AI Upload Validation for an Agri-Tourism Grant Portal
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Read the projectAI feature acceleration
Make AI useful inside the product you already have.
Add a focused AI feature with clear quality checks, permissions and fallback behaviour. Build it into a real user journey with visible usage costs.
- 20–30 minute review
- No preparation needed
- Clear feature scope
A scoped project or ongoing support—choose after review.Engineering Capacity
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
ILLUSTRATIVE WALKTHROUGH
Test the answer and the fallback
- 01
Input · incomplete support request
A user asks a question whose answer is not present in the approved knowledge source.
- 02
Expected behaviour · ask or escalate
The feature requests missing information or sends the case for review instead of inventing an answer.
- 03
Release check · representative examples
Compare outputs with agreed examples, including unsupported requests, source references and access boundaries.
Evaluate quality, latency and usage costs together. A model score alone is not proof that an answer is correct.
PUBLISHED PROJECTSee the work
See the work
behind the approach.
A published example of related work. Your systems, scope and acceptance checks are agreed separately.
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.
Planning estimate based on the inputs above, not a forecast of savings.
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
Test representative examples
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.
YOUR FIRST ENGAGEMENT
Start with one task AI can help a user complete.
Define the user decision, the available context and what a useful output looks like before choosing a model or adding a chat box.
Start with a conversation
Bring the existing journey and representative inputs, including examples where an incorrect answer matters.
An example helps; a finished specification is not required.
BEFORE IMPLEMENTATION
Know what you are agreeing to.
- A feature boundary and evaluation set
- Review, fallback and usage-control decisions
- An integration scope with release criteria
We agree the scope, delivery approach and price before implementation. Where discovery is needed, its scope and fee are agreed first.
AI outputs need evaluation and ongoing observation. Human review and fallback behaviour depend on the task and its consequences.
BEFORE YOU GET STARTED
AI Feature Acceleration questions
Scope, delivery and what happens next. The details that help you decide.
A RELEVANT WAY TO WORK WITH US
Engineering Capacity
Add engineering support to an existing roadmap, with responsibilities and delivery expectations agreed before starting.
Explore Engineering CapacityAsk about your projectDo we need an existing product?
This solution adds a focused AI capability to an existing product. If you are building a new product around AI, AI MVP Launch is the more relevant starting point.
How do you decide whether the feature is ready?
We agree representative examples, quality criteria, failure behaviour and usage limits. The evaluation includes unsuitable inputs and access boundaries as well as useful outputs. Passing these checks reduces risk but does not guarantee every future answer.
Is this a fixed feature project or extra engineering capacity?
A bounded feature can be scoped as a defined project. Engineering Capacity is relevant when your team has an ongoing roadmap and can lead priorities. We establish which arrangement fits before you commit.
What happens before implementation starts?
We review the intended outcome, existing systems and dependencies, then agree deliverables, acceptance checks, responsibilities and price. If discovery is needed first, its scope and fee are agreed separately before that work begins.
How long will the work take?
The delivery plan depends on scope, access, integrations and review dependencies. We agree milestones after that assessment rather than promise the same timeline for every system.
What access or preparation do you need?
Bring an example of the workflow or user journey and an overview of the systems involved. We agree the access needed for the work. Do not send passwords or private customer data through the enquiry form.
What happens after handover?
Handover, documentation, defect coverage and any ongoing support follow the chosen offer and written scope. New features, additional integrations and continuing maintenance are not assumed to be included.
Start here
Ship the AI feature with clear safeguards
Bring the feature you are trying to ship. We will define the task, the quality checks, 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