AmmaLogic
Rule-Based + LLM Technology

AI That Follows Your Rules

Our hybrid technology combines explicit rules with LLMs to create AI that's transparent, predictable, and safe for business use.

AmmaLogic Demo

1

Select Use Case

Choose a scenario to see how AmmaLogic combines rules with LLMs to create reliable AI.

Credit Application Scenario

See how AmmaLogic ensures compliance in loan applications by enforcing regulatory rules while providing natural language explanations to applicants.

Solutions

Find Your Ideal AI Solution

Our solutions combine rule-based systems with LLMs to create AI that's reliable, explainable, and effective.

Digital Engagement

Customer interactions with AI that follow your rules while providing natural language responses.

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Decision Support

Better decisions with AI that combines your business logic with data-driven insights and clear explanations.

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Process Automation

Streamline operations with intelligent automation that follows your rules while adapting to new situations.

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Digital Engagement

Our Digital Engagement solution combines rule-based guardrails with LLM capabilities to ensure every customer interaction is both helpful and compliant with company policies.

Key Features

  • 24/7 automated support with human-like responses
  • 100% compliance with company policies and regulations
  • Intelligent knowledge base integration with context awareness

Example: Refund Request

I purchased a product 3 weeks ago and it's not working. I'd like a refund.
I'd be happy to help with your refund request.
I've checked your order and confirmed it's within our 30-day refund period.
I've initiated your refund, which will process in 3-5 business days.
AI
Rule-Based Processing:
✓ Purchase date validated
✓ Within 30-day policy
✓ Auto-approved per policy
✓ Explanation provided

Decision Support

Our Decision Support solution makes AI-driven decisions transparent and reliable by combining your business rules with modern LLMs for clear, explainable insights.

Key Features

  • Rule-based processing with LLM-enhanced analysis
  • Complete audit trails for every decision
  • Plain language explanations of complex decisions

Example: Loan Application

Application Details
Loan Amount: $25,000
Term: 60 months
Credit Score: 720
Income: $72,000/year
Debt-to-Income: 28%
Decision: Approved
Decision Factors:
  • Credit Score (720) 35% impact
  • Debt-to-Income Ratio (28%) 45% impact
  • Employment History 20% impact

Process Automation

Our Process Automation solution combines the power of LLMs with rule-based constraints to automate workflows while ensuring compliance and reliability.

Key Features

  • Intelligent document processing and data extraction
  • Rule-based validation and compliance checks
  • Workflow automation with intelligent routing

Example: Invoice Processing

Invoice #12345
ABC Suppliers, Inc.
Processed
Extracted Data:
  • Vendor: ABC Suppliers
  • Amount: $4,250.00
  • Date: 2025-03-15
Rule Validation:
  • Approved vendor
  • PO match found
  • Amount matches PO

Ready to transform your business with reliable AI?

Our hybrid solutions combine the reliability of rule-based systems with the power of LLMs.

01

Rule-Based Reliability

Our AI follows explicit rules, ensuring compliance and predictable behavior.

02

Complete Transparency

Every decision has a clear, auditable trail explaining exactly how it was made.

03

Instant Adaptability

Rules can be updated instantly without retraining, adapting to new requirements.

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Core Technology

Rules + LLMs = Reliable AI

Current AI fails in critical applications because it's unpredictable. We've fixed this by combining rule-based systems with LLMs to create AI you can finally trust.

Hybrid Engine
Input Rule Engine Constraints LLM Module Flexibility Reasoning Traceable Decisions
Rule Constraints
LLM Capabilities
Hybrid Reasoning

Constraint Engine

Explicit rules derived from Prolog and expert systems ensure compliance and predictability.

→ 100% rule compliance

LLM Binding

LLMs run in rule-defined boundaries, maintaining natural language capabilities without hallucinations.

→ Flexible yet constrained

Reasoning Traceability

Every decision includes a complete audit trail showing exactly how it was made, eliminating the black box.

→ Full auditability

Output Examples

See how our hybrid system produces reliable, rule-compliant outputs

Financial Advisor

Rule-constrained output

I'd like to invest $50,000 in high-risk options to maximize my returns.

Based on your risk profile and investment goals, I can't recommend high-risk options. According to our suitability rules, we need to ensure investments match your documented risk tolerance level (moderate).

I can suggest a diversified portfolio with a moderate risk profile that still aims for growth.

Rules Applied:

• suitability_match

• risk_profile_adherence

Healthcare Support

Reasoning trail included

Can you suggest a treatment plan for this patient with hypertension and diabetes?

Based on the patient's profile (65-year-old with hypertension, type 2 diabetes, normal renal function), I recommend the following approach:

  1. ACE inhibitor (e.g., lisinopril 10mg daily)
  2. Metformin (500mg twice daily)
  3. Lifestyle modifications (DASH diet, regular exercise)
  4. Blood pressure and glucose monitoring schedule
Reasoning Trail:
  1. Applied ADA/AHA guidelines for comorbid conditions
  2. Checked for medication interactions and contraindications
  3. Verified dosages against patient's renal function
  4. Prioritized medications with dual benefits
rule_definition.ex
# Rule definition - non-technical business users can modify these
rule "compliance_check" do
  when
    transaction.amount > 10000
    transaction.customer.risk_level in [:medium, :high]
  then
    require_additional_approval(transaction)
    log_for_audit(transaction, reason: "High value + risk level")
end

# LLM integration with rule constraints
def generate_response(customer_query, context) do
  LLMEngine.generate(
    prompt: customer_query,
    constraints: [
      only_discuss_approved_products(),
      never_recommend_over_risk_threshold(),
      explain_reasoning_always()
    ],
    context: context
  )
end
A simplified view of how rule definitions constrain LLM behavior

Get Started with AmmaLogic

We're currently working with select early partners. Get in touch to discuss your specific needs.

contact@ammalogic.com
Response within 24 hours