AI · Deep dive 02
AI-Driven Product Features
Generative, retrieval-augmented, agentic — designed with the UX that keeps users trusting the output instead of second-guessing it.
What we build
Production AI features shipped inside your product: copilots, smart suggestions, RAG-driven search, agentic workflows. Built with guardrails, evaluation harnesses, and the UX patterns that make AI helpful instead of unnerving.
Does this sound familiar?
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Your users are asking 'why doesn't this have AI?' and leadership is asking the same thing.
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You shipped a chatbot and usage is 5% of what was predicted.
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Every AI feature feels bolted on — a separate tab, not part of the product.
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Engineers built a RAG prototype. It works 60% of the time. Nobody knows how to get to 95%.
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Users don't trust the AI output enough to act on it.
The customer payoff
What your users feel
What you feel once it’s running.
AI features embedded in the product flow, not in a side-panel ghetto.
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Trust-building UX patterns — citations, confidence levels, human override.
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Evaluation harnesses so quality regressions fail builds.
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Cost + latency dashboards that stay green under production load.
Phases
⏱ 6–12 weeks typicalHow AI-Driven Product Features actually runs.
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01
Prototype
Days, not weeks, to a rough prototype of the feature. We throw away the bad versions early."
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02
UX the trust
Design the interaction patterns that make users trust (or appropriately distrust) the output. Citations, confidence, edit paths, undo."
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03
Harden
Evaluation suite, guardrails, fallback paths, cost caps. Production rigour."
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04
Ship + iterate
Behind a feature flag. Watch real-user metrics; iterate on prompts + UX weekly."
The hand-off
In production
What lands in your hands — every artefact, nothing hidden.
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AI feature(s) shipped in your product
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Prompt library with versioning
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Evaluation test suite (prompt regression)
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Cost + latency dashboards
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Fallback paths (cheaper model, deterministic, or graceful degrade)
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UX documentation of the patterns used
The usual questions
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Q·01 Which model do you use by default?
Claude for nuanced generation + long context. GPT-4o-class for fast structured output. Open-weights when cost or privacy demands. Routed per task."
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Q·02 What about hallucinations?
Addressed at the UX layer (citations, confidence, explicit 'AI might be wrong') and the eval layer (regression testing). We don't pretend hallucinations don't happen; we design around them."
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Q·03 Can this be done cheaply?
Often. Many features work with smaller models + prompt engineering rather than fine-tunes. We'll start cheap and only escalate if the quality floor demands it."
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Q·04 What does 'production-grade' actually mean?
Eval suite running in CI. Latency + cost budgets. Error monitoring. Rate limiting. Fallback paths. The boring stuff that turns a demo into a feature."
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Q·05 Do we need an ML team?
Not usually. Most of this is prompt engineering + software engineering, not ML training. If fine-tuning is warranted we'll say so — rarely is."
Ready to start
Ship AI users actually use.
Start with the feature you'd ship first. We'll prototype in days, harden in weeks, and ship to real users.
Start an AI featureThe wider map
Every service page at a glance.
Each link below opens a dedicated page on that specific piece of one of our four service pillars. Jump sideways — different service, same way of working.
Digital Product Strategy
Service overview →Web & Mobile Development
Service overview →Business Automation
Service overview →AI Integration
Service overview →- 01 AI Opportunity Mapping
- 02 AI-Driven Product Features — you’re here
- 03 AI-Powered Automation
- 04 Evaluations, Guardrails & Observability
- 05 Vendor-Neutral Integration