Custom AI Development for Problems SaaS Can't Solve
Off-the-shelf AI tools work until they don't. When your use case is too specific, your data is too sensitive, or generic solutions hit their limits, you need custom AI development. We build purpose-built AI systems designed for your exact requirements.
Companies that have outgrown generic AI
Proprietary Data
Companies with data that can't be sent to third-party APIs
Domain-Specific Needs
Businesses with requirements generic AI doesn't address
Outgrown SaaS
Teams that have hit feature or accuracy limits on existing tools
Compliance Requirements
Organizations that need on-premise or private cloud AI
AI as Product
Products that need embedded AI as a core differentiator
Generic AI works for generic problems
Custom AI is the right choice when:
The question isn't "can we use off-the-shelf?"—it's "should we?"
Custom AI development services
Custom Machine Learning Models
- Classification and regression models trained on your data
- NLP models for domain-specific text understanding
- Computer vision models for specialized visual recognition
- Time series forecasting and anomaly detection
- Recommendation and personalization engines
LLM Customization and Deployment
- Fine-tuned language models for your domain and voice
- RAG systems with your proprietary knowledge base
- Private LLM deployment (on-premise or your cloud)
- Prompt engineering and chain-of-thought systems
- Multi-model orchestration and routing
AI-Powered Applications
- Customer-facing AI features and products
- Internal AI tools and copilots
- AI-enhanced workflows and decision support
- Real-time AI inference at scale
AI Infrastructure
- MLOps pipelines and model lifecycle management
- Model serving and scaling infrastructure
- Monitoring, logging, and performance optimization
- Data pipelines and feature engineering systems
From discovery to production
Discover
Week 1–3Deep dive into your problem space, data assets, and requirements. Explore possibilities, define success metrics, create technical roadmap.
Prototype
Week 4–6Rapid prototyping to validate approach. Test model architectures, data quality, and integration feasibility before full development.
Build
Week 7–12Full system development with iterative refinement. Weekly demos, continuous testing, adjustment based on real performance data.
Ship
Week 12–14Production deployment, integration testing, documentation, and training. Ensure your team can operate and maintain the system.
Custom AI vs. SaaS AI
| Factor | Custom AI Development | SaaS AI Tools |
|---|---|---|
| Accuracy for your use case | 90–99% (trained on your data) | 70–85% (generic) |
| Data privacy | Full control, on-premise option | Data sent to vendor |
| Integration depth | Native to your systems | API-based, limited |
| Customization | Unlimited | Vendor roadmap dependent |
| Long-term cost | One-time + maintenance | Recurring, scales with usage |
| Competitive moat | Proprietary capability | Same tools as competitors |
What custom AI delivers
improvement in model accuracy vs. off-the-shelf
reduction in per-prediction costs at scale
data control with private deployment
capabilities competitors can't replicate
Build AI That's Actually Yours
Generic AI gives generic results. If your competitive advantage depends on AI capabilities, those capabilities should be proprietary. Let's discuss what custom AI could do for your business.
Schedule a Technical ConsultationWe'll assess your requirements, data assets, and determine whether custom AI development makes sense for your use case.