Trusted across banking and fintech
Trusted across banking and fintech
”Vacuumlabs is more than a partner, it is part of the Afin Bank team and continues to go above and beyond to ensure we are successful.”
Jason Oakley
CEO
We offer full knowledge sharing and training for implementing AI SDL tailored to your delivery needs. It becomes your own delivery framework.
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Most AI initiatives bought speed for individuals, but delivery still moves at the pace of its slowest step. Launching new products and application modernisation services need more.
Each step of delivery has its own version of the truth. When someone finds a better way to prompt, structure a spec, or catch a class of bug, that improvement lives and dies in their own setup. Little is shared.
Each handover in the lifecycle drops most of the context that produced the work. The next person or agent reconstructs intent from a ticket title and a diff. Quality drops where the guessing happens.
A new model lands, a tool gets replaced, a team reorganizes, and the accumulated prompts, rules and conventions go with it. You rebuild the same scaffolding.
Digital transformation services today need to include true AI-native practices
The same context layer serves definition, coding, review, testing and documentation, so intent carries forward instead of being reconstructed at every handover. Whoever picks up the work starts with exactly what the last person knew. Quality stops depending on the tool or model.
Context is maintained once, at the project level, rather than duplicated and drifting across each role’s tooling. That single thread is what makes causes traceable: an architecture decision from day one can be connected to the bug it produces in maintenance, even months later.
Nothing comes back as an unattributed assertion. Answers point to the decision, spec or commit behind them, and where sources disagree, the conflict is transparent. You get an honest read on project status and clear view of what to do next.
Rules about what AI can touch, what it must cite and what needs human sign-off are set once and apply across every tool and agent.
Head over to our contact page were you can send us a message or schedule a call with an expert.
Senior AI & Fintech Engineer
Jozef specialises in AI-enabled product development, fintech platforms, and software architecture, helping teams modernise systems and deliver scalable digital products.
Mapping of the setup, sources, and governance. Plus, set up the governance. Find out where the information sits, how it’s used and who owns it.
Build the first Knowledge Base, then find the tools and automation that fill the gaps.
Each tool and automation is tailored to your needs.
Health reviews and change briefings. We stay with you after the project is done to make sure it’s future-ready.
One conversation, one baseline you can hold us to. If AI isn’t the answer for your SDLC, you’ll hear that from us first.
It’s measured against your own baseline on a specific delivery stream. The gain comes mostly from specification, documentation, test authoring and impact analysis — not from typing code faster. We take the baseline before we start, so the number is checkable rather than asserted.
Because that research is right, and it’s why we don’t start with tooling. The gain leaks out at review, at oversized change sets, and at unclear specs. Those three are what the framework instruments first, which is also why we measure at delivery level — cycle time and change failure rate — not at developer level.
We are, exactly as we would be for hand-written code. Generated code passes the same review gates, the same tests and the same definition of done. Contractual warranties don’t change because a tool was involved.
No. We run enterprise or business tiers configured with training opt-out and defined retention, and we hand the configuration to your security team for review.
You own the deliverable, as in any of our contracts. On contamination: snippet-level licence scanning runs in the pipeline, and we keep a provenance record of which components were AI-assisted and with which tool. If an investor or acquirer runs IP diligence, the answer is already prepared.
Fair question. On fixed-scope work the speed becomes a shorter timeline for you. On time and materials, you get more scope per sprint at the same rate, with the delivery metrics to prove it. What we won’t do is bill the old estimate for the new speed.
No. The framework is the process — the knowledge backbone, specs, guardrails, review gates, provenance, metrics — and tools plug into it. We pick per stack and per constraint, and swapping one out doesn’t mean redoing the engagement.
You still keep the repo, the artifacts and the baseline. If we find your bottleneck isn’t somewhere AI helps, we’ll say so and scope something else — or nothing at all.
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