Hands-on engineering for AI product teams
Your AI prototype works. Get it ready for customers.
AZdev provides senior production AI engineering for teams with a working prototype and a customer delivery to make. We build the integration, evaluation and operating controls around the model, then hand over a system your team can run.
Engineering contracts from $200 USD/hour. Based in Toronto, working with teams across North America.
When an outside engineering team is worth hiring
A customer pilot has a deadline, the existing team owns the core product, and nobody owns the path from a useful demo to a supported release. That is a defined delivery problem with a reason to fund it.
- The demo works on selected examples but fails on customer data.
- Retrieval or agent tools need access to systems with tenant-specific permissions.
- A model change improves one workflow and breaks another, with no release test to catch it.
- Latency, model spend or manual review makes the product difficult to operate.
If there is no agreed user workflow or budget owner yet, start with product discovery. Adding engineers before those decisions can make an expensive prototype more elaborate without making it more useful.
What the engineering engagement delivers
A release decision your team can defend
Define the workflow, the unacceptable failures and the evidence required to ship. Build evaluation cases from the actual customer task, with a held-out set for release decisions.
Application and data integration
Connect retrieval and tools to your existing product. Carry user and tenant permissions through each operation, and make consequential actions subject to explicit controls.
An operable production service
Implement tracing, timeouts, failure handling and cost measurement. Record which model, prompt and data version produced a result so an incident can be investigated.
A controlled rollout and a handover
Agree the initial user group, fallback behavior and rollback conditions. Deliver code, deployment instructions and a runbook your engineers can maintain.
For a dedicated evaluation or grader problem, see agent evaluation engineering. For governed predictive models in finance, see ML production and model risk.
Related delivery work
Enterprise audience intelligence
An anonymized engagement connecting a knowledge graph, AI-assisted audience discovery and permissions across an enterprise platform.
Read the platform case study
Latent Sciences
ML research infrastructure connecting experiments to on-demand GPU resources. Relevant infrastructure experience for teams moving beyond a local prototype.
Read the infrastructure case study
Engagement questions
Do we need a fractional CTO to hire AZdev for AI engineering?
No. An engineering engagement has a defined technical scope and works with your existing CTO or engineering lead. Fractional CTO work is a separate engagement for companies that need someone to own the roadmap, hiring and wider technical decisions.
Can you work with our existing engineering team?
Yes. We agree ownership of a workstream, work in your repositories and review process, and plan the handover with the engineers who will operate the system. The scope can begin with an assessment when the production blockers are still unclear.
How much does production AI engineering cost?
Senior engineering starts at $200 USD/hour. We agree capacity and scope before work starts. A fixed-fee assessment or delivery milestone is quoted against explicit deliverables, dependencies and acceptance criteria. Model usage, cloud costs and software licenses are budgeted separately.
Is this only for financial services?
No. We work with AI product teams and companies adding AI to an existing product. Our published work includes ML infrastructure for Latent Sciences and an anonymized enterprise audience-intelligence platform, alongside payments and financial services engineering.
Can you guarantee that an AI system will never make a mistake?
No. We define which errors the workflow can tolerate, which actions need approval, and when the system should stop or fall back. A release decision needs evidence about those boundaries. A promise of perfect accuracy is not useful acceptance criteria.
Bring the workflow and the release blocker
Tell us who will use it, what must work before launch and who will own the system afterwards. We can scope the engineering from there.
Discuss an engineering contract