AI engineering
Make AI useful inside your product.
We design and build AI capabilities that solve real problems for your users — with evaluation, monitoring, cost control, and privacy treated as part of the product, not an afterthought.
What it covers
What a typical engagement can include.
01
AI product discovery
Find where AI genuinely improves the product and where it would only add risk.
02
LLM & API integration
Integrate leading language models and AI services into your application.
03
AI agents & workflows
Multi-step workflows and agents with clear boundaries and human checkpoints.
04
Retrieval-augmented generation
Ground AI responses in your own documents and data.
05
Data & context architecture
Structure the context AI needs — and keep out what it shouldn't see.
06
Evaluation & quality assurance
Test sets and evaluation methods to measure output quality before and after release.
07
Monitoring & cost optimization
Track quality, latency, and spend in production.
08
Security & responsible deployment
Guardrails, data privacy, and safe failure modes.
When it makes sense
- You want to add AI features to an existing SaaS product
- You're building an AI-native product
- Your AI prototype works in demos but not reliably in production
- AI costs are growing faster than value
What shapes scope & cost
- Data availability and quality
- Accuracy and reliability requirements
- Volume of usage and cost targets
- Privacy and compliance constraints
What we need from you
- Clarity on the user problem AI should solve
- Access to representative data and examples
- Agreement on what 'good enough' output looks like
AI outputs are probabilistic and can be wrong. We design products so that's handled honestly — with evaluation, fallbacks, and human review where it matters.
Start here
Explore an AI Project.
Tell us about your product and where you are today. We'll reply with a practical next step.