Architecture design
We design the platform around your specific use case, data volumes, latency requirements and compliance constraints from day one.
Services
Custom AI Platform
An AI platform shaped around your business
When off-the-shelf AI tools cannot handle your data, your compliance requirements or your scale, we build a custom platform from the ground up — owned by you, deployed where you need it.
- Purpose-built for your use case
- Runs on your cloud or on-premise
- Fine-tuned models on your data
- Full IP ownership and no vendor lock-in
- Security controls designed around your requirements
What this engagement covers
Scoped to your situation — we start with what you already have rather than a fixed package.
Model fine-tuning
Base models fine-tuned on your proprietary data to achieve accuracy levels that generic models cannot reach on your domain.
RAG pipelines
Retrieval-augmented generation over your document corpus with citation, freshness controls and permission-aware access.
Private deployment
Runs inside your AWS, GCP or Azure account — or fully on-premise. Your data never leaves your infrastructure.
Evaluation harness
Automated testing that catches regressions before they reach users. Golden datasets, A/B testing and human review workflows.
Scaling infrastructure
Auto-scaling inference, request queuing, caching and cost controls so the platform handles traffic spikes without surprise bills.
Small first, then scale what works
Six stages, with a decision point after the pilot. Nothing grows until it has shown value.
01
Workflow Audit
Understand the current manual process, systems, users, inputs and outputs.
02
Architecture
Choose the smallest practical automation architecture.
03
Pilot
Build one usable workflow.
04
Integration
Connect APIs, browser workflows, internal tools, databases, AI models or third-party systems.
05
Validation
Test reliability, edge cases, user approval and expected output.
06
Scale
Expand only after the pilot demonstrates value.
What you get
- Custom AI platform deployed on your infrastructure
- Fine-tuned models with evaluation benchmarks
- API layer for integration with your applications
- Operations runbook and team training
Tools we reach for
Chosen per project, not per habit. If your team already runs something that works, we build on it.
- Claude
- OpenAI
- Llama
- Python
- Kubernetes
- Terraform
- AWS
- PostgreSQL
FAQ
Custom AI Platform — common questions
Build custom when you need private deployment, fine-tuning on proprietary data, or control over the full stack. If a SaaS tool covers 90% of your needs, it is probably the better choice.
Other things we build
Have a workflow in mind?
Tell us about the process. We'll review it and suggest the most practical next step.