FAQ
The practical questions, answered.
Still deciding? Reach out and we'll walk you through how an engagement would actually work for your product.
FAQ
The practical questions, answered.
Still deciding? Reach out and we'll walk you through how an engagement would actually work for your product.
What does a typical engagement look like?
Most projects start with a short discovery phase to map your product, users, and constraints, followed by an architecture proposal before any code is written. From there we work in short iterations with regular demos, not a single big reveal at the end.
Do you work with early-stage startups or only established companies?
Both. Early-stage teams get an architecture that won't need a rewrite at Series A; established teams get help modernizing systems that have outgrown their original design.
Which industries do you have the most experience in?
We have deep experience in regulated, high-stakes domains — EHS, healthcare, fintech, insurance, and government — where auditability, data integrity, and uptime are non-negotiable. We also bring broader engineering capability to SaaS, marketplaces, hospitality, media, EdTech, logistics, e-commerce, and legal services.
How do you handle security and compliance?
Security and compliance are part of the architecture conversation from day one, not an audit bolted on afterward — covering things like access control, data residency, and audit trails depending on your regulatory environment.
Can you integrate AI into an existing product?
Yes. We typically start with retrieval-augmented generation against your own data, using whichever provider fits your stack — Azure OpenAI, OpenAI, Anthropic Claude, or others — deployed inside your existing cloud environment so it fits your security boundary rather than sitting outside it.
Is our data safe? Will it be used to train someone’s model?
Your data stays in your environment. We deploy AI inside your own cloud, VPC, or on-premise, and we don't use your or your users' data to train third-party models unless you explicitly ask us to.
What about AI hallucinations and accuracy?
This is exactly what we engineer for. We ground answers in your own data with RAG, add source citations so outputs are traceable, use structured output for reliable results, and build in human review for high-stakes steps — plus evaluation to measure accuracy over time.
Which AI model do you use?
We're provider-agnostic — Azure OpenAI, OpenAI, Anthropic Claude, or open-source models — and we choose per use case based on accuracy, cost, and privacy, rather than forcing one model onto everything.
Can you deploy AI on-premise or in our private cloud?
Yes. For sensitive or regulated workloads we can deploy inside your own cloud, VPC, or on-premise environment, so AI sits inside your existing security boundary rather than outside it.
Do we work directly with the engineers building the platform?
Yes — you work directly with the senior engineers on your project, not through an account manager relaying context back and forth.
Are you tied to a specific technology stack?
No. We're technology-agnostic and choose the right tools for each product — across .NET, Node, Python, and Go on the backend; React, Angular, Vue, and Next.js on the frontend; native and cross-platform mobile; and Azure, AWS, or GCP. For AI we're provider-agnostic across Azure OpenAI, OpenAI, Anthropic Claude, and open-source models.