Ship AI features without unbounded risk.
Defined decisions, evaluated behavior, and a safe fallback when confidence is low.
- 20–30 minute review
- No preparation needed
- Clear decision boundary
AI acts alone — answer sent, action logged, evals watching.
Clean fallback — a human decides with full context. Nothing guessed.
The gap between a demo and a shipped feature
The prototype was the easy part. Here is where AI features get stuck on the way to production.
AI bolted on without a decision
A chat box added because the roadmap said “AI” — no one can say which decision the feature actually owns.
No eval harness
Quality judged by screenshots and vibes; nobody can say whether the last prompt change made it better or worse.
No fallback
When the model is unsure it answers anyway — there is no confidence gate and no human lane behind it.
Latency and cost surprises
The feature feels fine in testing, then p95 latency and the first API invoice land in production.
Prompt spaghetti
Prompts patched on prompts, edited live in production, with no versioning and no regression tests.
Stuck at prototype
The feature works in a notebook and a demo, but it has never survived real users inside the product.
All six are one problem: the feature was never defined as a decision with a boundary. Define the decision, and evals, fallbacks, and budgets all have something to attach to.
One decision, evaluated and gated
Every feature we ship follows the same shape: a defined decision, an eval suite it must pass, a confidence gate in front of your users, and a fallback when the model is not sure. That shape is the difference between a feature you ship and a risk you carry.
Four stages, built in order — the feature only reaches your users after it has earned its way through each one.
- 1Decision · The one decision the AI owns is named, with allowed inputs and outputs
- 2Evals · An evaluation suite from your real data scores every change to the behavior
- 3Gate · A confidence gate decides what is allowed to reach a user
- 4Fallback · Below the gate, a safe default or a human takes over — never a guess
Some things we will not ship into a live product, because they turn a feature into a liability. If that is what the plan requires, we will say so in the review — before any code is written.
- We will not ship unbounded agents that act without a defined decision
- We will not promise accuracy that has not been measured against an eval suite
- We will not automate judgment calls that belong with a person
The naive build vs the bounded build
Pick the feature closest to yours — how teams ship it naively on the left, the evaluated way on the right. A representative pattern; your spec will be exact.
- 1An LLM drafts replies straight from the ticket textno grounding
- 2The model answers even when it does not knowhallucinates
- 3Agents stop trusting it after the first bad replyabandoned
- 4Nobody measures suggestion qualityno evals
One bad answer · trust gone
- Suggestions grounded in your help center and ticket historygrounded
- A confidence gate — weak answers are never showngated
- The agent stays in the loop and approves each sendfallback
- Acceptance rate tracked against an eval suitemeasured
Only confident answers · trust compounds
Works with the stack you already run
No rewrite — the feature is engineered into your codebase.
The capabilities that deliver it
AI Feature Acceleration is built from three engineering capabilities working together.
Product Engineering
Engineering the feature into your existing codebase, data model, and UX with the same rigor as the rest of the app.
AI Systems
The defined decision, evaluation suite, confidence gate, and fallback that keep the feature bounded.
Integration
Connecting the feature to the data, services, and workflows your product already runs on.
How much work would this feature absorb?
Slide to your reality. This is only arithmetic — the review tells you whether the decision is bounded enough for the AI to own it.
≈ 2,080 hours a year of manual effort a bounded, gated AI feature would absorb — while the judgment calls stay human.
Assumes the step repeats year-round. We will tell you which part of it the AI can own — and which part should stay with a person.
From prototype to production feature
Four steps, and each one hands you a concrete artifact — not a status update.
Define the decision
We name the one decision the AI owns — its inputs, its allowed outputs, and where the boundary sits inside your existing product.
You get · A decision spec with an explicit boundary
Done when · The decision, its classes, and its boundary are agreed in writing
Build the eval harness
We build an evaluation suite from your real data and score the behavior against it — every model and prompt change now has a regression test.
You get · An eval suite scored on your real data
Done when · Baseline quality is measured and the quality bar is agreed
Ship behind the gate
We engineer the feature into your codebase with a confidence gate and a human fallback, so an unsure model never reaches a user.
You get · The feature live in your product, gated
Done when · The eval suite passes the agreed bar and the fallback fires correctly
Observe and tighten
We ship telemetry for quality, latency, and cost, watch real usage, and tighten prompts and the gate against the eval suite.
You get · Quality, latency, and cost telemetry
Done when · A full week runs inside the quality, latency, and cost budgets
Where AI Feature Acceleration fits
A bounded AI feature added to a live product — with a decision boundary, an evaluation, and a human fallback.
Good fit when
- You have a live product with real usage to add AI into
- There is a bounded decision the AI can own, with a clear boundary
- You have real data to evaluate the behavior against
- You want a defined human fallback when confidence is not enough
Not a fit when
- There is no product yet to build the feature into
- The AI has no measurable success criteria to evaluate against
- You want AI for its own sake, with no decision to improve
- There is no plan to instrument, evaluate, or maintain it
Not sure which side you are on?
AI Feature Acceleration questions
How is this different from just calling an LLM API in our app?
A raw API call is the easy 20%. AI Feature Acceleration is the other 80% — defining the decision the AI owns, shaping the UX, bounding the behavior with a confidence gate and fallback, evaluating quality, instrumenting telemetry, and engineering it into your existing app so it survives real usage. We ship a production feature, not a prototype behind a button.
Ship the AI feature without the risk
Bring the feature you are trying to ship. We will define the decision, the eval bar, and the fallback — and show you the path to production.
20–30 minutes · No preparation needed · Not ready for a call? Send a note instead