Customer-Facing AI Product Build
Claude inside the product your customers use.
What it is
Claude built into the product or service your own customers use.
Who it's for
Software companies adding AI capability to their product, and service businesses building AI into their customer experience.
What we deliver
Four phases, no surprises.
Discovery
We map your workflows, identify what is worth doing, define success metrics, and audit what we will be working with. No assumptions.
Design
We design the approach in full — including model selection, integrations, and controls — before a line of code is written.
Delivery
We build with regular demos and continuous feedback. You see progress, you give input, we adjust.
Handover
Documentation, training, and a clean handover to your team — with ongoing support available if you want it.
Every engagement is scoped to your situation, so timelines and investment depend on what you actually need. We will tell you honestly what we think makes sense — including when a smaller scope is the right answer.
Frequently Asked Questions
What is involved in adding Claude to a customer-facing product?
Nisco AI Systems covers product and interaction design for AI features, the production build, safety and moderation controls, latency and cost optimization for customer-scale traffic, evaluation and quality monitoring, and post-launch iteration. Customer-facing AI carries reputational risk that internal tooling does not, so abuse and moderation controls are part of the build rather than an afterthought.
Do you work alongside our engineering team or replace it?
Either. Nisco AI Systems builds alongside a client’s engineering team when there is capacity and appetite to own the system afterwards, and delivers end to end when there is not. Documentation and handover are included in both cases.
How do you control cost when AI features hit consumer volumes?
By treating cost as an engineering constraint from the design stage. Nisco AI Systems applies model selection matched to each task, prompt caching, batching where latency allows, and per-feature cost monitoring, so unit economics are visible before a feature reaches full traffic rather than after the first large invoice.
Talk to an Anthropic-credentialed team.
30 minutes, no sales pitch. Bring the problem, not a feature list.