilistic pattern matching
code accuracy
100% accurate, pre-validated code
May hallucinate functions or logic
context awareness
Retains full system architecture
Limited to short-term context window
security & compliance
Enforces security policies automatically
Can introduce vulnerabilities
scalability & maintainability
Consistent, structured output every time
Code may be inconsistent across sessions
customization & adaptability
Structured but allows developer control
Flexible for exploratory coding
1def process_payment(amount, card_details):
2 secure_token = encrypt_card(card_details) # ❌ Fake function, does not exist
3 gateway = PaymentGateway() # ❌ Undefined class
4 return gateway.charge(secure_token, amount)"encrypt_card()" and "PaymentGateway()" do not exist.
The LLM guessed the function names, leading to runtime errors.
OUR HYBRID APPROACH
Byggr doesn’t choose between LLM or DLM — we use both in a structured, scalable workflow to maximize their individual strengths.
WHY USE A LLM WHEN WE HAVE A DLM?
- UNDERSTANDING UNSTRUCTURED INPUT
LLMs interpret natural language descriptions, PRDs, wireframes, and pseudo-code.
DLM takes those interpretations and converts them into clean, production-ready code. - GENERATING SYSTEM MODELS
Instead of manually architecting applications, LLMs propose best-fit data models and high-level system designs, helping teams visualize structure, dependencies, and workflows before a single line of code is written.
- EXPLORATORY PROBLEM-SOLVING
Some decisions (SQL and REST) require multiple considerations — LLMs evaluate options.
Data Model → Users, Messages, Chats
API Design → Authentication, WebSockets, Message history
Where the DLM takes over
- Ensures code accuracy
Converts LLM-generated blueprints into structured, error-free code.
- Enforces best practices
Implements SOLID principles, modular design, and clean architecture.
- Prevents hallucinations
Ensures every function, class, and module is logically sound and executable.
1class ChatService:
2 def __init__(self, repository):
3 self.repository = repository # ✅ Uses structured, validated dependencies
4
5 def send_message(self, user_id, chat_id, text):
6 user = self.repository.get_user(user_id)
7 chat = self.repository.get_chat(chat_id)
8 if chat and chat.is_active:
9 self.repository.save_message(user, chat, text) # ✅ Predefined repository function
10 return True
11 return FalseThe LLM provided the blueprint, but DLM ensured structural correctness and maintainability.
Our Hybrid Workflow
Byggr doesn’t choose between LLM or DLM — we use both in a structured, scalable workflow to maximize their individual strengths.
Extracts system requirements, relationships, and dependencies (LLM)
LLM creates an initial draft of the system model (LLM)
Converts the structured system model into production-ready code (LLM)
Developers can modify logic, integrate APIs, or extend functionality (LLM)
Proposes high-level architecture and component interactions (LLM)
DLM validates, refines, and structures the model to ensure correctness (DLM)
Prevents hallucinations, enforces security and compliance, and maintains modular architecture (LLM)
DLM safeguards developer modifications, ensuring future AI-generated updates don’t overwrite custom code (LLM)
Contribute requirements in multiple formats (Human)
Verify probabilistic outcome and contribute to the system model creation (Human)
Verify code output (Human)
DLM safeguards developer modifications, ensuring future AI-generated updates don’t overwrite custom code (DLM)
Extracts system requirements, relationships, and dependencies (LLM)
Proposes high-level architecture and component interactions (LLM)
Contribute requirements in multiple formats (Human)
LLM creates an initial draft of the system model (LLM)
DLM validates, refines, and structures the model to ensure correctness (DLM)
Verify probabilistic outcome and contribute to the system model creation (Human)
Converts the structured system model into production-ready code (LLM)
Prevents hallucinations, enforces security and compliance, and maintains modular architecture (LLM)
Verify code output (Human)
Developers can modify logic, integrate APIs, or extend functionality (LLM)
DLM safeguards developer modifications, ensuring future AI-generated updates don’t overwrite custom code (LLM)
DLM safeguards developer modifications, ensuring future AI-generated updates don’t overwrite custom code (DLM)
THE POWER OF HYBRID AI IN SOFTWARE DEVELOPMENT
Byggr Studio’s LLM + DLM hybrid model isn’t just a technical choice — it’s a strategic innovation in AI-driven development.