For Heads of Product, CPOs and product leaders

AI features users actually keep using

Anyone can add a chat box. Shipping AI that users open again next week is a different problem — one that mixes product craft, evaluation and honest UX.

The job to be done

Ship AI features that materially move activation, retention or revenue — with a delivery cadence and quality bar that fits how your product team already works.

What actually keeps a Head of Product up

AI product bets

A short list of AI bets tied to a business metric — activation, retention, expansion — not novelty.

Honest capability framing

UX that communicates what the AI can and can't do, so trust compounds instead of erodes.

Evaluation as PM tooling

Golden sets, LLM-as-judge and A/B eval loops your PMs can actually read.

Fast iteration loop

Prompt, model and pipeline changes shipped weekly, not quarterly.

Where AI actually moves your numbers

Copilots inside your product

Contextual copilots that use your users' data, not a generic chat box.

Generative UX

Dynamic explanations, summaries and recommendations woven into existing screens.

Search that actually finds things

Hybrid semantic + keyword search with citations, filters and reranking.

Agentic workflows

Multi-step agents for onboarding, setup, migration and repetitive user tasks — with human checkpoints.

Outcomes to expect

20–40%
lift in activation on flows redesigned with AI copilots
2–3×
iteration velocity vs. treating prompts as code changes
0.7+
target user CSAT on AI features in production

Risks we take off the table

Adoption cliff

Users try it once, never return. Solved with real usage instrumentation and evaluation, not vanity demos.

Trust erosion

Wrong answers break brand. Solved with citations, confidence, guardrails and honest UX.

Prompt sprawl

50 prompts, no owner, no versioning. Solved with a prompt registry and eval-per-change.

Feature graveyard

AI features shipped, then abandoned. Solved with post-launch review gates tied to retention.

Your first 90 days with T7

Phase 1

Days 1–30 — opportunity map

Score AI opportunities against retention, activation and revenue impact; pick the flagship bet.

Phase 2

Days 31–60 — MVP + evals

Ship a working MVP behind a flag with a golden set, eval harness and clear success KPIs.

Phase 3

Days 61–90 — measured rollout

Roll out via experimentation, watch retention and CSAT, iterate weekly.

Why Head of Products pick T7

Product-embedded AI features in SaaS, healthcare, retail and fintech
Reusable copilot, search and agent components tuned for product speed
Evaluation harnesses PMs can actually read and act on
Design and engineering under one roof — no vendor handoffs on the critical path

Frequently asked questions

Do you work in-sprint with our product team?

Yes. Our squads slot into your ceremonies, planning and demo cadence — with weekly working software as the default.

How do we evaluate AI features?

We stand up golden sets and LLM-as-judge evals per feature, track them alongside product analytics and treat them as gate criteria for rollout.

Do you handle design as well as engineering?

Yes. Our teams pair PMs and designers with AI and full-stack engineers — critical for AI UX where model behaviour and interface are inseparable.

How do you avoid "chat box on everything"?

We start from the user's job-to-be-done. Sometimes that's a copilot, often it's inline generation, summarisation, ranking or agentic automation without a chat surface at all.

Ready to Build Your AI Product?

Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.

  • · AI feasibility & architecture review
  • · Product / MVP roadmap
  • · Integration & automation strategy