AI speeds up the work; nearly 20 years of putting products into production decide what is kept. Four steps, always in the same order. At each one, you know what is happening, what you can see and what you decide. Nothing reaches your users without your approval.
You see something from the first week, and nothing is built without being written down and reviewed. After that, every change follows the same path, only shorter.
Questions first: your users, your business, your constraints, what already exists. Then proposals: what AI can bring, what is better left undone, a simpler version when it is enough.
A written summary of what was understood, in your words, the options proposed and the questions still open.
The need, as written. And what will not be in the first version.
A prototype to try from the first week. Then what will be built is written down in black and white: user journeys, scenarios, AWS architecture, choice of AI models, delivery plan and estimate.
The prototype, then a short, readable specification, stored with the code, that you review and comment on.
The scope of the first version, the plan and the estimate. You have what you need to decide, with or without Elia Interactive.
The product is built fast, in small steps, with AI, under human direction and review. Every change passes the same checks before going any further.
Every week, a version to try on a private test environment, and a short check-in to discuss it.
Each change, on the test environment, before it goes any further. You can adjust along the way.
The approved version goes live on your AWS account. It is dated, and rollback is ready. Reliability and costs are then monitored.
Your product live, then regular reporting: response times, tests, monthly AWS costs, releases.
Every release. Then the next improvements, decided from real usage.
No project manager in between, no hand-offs between teams, no need distorted along the way. Decisions are made with you, and end up in the product exactly as made.
Code reviews, useful rituals, security rules, tools: whatever already serves your product is followed, not bypassed.
Specifications, decisions and technical choices are stored in the repository as work progresses. Nothing stays in one person’s head.
The code, documentation and access are handed over to you. Your team can evolve the product without depending on anyone.
AI writes fast, and it gets things wrong with confidence: a query that is too heavy, a key left in the code, a test deleted to make the build “pass”. Nearly 20 years in production are what catch these mistakes before your users do. A fix and a new feature follow the same path, from specification to production. A failed check stops the change.
A check fails: the change goes back to the code, then passes every check again.
Rollback ready: the previous version can be restored.
›“delay alerts” specification reviewed and approved
›code written with AI, reviewed line by line
✓automated tests: 1,240 of 1,240 passed
✓AI answers: 60 reference cases, all as expected
✓security checked · estimated cost within budget
◆live on the test environment, ready to try
◆approved by you
●in production · version dated today
Run on every change: unit tests, behaviour scenarios written in plain language, full journeys just like a real user.
At least two: a private test environment and a production one. More if needed, for example to compare two solutions.
AI answers are checked automatically, and re-run against reference cases whenever a model or a prompt changes.
Every version is dated. If a release causes a problem, the previous version is restored.
On Eliacover, Elia Interactive’s own product, everything can be shown: the same standard applies to your product.
automated test cases
behaviour scenarios
Your product and your data stay with you. You keep control of access, and can revoke it at any time.
Each part of the product only has the permissions it needs. Passwords and keys are never written in the code.
Data is encrypted at rest and in transit.
Access and data handling are checked before every release, just like the tests.
Your information stays confidential. A non-disclosure agreement can be signed from the first conversation. Client products are never shown without their consent.
AI models receive what the task needs, and nothing more. Anything not useful stays in your product.
With serverless on AWS, you pay for what you use: few users, small bill. Cost follows activity, and it is monitored from day one.
AI is not an end in itself. It is used where it serves your users, with a known cost and checks around every answer.
Not the biggest one everywhere: a fast, low-cost model for sorting, a more capable one where quality demands it.
Every AI action has a budget, with an alert. You know what a request, a document, a customer costs.
Answers checked automatically, a fallback when the AI fails, uncertain cases flagged to a human.
Before a model or a prompt changes, the reference cases are re-run: quality cannot slip without anyone noticing.
| Task | Model chosen | Guardrail | Cost tracked |
|---|---|---|---|
| Sort a request | Fast and low-cost | Result compared with reference examples | Per request |
| Search your documents | Search in an index dedicated to your product | Only the documents the user is allowed to see | Per search |
| Write the answer | More capable, used only here | Sources cited; no source, no answer | Per answer |
| Uncertain case | None | Flagged, then handled by someone on your team | No AI cost |
The stack, the infrastructure and continuous integration, in one minute.
Serverless on AWS, infrastructure as code, manual approval before production.