Question 1

What kinds of products do you integrate AI into?

Accepted Answer

Existing B2B SaaS, internal tools, and customer-facing web apps — anywhere your team already has APIs, auth, and a deployment pipeline. We focus on in-product features: copilots, RAG over your data, workflow automation, and tool-calling against your product APIs.

Question 2

Do you replace our stack or integrate into it?

Accepted Answer

We integrate into it — AI inside your product, not a sidecar tool or platform rewrite. We add middleware, services, and UI in your repo and deploy through your existing CI/CD. No platform migration, no separate vendor console your team has to operate. Your databases, identity provider, and observability stack stay in place.

Question 3

How long does a typical engagement take?

Accepted Answer

A technical audit and architecture proposal usually takes one to two weeks. A first production feature often ships in four to eight weeks depending on scope, data readiness, and review cycles. Larger rollouts are broken into incremental milestones behind feature flags.

Question 4

Who owns the code after you ship?

Accepted Answer

You do. Everything lands in your repository with tests, runbooks, and handoff documentation. We design for your team to operate, extend, and review changes — not for ongoing dependency on us, though we can stay on for iteration and expansion.

Question 5

How do you handle security and data privacy?

Accepted Answer

Auth boundaries match your existing RBAC, prompts and context are scoped per tenant where needed, and we design for audit logging on sensitive actions. Data handling follows your policies — we don't train models on your customer data unless you explicitly require it.

Question 6

Which LLM providers do you support?

Accepted Answer

OpenAI, Anthropic, Google Gemini, and self-hosted models via an abstraction layer in your codebase. That lets you swap or route providers without rewriting product features — useful for cost control, compliance, or failover.

Question 7

We already built a POC — can you productionize it?

Accepted Answer

Yes. That's a common starting point. We assess what's there, harden the integration path (rate limits, observability, evals, fallbacks), and get it behind proper auth and deployment practices so it survives real traffic and your eng team's review bar.

Question 8

How is pricing structured?

Accepted Answer

Scoped engagements — typically a fixed fee per phase (audit, build, operate) based on complexity and timeline. We'll outline options after the technical assessment so you have a clear estimate before committing to implementation work.

Guides & articles

Build an agent with LangChain — a practical tutorial

AI integration services — what we build and how we deliver

RAG without the platform rewrite

What production-ready LLM integration actually means