Compare · J Labs vs doing it yourself
Most companies should start with ChatGPT and the off-the-shelf tools their teams already have, and many should stop there. J Labs is for the moment a workflow needs to run without a person pasting things into a chat window: on your data, inside your systems, with someone accountable when it fails. This page says plainly which is which.
Summary
Doing it yourself with ChatGPT and off-the-shelf tools is the right first move for almost every company of 50 to 5,000 people, and J Labs will say so on a discovery call. Individual licences give people a faster way to draft, summarise, classify and check their own work, and a team that has used them for a few months knows far more about where AI helps than one that has not. The limit is structural. A prompt in a chat window depends on a person to paste the input, judge the output and act on it. It does not read from the CRM, write to the ERP, run at three in the morning or fail loudly when the format changes. J Labs builds the system that does: scoped in a four-day Spec from €5,000, priced as a fixed scope, timeline and price from €5,000 to €200,000+, delivered on weekly 30-minute check-ins with open staging and documentation written as we go. The average build ships in eight weeks, 95% of projects reach production and a post-launch support window covers the first month. Every engagement starts with a 20 or 45-minute call and a one-page summary within 24 hours; if the honest answer is a licence and a usage policy, that is what the summary says.
Side by side
| J Labs | Doing it in-house with ChatGPT and off-the-shelf tools | |
|---|---|---|
| Time to first production system | An average of eight weeks, after a four-day Spec where scope needs defining. | Minutes to a useful prompt; an unattended system on company data is a different project entirely. |
| Cost model | A fixed price per engagement, from €5,000 to €200,000+, plus change orders beyond scope. | Licence fees plus the hours of the people doing it, which rarely appear on any budget line. |
| Who owns the outcome | J Labs commits to acceptance criteria written into the proposal before work starts. | Whoever wrote the prompt, until they change roles or leave. |
| Works with legacy systems | Yes. Reading from and writing to SAP, NetSuite, bespoke CRMs and long-lived databases is the work. | Only through copy and paste; a chat window does not connect to your ERP. |
| Scope discipline | Scope, timeline and price are fixed in the proposal; expansions become change orders you decide on. | Unbounded, which is fine for exploration and hard to govern once several teams are doing it. |
| Knowledge transfer | Your team is in the room from week one, with open staging, visible pull requests and documentation written as we go. | Prompts live in personal accounts and shared documents, if they are written down at all. |
| Data handling | Data sources, destinations and what the model may see are written into the Spec before anything is built. | Depends on the licence tier and on every employee reading the usage policy. |
| Best for | Workflows that must run on company data, across systems, for more than one person, unattended. | One person, one workflow, clean data in one place and a human checking every output. |
The honest call
Choose J Labs when
The workflow has to run without a person pasting into a chat window. Invoices arrive and need matching, leads land and need routing, calls end and need summarising into the record at three in the morning. That is a system, not a prompt, and J Labs builds it on the software you already run.
More than one person depends on the output. Once a second team relies on the result, questions of accuracy, evaluation, monitoring and who fixes it when the format changes become real. Those are engineering questions with a fixed scope and a fixed price.
A wrong answer costs something. Misclassified tickets, a mis-keyed payment, a candidate rejected on a hallucinated fact. When the cost of error is money, customers or compliance, the system needs acceptance criteria before it is built and a support window after it ships.
Choose doing it in-house with chatgpt and off-the-shelf tools when
One person owns one workflow. A recruiter screening CVs, an analyst drafting the weekly commentary, a lawyer summarising contracts. If a single person is both the input and the judge of the output, a good licence and a well-kept prompt library is the whole solution, and a build would be over-engineering.
The data is already clean and in one place. When the input is a document on your screen and the output is a document you edit, there is no integration to build. The case for engineering begins when the data lives in three systems and none of them has been tidy since the migration.
You have not yet learned where AI helps in your business. A few months of everyday use across teams tells you more than any vendor can. Give people licences, write a short usage policy, watch what sticks, and bring the pattern that keeps recurring to a discovery call.
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