Services
From Strategy to Production
We cover every phase of your AI journey. A structured process, clear deliverables, and no surprises.
Discover
Timeline: 1–2 weeks
We immerse ourselves in your business — your data, workflows, pain points, and goals. We identify where AI creates the most impact and build a prioritized opportunity map.
Deliverables
- AI readiness assessment
- Data landscape audit
- Opportunity prioritization matrix
- Executive summary & recommendation
Design
Timeline: 2–3 weeks
We architect the solution end-to-end. Model selection, infrastructure design, security requirements, integration points, and a detailed implementation roadmap.
Deliverables
- Solution architecture document
- Model selection & evaluation
- Infrastructure blueprint
- Implementation roadmap with milestones
Build
Timeline: 4–8 weeks
Custom AI solutions built to production standards. We write clean, tested, documented code that integrates with your existing systems and meets your security requirements.
Deliverables
- Production-grade AI models
- API integrations
- Custom data pipelines
- Comprehensive test suite
Deploy
Timeline: 1–2 weeks
We deploy on your infrastructure with full security hardening. Your data stays under your control, your models run in your environment, your compliance requirements are met.
Deliverables
- Production deployment
- Security hardening & audit
- Monitoring & alerting setup
- Runbook & documentation
Optimize
Timeline: Ongoing
AI systems improve over time — when managed correctly. We continuously monitor performance, retrain models, and refine outputs to keep your system at peak.
Deliverables
- Performance monitoring dashboard
- Regular model evaluation
- Drift detection & retraining
- Quarterly business review
Capabilities
What We Build
Specific technical capabilities we bring to every engagement.
LLM Deployment
Deploy large language models on your infrastructure. GPT, Llama, Mistral — model-agnostic.
RAG Systems
Retrieval-augmented generation pipelines that ground AI responses in your proprietary data.
Fine-Tuning
Custom model training on your domain data for superior accuracy and relevance.
Data Pipelines
End-to-end ETL and feature engineering for clean, reliable AI-ready data.
Process Automation
Intelligent automation that handles complex workflows, not just simple rules.
Computer Vision
Image and video analysis systems for quality control, monitoring, and classification.
Sovereign Infrastructure
On-premise and private cloud AI deployments with zero data leakage guarantees.
Compliance & Governance
AI governance frameworks, audit trails, and regulatory compliance (GDPR, HIPAA, SOC 2).
New
Get Cited by AI
AI search is replacing traditional search. Our new Generative Engine Optimization (GEO) service ensures your brand appears in AI-generated answers — built by the same team that deploys LLMs and architects RAG systems.
FAQ
Common Questions
What is sovereign AI?
Sovereign AI means running AI systems on infrastructure you own and control — your servers, your data, your models. No data leaves your perimeter, no third-party APIs process your information, and no vendor controls your AI roadmap. It combines open-source models like Llama and Mistral with on-premises or private cloud deployment.
How long does a typical AI deployment take?
A typical end-to-end deployment takes 8 to 15 weeks, from initial discovery through production launch. The discovery phase takes 1–2 weeks, design takes 2–3 weeks, build takes 4–8 weeks, and deployment takes 1–2 weeks. Simpler use cases like a single RAG system can be production-ready in as little as 4–6 weeks.
What open-source models do you support?
We are model-agnostic and deploy any open-source model that fits your use case. Common choices include Meta's Llama 3 series, Mistral and Mixtral, Qwen, Falcon, and specialized models for code, vision, or domain-specific tasks. We evaluate and benchmark models against your specific requirements before recommending a deployment.
How does on-premises AI compare to cloud APIs in cost?
According to Lenovo's 2026 Total Cost of Ownership analysis, self-hosted AI inference can be up to 18 times cheaper than cloud API equivalents over three years. The break-even point typically arrives between 3 and 6 months. After that, the marginal cost per inference approaches zero — you only pay for electricity and maintenance.
What compliance standards can you support?
We design deployments to meet GDPR, HIPAA, SOC 2, ISO 27001, and EU AI Act requirements. Because sovereign AI keeps all data processing on your infrastructure, compliance is significantly simpler — you control the entire data lifecycle with no third-party data processing agreements required for AI inference.
Do I need specialized hardware?
GPU hardware is required for AI inference. A single NVIDIA A100 or H100 server ($15,000–$40,000) handles most enterprise workloads. For smaller deployments, consumer GPUs or even CPU-only inference with quantized models can work. We assess your throughput requirements and recommend the most cost-effective configuration.