An independent review of the prototype inside two weeks
Enterprise engineers, the same team from review to launch
Ongoing support once the rollout is live
Why prototypes stall
Why a Working AI Prototype Rarely Reaches Production
The prototype is not the problem. Somebody on your team connected a model to your data, showed it to the leadership and got a yes. Then the questions started: who owns it, what it costs at ten thousand users, what happens when it answers wrongly, and whether security has looked at it.
That is the gap we close. We read the prototype as it is, tell you in writing what it needs to run as an enterprise system, and then build exactly that with engineers who have shipped production software since 2006.
What you will not get from us is a rewrite for its own sake. If the prototype's approach is sound, we keep it and harden it. If it is not, we say so with the reasons, before you spend another quarter on it.
Does this sound familiar?
Six Signs an AI Prototype Is Not Ready for Production
If three of these apply, the prototype needs an independent review before anyone promises a rollout date. If one does, it is still worth a conversation.
It Runs on One Laptop
The notebook, the API keys and the sample data all live with the person who built it. Nobody else has run it end to end, and there is no repository to hand over.
Nobody Can Say What It Costs
The demo ran on a free tier. At real volume the token spend, the vector store and the retries add up, and nobody has modelled the bill per user or per conversation.
The Demo Data Is Not Your Data
It answers from a spreadsheet export. Production means live reads from the ERP, the CRM or the commerce platform, with permissions that match who is asking.
Security Has Not Looked at It
Customer data goes into prompts, the model can be talked into ignoring its instructions, and there is no audit trail of what it said to whom.
The Answers Change Between Runs
It worked in the demo and failed in the next one. There is no evaluation set, so nobody can say whether a prompt change or a model update made it better or worse.
There Is Nobody to Call
The person who built it has a day job, or a new one. When the model provider changes an API or the answers drift, there is no team on call and no runbook.
How it works
How We Take an AI Prototype to Production
Code, Prompts and Architecture
Data, Privacy and Access
Security and Guardrails
Cost and Scale at Real Volume
Evaluation Suite
Guardrails and Permissions
Integrations on Live Systems
Staged Rollout
Quality and Cost Monitoring
Model and API Updates
Named Engineers on Call
Documentation and Handover
The review, in writing
What the AI Prototype Review Produces
2 weeks to the review1 team to support
By the end of the review you hold one document. It says what the prototype does and how, which parts are sound and which are not, what it would cost per month at your real volume, where customer data goes and whether that is allowed, and what has to change before an enterprise rollout.
Every finding carries a fix and an estimate, so the review is a budget you can approve rather than a list of concerns. Where the honest conclusion is a different approach, the review says so and shows the numbers beside it, before you commit a team to the build.
It is written for your own engineers as much as for ours. Some clients hand it to the team that built the prototype and keep us for the parts that need production depth, such as the evaluation suite, the integrations or the rollout.
Either way, the review is the plan the build follows, so nothing in it is written to be read once and filed.
Verified reviews
What Our Clients Say
2.1s
faster largest contentful paint
This team consistently goes above and beyond in terms of helping to ascertain the most viable and feasible outputs and outcomes for our evergrowing business needs.
Anthony Perrotta
E-Commerce Operations Manager
4 yrs
as their sole technical partner
They designed for where the product was going, not just what we asked for. Early on we described a single-company setup; they built the data model and permission layer so multi-tenancy wouldn’t require a rewrite later.
Alex Tee
CTO
What we take to production
The AI Prototypes We Turn Into Production Systems
Most start as one person's proof that a model can do the job. Production means the same job done for every user, every day, inside your security and cost limits.
Customer-facing assistants
Sales and support agents that answer from your catalog, your orders and your policies, with guardrails on what they may say and do.
Internal copilots
Assistants over your documents, tickets and data for sales, operations and finance teams, with access that follows each user's own permissions.
Document and ticket automation
Classification, extraction and drafting over invoices, claims, applications and support queues, with a human review step where the risk needs one.
Catalog and content pipelines
Product descriptions, attributes, translations and images generated at scale, checked against your rules before anything is published.
Integrations with your systems
Live connections to the ERP, the CRM, the commerce platform and the data warehouse, so the model reads current data instead of an export.
Evaluation and cost control
Evaluation sets, regression runs on every change, and reporting on quality and spend per use case, so the rollout can be defended with numbers.
Three ways in
Three Ways to Start
Independent Prototype Review
A fixed-scope, two-week read of what your team built. You get the written review with a finding, a fix and an estimate per item, and you decide who carries it out. It stands on its own if you go no further.
Code and architecture
Security and data
Cost at real volume
Written review
Prototype to Production
We take the prototype through the review's plan to an enterprise rollout: evaluation suite, guardrails, integrations on live systems and a staged launch. The engineers who read the code are the engineers who ship it.
Evaluation suite
Guardrails and access
Live integrations
Staged rollout
Ongoing Support
Once it is live, we run it with you: monitoring on quality and spend, model and API updates as the providers ship them, named engineers on call and a runbook your own team can follow.
Quality monitoring
Model updates
Cost reports
Named engineers
Trusted by 100+ companies
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Before You Call, Read These
FAQ
Frequently Asked Questions
Still have questions?
Tell us about your store and we will come back with straight answers.
Talk to us
What counts as an AI prototype for this service?
Anything that proves a model can do a job for your business and is not yet safe to put in front of every user: a notebook, a chatbot built on a weekend, an internal tool one team already relies on, or a vendor demo you want to own instead of rent. If it runs and someone in the company wants it rolled out, it qualifies.
What does the independent review look at?
Six things: the code and architecture, the prompts and the model dependency, the data the prototype reads and writes and who may see it, the security posture including prompt injection and data leakage, the cost per user or per conversation at your real volume, and the operations side, meaning monitoring, updates and who is on call. Each finding carries a fix and an estimate.
How long does the review take, and what do we need to provide?
Two weeks from access. We need the repository or a copy of the code, a demo environment or a recording, and a thirty minute call with whoever built it. Where the prototype reads company data we also ask for a description of that data and who is allowed to see it. An NDA is signed before we see anything.
Can you review the prototype without taking the project over?
Yes. The review is written so your own engineers can carry it out, and some clients do exactly that. Others hand us the production build and keep the person who built the prototype as the product owner. Either works, and the review does not change depending on which you choose.
Will you tell us to throw the prototype away?
Only if the numbers say so, and then we show them. Most prototypes are sound in their approach and short on everything around it: evaluation, access control, integrations and operations. We keep what works and build the rest. A rewrite is recommended when the architecture cannot meet the cost, latency or security limits you have, and the review says why in plain language.
What changes between a prototype and a production system?
The model is the same; everything around it is new. An evaluation set that measures every change before it ships. Guardrails on what the system may say and do. Access that follows each user's own permissions. Live integrations instead of exports. Monitoring on quality and spend. A rollout plan that starts small. And a team on call when the provider changes something at two in the morning.
Which models and platforms do you work with?
The ones your prototype already uses, unless the review shows a reason to change. That covers the major hosted model providers, open models you run yourself, and the commerce, ERP and CRM platforms the system has to read from. We do not sell a platform of our own, so the recommendation is not tied to one vendor.
How do you handle customer data and compliance?
The review maps where every piece of personal or confidential data goes: into prompts, into logs, into a vector store, to a third party. Production then enforces the rules: data minimisation and anonymisation before anything reaches a model, retention limits on logs, access scoped to the asking user, and an audit trail. For regulated data we document the controls so your compliance team can sign off.
How do you control the cost of running it?
By measuring it before the rollout and watching it after. The review models the spend at your real volume. The production build adds caching, routing cheaper models where they are good enough, limits per user and per conversation, and a cost report per use case. The staged rollout means the first real bill arrives for one team, not the whole company.
How do you know the system is still working after launch?
The same evaluation set that gated the launch runs on a schedule and on every change, and production conversations are sampled and scored. Quality, latency and spend sit on one dashboard with alerts. When a provider updates a model, the suite runs first and the update ships only when the scores hold.
What does ongoing support include?
Monitoring on quality and spend, model and API updates applied as providers ship them, prompt and evaluation maintenance as your products and policies change, named engineers on call with agreed response times, and a runbook your team can follow. The same people who built it stay on it, so nobody starts from scratch when something changes.
How quickly can a prototype be in production?
The review takes two weeks. The build depends on what it finds: a prototype that already sits on your systems is usually in a staged rollout within six to ten weeks of the review, and one that needs new integrations or a different architecture is planned as its own project with its own dates. The review gives you the timeline for your prototype rather than an average.
Can you work alongside the team that built the prototype?
Yes, and we prefer it. They know the problem, the data and the users. We add the production engineering, the security and the operations, and the review names who does what. Many rollouts end with the original builder as the product owner and our engineers on support.
Do you build AI systems from scratch as well?
Yes. Our own AI Sales Agent runs on live stores, and the same team builds assistants, copilots and automation for clients. This page is for the common case where a prototype already exists; if you have the use case and no prototype yet, the first stage is a short discovery instead of a review, and the rest is the same.
Is there a price list?
No. The review is fixed in scope, and its price is agreed once we know the size of the prototype and the systems it touches. The production build and support are quoted from the review, item by item, so you approve a budget rather than a rate. Nothing on this site carries a price because no two prototypes start from the same place.
Let’s build something great together!
Have a Prototype That Works?
Book a call, send us the repository or a demo link, and we will tell you inside two weeks what it needs to run as an enterprise system, what that would cost and whether we would build on it.