Question 1

How do you integrate AI into existing products safely?

Accepted Answer

We start with threat modeling and data classification, then introduce retrieval and tooling behind feature flags. Observability, eval harnesses, and human review gates ship alongside the model—not after.

Question 2

What engagement models do you support?

Accepted Answer

Dedicated product pods, staff augmentation, and advisory retainers. Most teams begin with a 2–4 week discovery sprint that produces a roadmap, architecture sketch, and fixed-scope MVP proposal.

Question 3

Can you work within our compliance requirements?

Accepted Answer

Yes—SOC2, HIPAA, GDPR, and industry-specific controls are part of our default playbook. We align on policies, logging, and vendor boundaries before writing production code.

Question 4

What does your delivery stack look like?

Accepted Answer

TypeScript-first on the frontend, polyglot services where justified, infrastructure as code, trunk-based development, and automated preview environments for every change.

Question 5

How do you measure success?

Accepted Answer

We co-author OKRs and engineering KPIs—latency budgets, error budgets, conversion deltas, and model quality metrics—then review them weekly with your stakeholders.

Question 6

Do you provide ongoing support after launch?

Accepted Answer

We offer SRE-backed SLAs, on-call rotations, and continuous improvement sprints so your platform keeps pace with traffic, regulation, and model upgrades.

Straight answers about how we work

Everything you might ask before a discovery call

Ready to Build the Future with AI?