Policy-Aligned AI Without Cloud Dependency

Train SLMs on your policies, not on risky cloud APIs or contaminated historical data. Compliant by design. Edge-deployed. In a matter of hours.

☁️ Cloud LLMs Expose Data Your conversations live on OpenAI/Anthropic servers. Regulators don't approve. Latency kills user experience.

📊 Historical Data is Contaminated Support logs contain policy violations, bias, outdated procedures. Legal will block this project.

🛡️ Guardrails Aren't Enough Runtime filtering is expensive. You're validating a broken model, not fixing one.

⏳ Data Cleaning Takes 6 Months You'll never be certain it's compliant. You'll never get Legal approval.

See the Problem See the Solution

Why Enterprises Can't Use Cloud LLMs or Historical Data

Cloud LLMs Hide Costs, Not Just Price

You're paying per token. Your customer conversations live on OpenAI/Anthropic servers. Your proprietary workflows? Logged. Your compliance audits? Show data sovereignty violations. For regulated industries—healthcare, finance, insurance—this is a blocker, not an option.

Hidden cost: 75% more expensive than edge-deployed SLMs over time
Data residency: Can't promise regulators your data stays within your infrastructure
Latency: API calls are 1000ms+. Local inference is 200ms
Integration friction: Each new policy change requires prompt engineering, not retraining

Historical Data is Contaminated. Always.

You assume your support logs, call transcripts, and past conversations are training-ready. They're not. That data isn't a record of "what should happen"—it's a record of "what happened," including mistakes, violations, and outdated procedures.

Quality problem: Real conversations include policy violations, employee errors, inconsistent responses
Compliance blocker: HIPAA prohibits training on patient data (even de-identified). GDPR requires explicit consent. PCI-DSS prohibits real transaction data
Bias liability: Historical hiring data encodes discrimination. Lending decisions encode redlining. Your AI inherits the sins of the past
Proof problem: Can you certify to auditors that 100,000 conversations contain zero violations? No. That's why Legal blocks these projects
Time cost: 3-6 months of data engineering. $50k-$100k in costs. And you'll never be certain it's clean

Why Guardrails Alone Don't Work

So you add runtime validation to catch bad responses after they're generated. But that's filtering a broken model, not fixing it. High rejection rates destroy UX. You're paying cloud LLM costs + guardrails overhead.

The real gap: 44% of enterprise AI projects fail at the 80%→95% accuracy gap—between POC and production-ready. Guardrails can't close that gap. The model has to be trained right from the start.

The SLM-Based Alternative: Policy-Aligned Training

Instead of training on contaminated data or depending on cloud APIs, start with your policies. Extract them. Generate clean, policy-aligned training data from them. Train a small, edge-deployable SLM on that data. Deploy it within your infrastructure. Compliance by design, not by filtering.

Extract Policies

Upload your enterprise documents—guidelines, compliance rules, SOPs. Platform extracts structured policies via Knowledge Graph analysis.

Generate Data

Create 1,400-3,500 synthetic training pairs from policies using our CSJ pipeline. 2-Judge validation ensures every example is policy-compliant.

Train Model

SLM (3-7B parameters) trained on constitutional data. Constitutional alignment baked in from the start—not added later via filtering.

Monitor Compliance

Deploy to your infrastructure. SDK runs locally. Every response validated against live policies. Audit trail stays with you.

Hours

Data generation, not months of data engineering

Days

Not Months to Production

75%

Cheaper Than Cloud LLMs

Why AlignGenie Is Different

Policy-Driven, Not Data-Driven

Start with policies (not historical logs)
Generate clean training data by design
Constitutional alignment from the start
No data engineering required

Legal-Approved Methodology

Policies = your legal ground truth
No real customer data in training
Full provenance tracking
Defensible for regulatory audits

Cost-Competitive at Scale

Hundreds, not tens of thousands for data engineering
75% cheaper than cloud LLM APIs
No ongoing per-token fees
Works with open-source SLMs

Fast Time-to-Production

Hours, not months to first model
No data engineering overhead
Policy updates without retraining
Ready for immediate testing

The real difference: We solve the "how do we get compliant training data?" problem that blocks 90% of enterprise AI projects. Guardrails platforms validate outputs. Fine-tuning platforms assume you have data. We generate the data from policies and handle the entire lifecycle—policy extraction, training, and runtime validation.