Service — AI

AI product development, built by people who ship the boring parts too.

The demo is the easy part. We build AI products that hold up in production — with evaluation harnesses, cost ceilings, fallback behaviour, and a clear answer for what happens when the model is wrong.

The demo is the easy part. We build AI products that hold up in production — with evaluation harnesses, cost ceilings, fallback behaviour, and a clear answer for what happens when the model is wrong.

For teams past the prototype stage who need something reliable enough to charge for. Starting at $18,000. Typical timeline: 4–12 weeks.

What you get

  • A production LLM application, agent, or retrieval system
  • An evaluation harness so quality changes are measured, not guessed at
  • Token and cost instrumentation with enforced ceilings
  • Defined failure behaviour — what the product does when the model is wrong
  • Prompt and model versioning so a change can be traced and rolled back

How it works

Define correct

Before building, we write down what a good output is and how it will be measured. Projects without this ship on vibes and regress invisibly.

Build the harness with the feature

The evaluation suite is built alongside the product, so every prompt or model change is scored rather than eyeballed.

Cost and failure engineering

Token budgets, caching, model routing, and fallbacks are designed in. Unit economics that only work at demo volume are a real failure mode.

Ship and monitor

Live quality and spend are monitored after launch, because model behaviour drifts and provider pricing changes.

Related work

  • CrossRem — Multi-vendor e-commerce marketplace platform
  • GEM XD — Gamification platform to drive action and build habits

Frequently asked questions

What kinds of AI products do you build?

LLM-powered applications, agentic workflows, retrieval-augmented systems over private data, and internal AI tooling. We work across providers and recommend based on the workload rather than defaulting to one vendor.

How do you keep AI costs under control?

Token instrumentation from day one, prompt caching where the workload allows, routing cheaper models to easier steps, and hard spend ceilings. We treat unit economics as a design requirement, because a feature that is unprofitable at scale is not shippable.

How do you handle the model being wrong?

Explicitly. Every AI feature we build has defined failure behaviour — a fallback, a confidence threshold, a human review step, or a clear refusal. Deciding this at design time is the difference between a product and a demo.

Can you work with our existing product and data?

Yes. Much of this work is adding AI capability to systems that already exist, which means integration, data access, and permissions matter as much as the model. We scope those as first-class parts of the build.

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IONIAN Digital is a software development and web design agency in the New York City and New Jersey area, working with clients worldwide. Start a project or see all services.