Your AI pilot works. Let's get it into production.

We take stalled AI pilots and proofs of concept into production, including ones we did not build. Three AI agent deployments are running in production today.

Getting an AI pilot into production is an engineering job, not a research one. The model usually works. What is missing is everything around it: retrieval that works on your real documents, a test set that proves the answers are right, a way to deploy and monitor it, proper error handling, and a clear owner for what the system tells customers. AMT (Advanced Millennium Technologies) has built AI systems since 2013 and is ISO 27001:2022 certified.

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// why_pilots_stall

Why AI pilots stall before production

The demo went well and everyone was pleased. That was months ago. It is still not live, and the question in leadership meetings has quietly changed from when does it launch to what happened.

Here is the thing. This is the normal outcome, not a rare one, and it is almost never because the team was weak or the technology was wrong. A pilot and a production system are two different pieces of engineering:

A pilot proves the idea can work.
A production system proves it is safe, affordable, accountable and correct, including on inputs nobody prepared for.

The second job is bigger, and most of it is invisible until you start.

// five_questions

Five questions to check if your AI pilot can still reach production

Answer these with your team before you talk to any vendor. In our experience, the answers predict most of the remaining work.

Was it tested on all your real documents, or a hand-picked sample? If the pilot only searched a small, clean set of documents, the hardest part has not been tested yet: finding the right answer across your full, messy document library.
Do you have a scored test set? This is a fixed list of real questions with known correct answers, used to grade the system after every change. Without one, nobody can tell whether a change made things better or worse, so nobody dares change anything. That is often why the project stopped moving.
Can it change anything in your systems, and who approved that? A read-only pilot feels safe but proves little. Deciding what the AI is allowed to do, such as update a record or issue a refund, is where launch plans most often stall.
What happens with the hardest 5% of inputs? Unusual questions, bad data, edge cases. If nobody has checked, that is the work, and it is usually about a third of what remains.
Who is accountable for what it tells customers? If the answer is the AI team, your legal team has probably not reviewed it yet.

// twelve_weeks

AI pilot to production in 12 weeks: what happens and what you get

AI pilot to production in 12 weeks, week by week
Weeks	What we do	What you get
1 to 2	Assessment. We review the code, the data, any testing approach and the deployment path, and talk to the people who built it.	A written assessment of what to keep and what to replace, with an honest recommendation, including restart if that is the right answer. You own it either way.
3 to 4	Real data and a test set. Connect retrieval to your full document set and build the scored test set.	A baseline: for the first time, a number that shows how accurate the system really is.
5 to 8	Close the gaps. Usually error handling, edge cases, integrations and access control.	A system that behaves well on inputs the pilot never saw.
9 to 10	Governance. What the AI may and may not do, human review steps, audit logs, and the answers legal and security will ask for.	The approval pack that gets you through internal review, which is often the real blocker.
11 to 12	Controlled release. Internal users or a small customer group, with monitoring and a way to roll back.	A live system with a measured baseline and a plan to widen access.

Twelve weeks is the typical case, not a promise. A pilot with clean data and one integration moves faster. A pilot built on sample data against three undocumented systems takes longer.

The assessment is yours to keep

Weeks one and two produce a document you own whether or not you continue with us, even if it recommends stopping. When a project is in this position, an honest second opinion is worth more than another vendor pitch.

Get the five questions as a one-page assessment

Run it with the team that built the pilot before you bring anyone in. It shows where the project is really stuck and makes your next vendor conversation much shorter.

// ai_services

AI services that take a pilot the rest of the way

AI services that take a pilot into production
Service	What it covers
AI agent development	Agents that complete tasks in your systems, with clear limits on what they may change.
RAG development	Retrieval over your real documents, with answers traceable to the source passage.
Conversational AI	Chat agents across web and WhatsApp that handle real transactions, not just FAQs.
Voice AI development	Natural conversation on the phone line, in front of or instead of an IVR menu.
Generative AI development	Content, summarisation and extraction features built into your products and workflows.
MCP server development	Model Context Protocol servers that give AI models safe, structured access to your tools and data.

// original_team

Working with your original team or vendor

Usually the original team is still there, and usually they were not the problem. We work alongside them by default. They know things no document captured, and a rescue that sidelines the internal team creates a different problem three months later.

If another vendor built the pilot, we judge the work on its merits, not on who built it. Often a lot of it can be kept.

// results

AI in production: our results

Telecom conversational AI agent, live on a carrier network. 70% of customer queries resolved without a human agent, under 3 seconds average response, handling bill payments, plan upgrades and SIM management through live calls into billing, CRM and provisioning.
AI insights agent inside a commercial SaaS product. Shipped as part of a product customers pay for, not an internal tool. 35% higher flow completion.
Across our AI agent deployments. Three in production to date, with an average cost reduction of 40%.

// governance

Our AI governance commitments

Your data is never used to train anything outside your engagement.
No AI-generated code ships without explicit human review.
IP, including prompts, models and pipelines, is assigned to you in the contract before work starts.

// cost

AI implementation cost: how it is priced

AI pilot to production cost
Stage	Pricing model	What drives the effort
Assessment (weeks 1 to 2)	Fixed price, 2 to 3 weeks	Number of systems, volume of code, and whether any testing approach already exists. You keep the output.
Pilot to controlled release	Scoped after the assessment (medium to high effort)	Number of integrations and the state of your data, far more than the AI model itself.
Ongoing AI team after launch	Monthly	Engineers, an architect and QA, sized to what you run.
Typical budget range	25 to 35 USD per hour	Higher end for AI architects and governance work, lower end for integration and QA.

// not_right_fit

When we are not the right fit

// faqs

FAQs: AI pilot to production

Why do AI pilots fail to reach production?

Usually because nothing was built around the model: no connection to the full real data, no test set, no deployment path, no rules on what the AI may do, and no clear owner for its answers. The model itself is rarely the problem.

What is the difference between an AI POC, a pilot and production?

A proof of concept shows the idea can work on sample data. A pilot puts it in front of a small group of real users. Production means it runs on all your real data, for everyone it is meant for, with monitoring, governance and a clear owner.

What is RAG, and why does it matter in production?

Retrieval-augmented generation (RAG) means the AI looks up your own documents before answering, so answers are grounded in your content and can cite their source. In production, the quality of that retrieval decides most of the accuracy.

What is an AI evaluation test set?

A fixed list of real questions with known correct answers, scored automatically after every change. It is how you prove an AI system is getting better instead of guessing.

Can our AI pilot be rescued, or should we start again?

Most can be rescued. The two-week assessment gives you a straight answer, including restart when that is the cheaper route.

How long does it take to move an AI POC to production?

About 12 weeks to a first controlled release. Data quality and the number of integrations affect the timeline more than anything else.

What if another vendor built the pilot?

We assess the work on its merits. The first output is an honest view of what can be kept, and often a lot can.

How much does it cost to take an AI pilot to production?

The assessment is fixed price and can be bought on its own. After that, cost depends mainly on integrations and data condition.

Tell us where your pilot stopped

30 minutes with an engineer who has taken AI into production on a live carrier network. You will leave knowing what stands between your pilot and a release, whether or not you work with us.

Not ready to talk? Read how we deliver. No form.