AI Systems
40%
higher close rates
- 18 dayssales cycle, down from 45
- $1.2Mstalled pipeline recovered
NexusCRM
AI-native CRM decision system
Lead scoring, outreach drafting, and pipeline risk detection were rebuilt around AI decision support.
AI feature acceleration
Add useful AI features without creating unbounded product risk.
Define the decision, evaluate the behavior, and ship AI that has a clear fallback when confidence is not enough.
- 20–30 minute review
- No preparation needed
- Clear decision boundary
- higher close rates
- 40%
- sales cycle, down from 45
- 18 days
- stalled pipeline recovered
- $1.2M
NexusCRM · AI-native CRM decision system
Organizations we’ve worked with
- Airbnb
- TheAMLab
- Hood
- NAMA
- Klaro
- GoBoon
- Studio Arlington
Where AI features stall
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.
The diagnosis
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.
- 01AI bolted on without a decisionA chat box added because the roadmap said “AI” — no one can say which decision the feature actually owns.
- 02No eval harnessQuality judged by screenshots and vibes; nobody can say whether the last prompt change made it better or worse.
- 03No fallbackWhen the model is unsure it answers anyway — there is no confidence gate and no human lane behind it.
- 04Latency and cost surprisesThe feature feels fine in testing, then p95 latency and the first API invoice land in production.
- 05Prompt spaghettiPrompts patched on prompts, edited live in production, with no versioning and no regression tests.
- 06Stuck at prototypeThe feature works in a notebook and a demo, but it has never survived real users inside the product.
The bounded-feature model
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.
How the feature stays bounded
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
The honest boundary
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
In production
Systems already in production
Real engagements delivered by the same capability — measured after go-live.
WalmartAI Systems
Device failure detection with machine learning
92% prediction accuracy and 65% lower device downtime.
Reel CreatorAI Systems
AI video generation pipeline
100,000+ reels generated while production time dropped by 85%.
Pick your feature
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.
Shipped naively
- 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
Bounded and evaluated
- 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.+ your existing stack
- OpenAI
- Anthropic
- Azure OpenAI
- LangChain
- Pinecone
- Postgres
- React
- Next.js
- .NET
- AWS
- Vercel
How it works
The capabilities that deliver it
AI Feature Acceleration is built from three engineering capabilities working together.
Primary
Product Engineering
Engineering the feature into your existing codebase, data model, and UX with the same rigor as the rest of the app.
Existing codebaseUXData model
Explore capabilitySupportingAI Systems
The defined decision, evaluation suite, confidence gate, and fallback that keep the feature bounded.
Defined decisionEvalsConfidence gate
Explore capabilitySupportingIntegration
Connecting the feature to the data, services, and workflows your product already runs on.
Live dataServicesWorkflows
Explore capabilityDo the math
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.
252000
130
Hours the feature would absorb
40hrs/week
≈ 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.
How we deliver
From prototype to production feature
Four steps, and each one hands you a concrete artifact — not a status update.
- 01 · Define
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 and its boundary are agreed in writing
- 02 · Evaluate
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
- 03 · Ship
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 bar and the fallback fires
- 04 · Observe
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 quality, latency and cost budgets
Is this the right starting point?
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
Start here- 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
Not yet- 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? Most teams aren’t — until the workflow is mapped.
AI Feature Acceleration questions
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.
Prototypes prove the idea; production demands an eval suite, a confidence gate, a fallback, and integration with your existing data and UX. That gap is exactly the work we do — taking the promising prototype and engineering it into something the product can ship, monitor, and stand behind.
No. We work alongside your team to accelerate one AI feature into production, engineering it into your existing codebase and handing back something instrumented and maintainable. You keep ownership of the product.
We bound the feature to one defined decision with validated outputs, put a confidence gate in front of users, and back it with an eval suite and telemetry. You get behavior you can measure and predict, plus signals when something drifts, rather than a black box in front of your users.
VPs of Product, CTOs, and CPOs with an existing product who are adding a real AI capability. It is not for AI with no defined product problem, demo-only builds, or teams without a product to ship into.
Start here
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