Question 1

What does ASLM build for businesses?

Accepted Answer

ASLM builds custom software, SaaS products, automation, AI features and web applications for UK and US businesses — plus energy and carbon data systems. The work is founder-led: whoever scopes it is who builds it. Every build ships with security, error handling and the records an auditor would ask for, and it lives in systems the client owns.

Question 2

How do I make an AI prototype production-ready?

Accepted Answer

Six things separate a prototype from a production system. Decide what happens when the model is wrong, not just when it is right. Ground answers in your own data so they are traceable. Put limits on what the AI is allowed to do, and a human in the loop wherever a mistake is expensive. Add an automated evaluation set so you can tell whether a change improved anything. Enforce access rules in the data layer rather than in application code. And record every interaction — input, output, model version, who reviewed it — because that evidence cannot be created after someone asks for it. The model is rarely what stops a prototype. The system around it is.

Question 3

How do I choose a UK partner for production-ready AI?

Accepted Answer

Ask four questions. Show me what happens when it is wrong — a team that has shipped to production has a specific answer for a specific failure; a team that has not will describe how careful they are. Who actually writes the code, and will they still be on this in month four? What evidence will exist afterwards that a human reviewed what shipped? And what do we own at the end? Look for documents rather than intentions: a team that has crossed into production has specifications, test reports and architecture notes. One that has not will talk in the present tense about plans.

Question 4

Do you only do AI?

Accepted Answer

No. A good proportion of what we build has no model in it at all — custom software, internal tools, integrations between systems that were never joined up. When AI is not the right answer we say so, because that build is usually cheaper and more reliable.

Question 5

How do we start?

Accepted Answer

With a Reality Check: one week, a fixed fee, and a written recommendation covering where AI and automation would pay off in your business, what each would really cost, and what to leave alone. You own the report whether or not you hire us to build anything.

Question 6

Is the Reality Check a sales call in disguise?

Accepted Answer

No. It is a fixed-fee piece of work with a written deliverable, and it frequently concludes that some of what you were considering is not worth building. That conclusion is the product. If the honest answer is that you should do nothing yet, the report says so.

Question 7

Can you work with our existing developers?

Accepted Answer

Yes, and it is often the best arrangement. We bring the production discipline, your team keeps the domain knowledge, and everything we write lives in your repositories with the reasoning documented so they can carry it on without us.

Question 8

Who actually does the work?

Accepted Answer

One principal, senior throughout. Whoever scopes your work is who builds it and who answers the phone — no account managers, and no juniors learning on your systems. Capacity is deliberately limited as a result.

Question 9

What do we own at the end?

Accepted Answer

Everything. The code lives in your repositories, specified and documented, so another team could pick it up. That is the test of whether documentation is real, and it is deliberately in your favour rather than ours.

Question 10

How do you make AI safe for real customers?

Accepted Answer

By limiting what it can do, grounding what it says, checking it continuously and keeping a record. In practice: the AI gets a narrow job and no ability to act beyond it; answers are retrieved from your own material with the source shown; an automated evaluation set runs on every change so quality regressions are caught before customers meet them; and every interaction is logged with its model and prompt version. Where being wrong is expensive — money moving, an entitlement decision — a person stays in the loop.

Question 11

Can you work with regulated businesses?

Accepted Answer

Yes, and it is the work we are built for. We have delivered payroll software recognised by HMRC, a multi-tenant AI system built to SOC 2 pre-audit standard, and hands-on AML and KYC engineering. We read the regulation and the codebase in the same sitting, and we build the evidence layer in from the first commit rather than before an audit.

Question 12

How do we know when something is ready to launch?

Accepted Answer

When you can answer three questions in writing: what happens when its dependencies fail, who can see data they should not, and what evidence exists that a human reviewed it before release. If any of those takes more than a few minutes to answer, it is not ready — and that gap is normally the consequence of building fast, which was probably the right call at the time.

Question 13

What happens after launch?

Accepted Answer

Launch is the beginning of the risk, not the end of the project. Dependencies acquire published vulnerabilities, certificates expire, data volumes cross thresholds and access outlives employment. We offer a retainer covering monitoring that alerts a human, a patch cycle, quarterly access review, restore testing and review of what ships. If you would rather your own team held that, we document it so they can.

Question 14

What won't you do?

Accepted Answer

We will not promise a savings percentage before seeing a baseline, present a prototype as a production system, or take work we do not believe pays for itself. We are also the engineering side of compliance, not the compliance function — your compliance officer owns the obligation and we build the systems so the answers they need are actually there.

Straight answers.