Most AI is fluent, confident, and sometimes wrong. We're an Amsterdam R&D studio building our own software to make it dependable — every claim checked against the evidence, the gaps flagged honestly — so your team can act on what it tells you.
Foundation models are capable and unpredictable. We build the systems around them — verification, grounding, significance — that turn confident, sometimes-wrong output into results a team can act on. It's our own software, not a thin wrapper.
Our own product reads many public sources across dozens of markets and surfaces the few things that genuinely changed — sourced, and explained. It runs in production every day: the proof that our reliability methods hold up on real, messy data, not just in a demo.
Every result stays linked to the evidence behind it. Any claim can be followed back to its source — by your team, or by ours — and where the model has nothing to stand on, the gap is shown, not hidden.
Sources disagree, formats drift, models hallucinate. We treat unreliable input as the normal case — and design our own software for it from the first line of code, rather than wrapping a tool that assumes the data is clean.
This is the part that's genuinely hard — and the part off-the-shelf tools don't solve. So we research and build our own algorithms for it, in Python and TypeScript, and hold every result to the evidence rather than to how confident it sounds.
01 / Working principle
Most AI sounds confident and is sometimes wrong. Ours checks every claim against the evidence, flags what it can't support, and refuses to invent cause and effect. If we can't trace a result back to a source, we don't ship it.
02 / Working principle
The hard part isn't collecting signals — it's finding the one that matters across noisy, mismatched data from dozens of sources, without flooding you with false alarms. We tune hard for what deserves attention, and against everything that doesn't.
03 / Working principle
No single source is taken at face value. A finding has to corroborate across independent sources before it surfaces — and where they genuinely disagree, we keep the disagreement visible rather than papering it into one clean, false answer.
04 / Working principle
A snapshot tells you what is; the value is in what moved. We re-read the landscape continuously and separate real change — a new entrant, a shift in the data — from the noise that looks like change but isn't.
We treat reliability as an engineering problem, not a disclaimer. Several of these problems aren't fully solved — by us, or by anyone. Where they aren't, we say so, develop our own methods, and keep them measured against the evidence rather than against how good the output sounds.
Our own working principles — not a wrapper around someone else's model. We're honest about what's still uncertain, and we earn confidence the slow way: against real data, at real scale.
We show our work.
Mindspark Tech Labs is the product and R&D arm of the Mindspark group, sister to Mindspark Digital Labs, our consulting practice. We're founder-led and deliberately small — an Amsterdam studio that researches and builds its own software for making real-world AI dependable: reliable enough to act on, and accountable to the evidence. We own what we build, and we care more about whether a result holds up than about how it sounds.
For teams building on AI that needs to hold up — partners, collaborators, and anyone with a reliability problem worth solving. We answer our own email.