What we modernise · Data
J Labs designs and builds the layer that makes the data you already hold behave as one shape, across the ERP, the CRM, the databases that predate the current team and the spreadsheets that hold it all together. Units, currencies, periods and naming get standardised, systems get connected, and one trustworthy source sits underneath the reporting and the AI use cases that were waiting on it. Nothing gets ripped out; what you have starts to work.
Summary
J Labs makes the data already inside an operating company usable. The work sits on top of the systems already in place (an ERP, a CRM, a Postgres or SQL Server database that predates the current team, and the spreadsheets that hold it together) and closes the gaps between them: units, currencies, periods, supplier and product names are standardised so the same line item reads the same wherever it appears; systems are connected so a record entered once is right everywhere; the gaps and duplicates that make a dataset lie are fixed in the order that matters; and a single trustworthy source is stood up underneath the reporting and the AI use cases that were waiting on it. Every engagement starts with a 20 or 45-minute discovery call and, where it makes sense, a four-day Spec from €5,000 that produces the written brief and acceptance criteria before any build is priced. Builds are fixed scope, fixed timeline and fixed price, run on weekly 30-minute check-ins with the client team in the room, and ship in an average of eight weeks. For a Danish quantity-surveying firm, J Labs standardised procurement data across 6 countries in a six-week cleanup phase, then built a pricing intelligence layer on top. Nothing is ripped out and no data lake is required first.
What we automate
Currency drift, unit-of-measure inconsistency, vendor and product aliases, country-specific tax handling, period misalignment, collapsed supplier hierarchies and gaps that are not missing at random: the problems that make a model lie, fixed in the sequence that matters before anything is built on top.
The same line item, the same customer, the same unit reading the same wherever it appears, across entities, regions and years of programmes that used different conventions. This is the phase that made pricing intelligence possible for a global quantity-surveying firm.
ERP to CRM, finance to operations, the legacy database to the tools the team actually opens. A record entered once is right everywhere, and the reconciliation spreadsheet that bridged two systems retires. Wrap, rebuild or replace is a decision we make with you, and show our working on.
A single, governed dataset the board pack, the operational dashboards and the finance team all read from, so the number in one meeting matches the number in the next. Sized to the tier you are at (spreadsheets and a shared drive is a legitimate starting point), not to a reference architecture from a vendor.
Once the data behaves, the use cases that were waiting on it: pricing intelligence, cost benchmarks, classification at volume, question-answering over documents. We choose prompting, retrieval or fine-tuning on the evidence, and it is rarely the most expensive option.
Putting an intelligence layer on data that has sat in the same database for years is core engineering, not cleanup work for greenfield projects. We connect legacy systems, automate the gaps and, if a system is genuinely past saving, say so and show the working. The price is the same either way.
Fit
This is for you if
You hold years of data across several systems and cannot answer a simple question from it. The data exists. It is in the ERP, the CRM, two databases and a folder of spreadsheets, in different units and naming, and every report starts with someone rebuilding it by hand.
You have been told to "use AI" and the honest blocker is the data. Every pilot stalls at the same place: the inputs cannot be trusted. Fixing that is not a side quest before the real project; it is the project, and it is where most of the value sits for an established business.
You can name the decision the data should support. A pricing call, a procurement benchmark, a board number, an operational forecast. A known owner, data that already lives somewhere in your stack, and a budget holder who can say yes. That is the shape we can scope in four days and build in weeks.
This is not for you if
You want a data lake built before there is a use case. A warehouse and a governance programme are the top tier of readiness, not the entry ticket. If the brief is "build the platform and the use cases will come", we will say so on the call and help you find the first use case that pays for the next tier.
You are a software company looking for a data platform for your product. We work alongside your operational systems, not inside your product or your engineering organisation. Customer-facing data infrastructure, analytics features and ML pipelines inside an application are a different team.
Nobody can say what "clean" means for your data. Standardisation needs a decision on what the canonical unit, currency, period and name are, and someone with the authority to make it. If that owner does not exist yet, the honest first step is a 20 or 45-minute call and a one-page summary that names them, not a cleanup phase.
Systems
The ones you already run. We don't insist on rip-and-replace: the automation layer goes on top of the systems in place, and if a system is genuinely past saving we say so on the discovery call and show our working.
Engagement options
| Engagement | Price | Timeline | What you get |
|---|---|---|---|
| The Spec | From €5,000 | Four days | A written brief you own outright: problem definition, acceptance criteria, architecture sketch and cost estimate. Vendor-neutral: take it to us or to anyone. |
| Data cleanup and integration | Fixed price, quoted after discovery | Typically four to eight weeks | Standardisation, cleanup and integration across the systems in scope, with one trustworthy source in production, weekly check-ins, open staging, documentation written as we go and a post-launch support window. |
| Data foundation and intelligence layer | Multi-phase, to €200,000+ | Multi-month, phased | Cleanup first, then integration, then the intelligence layer (pricing, benchmarks, classification or retrieval) on top of a governed source of truth, with a review cadence run with your data owner. |
None of these is an off-the-shelf product. There is no J Labs software to install: every system is specified in the brief, built for your stack, and owned by you. Every engagement is fixed scope, fixed timeline and fixed price, with acceptance criteria agreed before work starts. See how we work and the Spec.
What we've shipped
Quantity surveying · Global
Procurement data standardised across 6 countries in a six-week cleanup phase, then a pricing intelligence layer on top.
Frequently asked
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