Domain-Specific Model Compression
Take any foundation model. Teach it your domain. Compress it to run on a laptop. No GPUs. No API costs. No data leaves the device.
60-70%
Size Reduction
30B params → 18GB GGUF
95%+
Capability Retention
On domain-specific tasks
25+
Tokens/Second
On M4 Max MacBook Pro
$0
Per Inference
After deployment
The Problem
$0.01-0.15 per query. At scale: $50K-500K/year. Plus latency, rate limits, and your customers' data flowing through third-party servers.
Requires GPU servers at $10K+/month. Models are general-purpose – mediocre at your specific domain tasks. DevOps overhead is massive.
Better quality but still needs expensive GPU infrastructure. Models are too large for edge or local deployment. Ongoing hosting costs never end.
No GPUs. No API costs. No data leaves the device. One fixed price.
Middle-Out Compression
Kore's self-learning compression pipeline discovers optimal quantization paths that human engineers would never find manually.
Kore profiles the foundation model against your domain data – support tickets, product docs, sales calls, internal terminology. It maps which neural pathways are critical for your use case and which are expendable.
Capability circuit mapping identifies the 30-40% of model parameters that matter for your domain.
QLoRA fine-tuning on your domain corpus teaches the model your language, your edge cases, your patterns. GRPO reinforcement learning optimizes for your specific quality criteria.
Your data never leaves our secure training environment. Models are trained, not stored.
The core innovation. Adaptive Precision Distillation gives critical pathways 6-8 bit precision while compressing everything else to 2-3 bits. A self-learning loop compresses, evaluates, adjusts – finding quantization paths humans would never discover.
ForgeMonitor predicts quality impact before each step. No surprises.
GGUF export for cross-platform deployment. MLX variant for Apple Silicon. Desktop runtime with auto-update. SDK for embedding directly in your product.
Delta updates mean model refreshes download in under a minute.
Adaptive Precision
Traditional quantization treats every weight the same. Kore identifies which neural pathways matter for your domain and preserves them at full precision – while compressing everything else aggressively.
# Precision allocation for a Legal AI model
Result: 65% size reduction. 97% legal reasoning retention. Runs on a MacBook Air.
Use Cases
Contract analysis, case research, compliance review
Eliminate $200K/yr API costs. Keep client data on-device for attorney-client privilege.
Clinical decision support, medical coding, chart review
HIPAA compliance by default – patient data never leaves the device.
Risk analysis, regulatory compliance, trading signals
Sub-millisecond inference. Zero data exfiltration risk.
Code completion, bug detection, documentation generation
Offline capability. Zero-latency autocomplete. No telemetry.
Ticket classification, response drafting, knowledge base search
$0.00 per inference after deployment. Infinite scale at fixed cost.
Deal analysis, objection handling, competitive positioning
Our own use case. Adrata runs on Kore.
Architecture
Kore handles the entire pipeline. You provide domain data. We deliver a production-ready local model with auto-update infrastructure.
┌─────────────┐ ┌──────────────┐ ┌───────────────────┐ ┌─────────────┐
│ Your Data │───▶│ Domain │───▶│ Middle-Out │───▶│ GGUF/MLX │
│ (encrypted) │ │ Fine-Tune │ │ Compression │ │ Export │
└─────────────┘ └──────────────┘ └───────────────────┘ └─────────────┘
▲ │
┌────────┴────────┐ │
│ ForgeMonitor │ ▼
│ (self-learning │ ┌─────────────┐
│ quality gate) │ │ S3 + CDN │
└─────────────────┘ │ Delivery │
└─────────────┘
│
▼
┌─────────────┐
│ Desktop │
│ Runtime │
│ + SDK │
└─────────────┘Your data is encrypted in transit and at rest. Trained on isolated GPU instances. Data is purged after model delivery.
Contractual SLA: less than 5% capability degradation on domain tasks. A/B testing framework included. Automatic rollback if quality drops.
Model refreshes download in under a minute via delta updates. Desktop runtime handles versioning, rollback, and auto-update.
Pricing
One subscription. Your model gets smarter every month. No per-token charges. Ever.
$5,000/mo
One domain model for teams getting started with local AI.
Most Popular
$15,000/mo
Multiple models with monthly freshness for production use.
$35,000/mo
Unlimited models with continuous learning and dedicated engineering.
$75K+/mo
White-label the quantization pipeline. Build Kore into your product.
Setup fee: $10,000 (waived for annual contracts). All plans include initial model delivery within 4 weeks.
Why Us
Adrata's own sales intelligence platform runs on a Kore-compressed model. We're not selling you theory – we're selling you the engine that powers our product.
Kore powers Adrata's local AI for deal intelligence, behavioral analysis, and multi-agent orchestration. We eat our own cooking.
Adrata's intelligence corpus – millions of data points from commercial environments – provides unmatched domain training data.
The AWS pipeline, the compression algorithms, the delivery infrastructure – all built and battle-tested on our own models.
We sell to the same CROs, VP Sales, and engineering leaders who would buy Kore. We know what matters: cost, privacy, performance, simplicity.
Get a domain-optimized model running on your hardware in 4 weeks. Fixed cost. Infinite inference. Your data stays yours.