01
Measure first
We agree how quality will be judged before we build, so “better” means something.
SERVICES — AI SYSTEMS · EMBEDDED EXPERTS
Put AI to work, on your terms.
We help teams design, build and ship products with large language models. Hire us for a scoped project, or embed our experts in your team.
TWO WAYS TO WORK TOGETHERPROJECT · ONGOING
01
AI systems
- Shape
- A scoped project with a clear outcome.
- Who directs
- We run delivery. You review at each milestone.
- Best when
- You need something built, assessed or rescued.
- Ends with
- A working system, its evaluations, and a handover.
02
Embedded experts
- Shape
- One expert, or several, joining your team.
- Who directs
- You do, day to day, as with any teammate.
- Best when
- You have a roadmap and need capacity or a specialist skill.
- Ends with
- Skills and context that stay in your team.
AI SYSTEMSSCOPED PROJECTS
Find where an LLM helps, then build it properly.
Most LLM projects stall between a promising demo and something people can rely on. We work on both ends: choosing the right problem, and making the solution measurable, safe to run and affordable to operate.
We will tell you when a simpler approach is the better answer.
DISCOVERY
Use cases and feasibility
Where an LLM helps, where it doesn’t, and what a sensible first version looks like.
KNOWLEDGE
Retrieval and question answering
Search and answers over your documents and data (RAG), with attention to retrieval quality and citations.
AUTOMATION
Agents and workflows
Assistants that use tools and take actions in your systems, with limits and human approval where it matters.
QUALITY
Evaluation
Test sets, automated checks and review loops, so quality can be measured and regressions caught before users see them.
Our eval-driven method →
EFFICIENCY
Models, cost and latency
Choosing models, designing prompts and context, and using caching and routing to hit quality, speed and budget targets.
TRUST
Safety and governance
Guardrails, data handling, monitoring and audit trails for systems that touch sensitive work.
HOW AN ENGAGEMENT RUNS
01
Assess
Goals, data and constraints, ending in a written recommendation.
02
Prototype
A working slice on your real data, to learn what is possible quickly.
03
Evaluate
Measure against agreed criteria, then decide whether to go further.
04
Ship and hand over
Production hardening, monitoring, documentation and training for your team.
What happens in the first meeting, and the seven stages behind these four →
EMBEDDED EXPERTSONGOING
Experts inside your team, not outside it.
Our engineers and specialists join your team, work in your repositories, tools and rituals, and take direction from you. You get the skills you are missing without waiting on a long hiring cycle.
They share what they know as they go, through pairing, reviews and documentation, so the capability stays with you.
Need general engineering staffing rather than AI or fintech specialists? Our sister company 
D3 Engineers ↗ places engineers on your team.
ROLE
Applied LLM engineer
Builds and improves LLM features: prompts, retrieval, tool use and the code around them.
ROLE
AI product engineer
Full-stack work that turns an LLM capability into a product people can use.
ROLE
Data and platform engineer
Pipelines, storage, deployment and monitoring that keep LLM systems running reliably.
ROLE
Evaluation and quality lead
Owns test sets, metrics and review processes, and keeps quality visible as the system changes.
ROLE
Software engineers
General engineering capacity for the rest of your roadmap, the same way we already staff engineering teams.
MIX
A blend
Start with one person or a small group, and adjust the mix as your needs change.
HOW IT WORKS
01
Scope the role
What the work is, which skills it needs, and how you would like to collaborate.
02
Match an expert
We propose someone suited to the work and your team, and you meet them first.
03
Embed
They join your channels, backlog and standups, and start on your priorities.
04
Review and adjust
Regular check-ins on the work and the fit, and room to change the shape of the engagement.
HOW WE APPROACH AI WORKFOUR HABITS
02
Start small
The smallest thing that could work, tested on real data, before anything ambitious.
03
People in the loop
Where a mistake is costly, a person reviews the model’s work, and the system is built to make that easy.
04
Plan for cost
Latency and spend are design constraints from day one, not surprises after launch.
ALSO FROM FRONTIERTHE REST OF THE STUDIO
Built in-house: Azia AI, a virtual try-on product that puts generative image models to work in a browser extension, account gallery and dashboard.
See Azia AI →