Custom SLMs & Edge Deployments
Private, hyper-specialised AI models.
Stop sending sensitive proprietary data to third-party APIs. We fine-tune open-source Small Language Models (SLMs) to outperform massive models on your specific tasks, deployed entirely within your VPC or on disconnected edge hardware.
Delivery Snapshot
100%
data privacy
< 50ms
edge inference
10×
smaller model, same accuracy
Expected outcomes outcomes
Measurable results that improve delivery speed, resilience, and ROI.
Why teams choose us
Models built for your domain, running on your infrastructure.
Total data privacy
Your data never leaves your infrastructure — zero third-party API exposure.
Domain-specialized accuracy
Fine-tuned models that beat general-purpose models on your specific tasks.
Low-latency inference
Quantized models run at millisecond latencies on commodity hardware.
Core capabilities
Custom models that run where your data lives — cloud, on-prem, or edge.
LoRA/QLoRA fine-tuning
Adapt open-source foundation models to outperform GPT-4 on your specific domain tasks.
Open-source foundation models are adapted to your domain with efficient fine-tuning pipelines and measurable quality gains.
- Domain dataset curation and split strategy
- Parameter-efficient tuning and validation
- Benchmark tracking against base model
Model quantization
Run capable models on edge hardware with 4-bit quantization and optimized inference engines.
Models are compressed for target hardware while preserving response quality in real-world scenarios.
- Hardware-aware 4-bit/8-bit quantization plans
- Latency and memory profiling per deployment target
- Quality regression checks post-quantization
Air-gapped deployment
Deploy models in fully disconnected environments — no internet dependency, complete data sovereignty.
We deploy secure inference stacks in isolated environments for organizations with strict sovereignty and compliance requirements.
- Offline packaging for model and runtime artifacts
- Private registry and update workflow controls
- Network isolation and access governance
Where it applies
Practical scenarios that map to measurable outcomes.
Regulated industries
Healthcare, finance, and government requiring full data sovereignty.
- HIPAA-compliant AI
- On-premise deployment
- Audit trails
Edge computing
AI on manufacturing floors, remote sites, or embedded devices.
- Offline-capable inference
- Low-power hardware
- Real-time processing
Domain-specific NLP
Legal, medical, or technical language that generic models handle poorly.
- Custom taxonomy training
- Multi-lingual support
- Terminology accuracy
How we work
A focused, milestone-driven approach that keeps momentum and clarity.
Requirements & data audit
Requirements & data audit
Define target tasks, evaluate training data quality, and select base models.
Fine-tuning & evaluation
Fine-tuning & evaluation
Train with LoRA/QLoRA, benchmark against baselines, and iterate on quality.
Optimization & quantization
Optimization & quantization
Compress models for deployment target (cloud GPU, CPU, edge device).
Deployment & monitoring
Deployment & monitoring
Deploy on your infrastructure with drift detection and retraining triggers.
Frequently asked questions
Answers to common project and collaboration questions.
What are Small Language Models (SLMs)?
Can fine-tuned models run without internet access?
How much training data do I need?
Ready for AI that runs on your terms?
Let us build a custom language model optimized for your domain and deployed on your infrastructure.