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AI Enablement

AI Enablement That Actually Takes Work Off Your Team

You already know AI could save your team hours every week. The hard part is doing it right: mapping your real processes, picking tooling that fits, keeping token costs sane, and staying compliant. We bring industry, process, and AI engineering know-how into one team so your setup delivers real relief, not another tool nobody opens.

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Cancel any week. Last week refunded if we didn't blow you away. No hours tracked.

  • 75+ products shipped
  • 10+ years experience
  • No-Bullshit Guarantee
// 01

Why internal AI stalls

95% of enterprise GenAI pilots show no measurable P&L impact (MIT, 2025)

The technology is rarely the reason. Adoption stalls when the wrong process gets automated, no one owns the running system, or cost and compliance kill it late. Our antidote is boring, and it works:

  • ✓
    Map the real process first. We score where AI pays off and rule out the steps a form or a rule already handles. Automating the wrong step is how this fails.
  • ✓
    Measure before you scale. KPIs and a dashboard from day one, so 'is it working?' has a number, not an opinion.
  • ✓
    One workflow to production, then expand. We ship a single high-value workflow with guardrails, prove the saving, and grow from there. No big-bang rollout.
// 02

What We Offer

Workshops & Talks

We come to your team (on-site or remote) and show what AI can realistically do in your context. Formats range from a keynote to a half-day interactive session to a multi-day hands-on workshop, co-delivered with a domain expert when the topic calls for it. You leave knowing what to automate, what to leave alone, and how to start.

Done-for-You AI Setup

We map your processes, score where automation pays off, and build it into your existing systems. Audit, architecture, implementation, guardrails and monitoring, handover docs, and optional maintenance. You get a working setup on your own stack, not a slide deck and a pilot that stalls.

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From audit to handover

Every done-for-you engagement runs the same disciplined path, so nothing ships by accident and nothing important gets skipped.

01

Audit

We map how the work happens today and where AI realistically helps.

02

Score

Rank the candidates by payoff and risk. Rule out what should not be automated.

03

Architect

Model choice, local-vs-API, routing, data residency, and a cost budget, decided before we build.

04

Implement

Built into your existing systems, not a parallel sandbox that never reaches anyone.

05

Guardrails

Monitoring, evals, and fallbacks so a wrong answer is caught before it reaches a customer.

06

Handover

Runbooks plus team upskilling. You own and run it; maintenance is optional, not baked in.

// 04

Built into every setup

Cost & Efficiency Engineering

AI gets expensive fast when it's wired up naively. We manage token and context usage, route each task to the cheapest model that can actually handle it (semantic routing), cache aggressively, and add fallbacks. The result stays affordable as usage grows.

Compliance-First Setups

When sensitive data or regulation is in scope, we run local or open-weight models in your own environment, keep data resident where it must stay, and map the setup to the standards you answer to (GDPR, the EU AI Act). Adoption stops being blocked by the legal question.

// 05

Compliance, handled

For EU teams the blocker is rarely capability. It is where the data goes and who can read it. We design for that from the start, not as an afterthought.

  • ✓
    EU AI Act, on the calendar. High-risk obligations apply from 2 August 2026. We map your use case to its risk tier and build the documentation, human oversight, and logging it needs.
  • ✓
    Data residency that holds up. A US hyperscaler's EU region is not residency: the US CLOUD Act still reaches it. For regulated data we run open-weight models on infrastructure you control.
  • ✓
    Semantic routing. Trivial work goes to a cheap model, sensitive work to a local one. The router keeps cost down and keeps data where it has to stay.
  • ✓
    Audit trail by default. Per-request logging, so you can show a regulator what ran, on what data, and why.
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Tools and standards we work to

We bring the tooling and the rules, so you don't have to assemble them under a deadline.

GDPREU AI ActData residencyLocal modelsOpen-weight modelsSemantic routingToken budgetsEvalsRAGMonitoringRunbooksHandover docs
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How we make AI enablement actually work

  • ✓
    Three competencies in one team. Industry and process know-how, tooling, and AI engineering together. Most vendors bring one and guess at the rest, which is how the wrong step gets automated.
  • ✓
    Real value, not theater. We map your actual process and automate only where it pays off. If a manual step or a simple script beats AI, we'll say so, even when it shrinks the engagement.
  • ✓
    On your infrastructure. Your cloud, your data, your tools. We work with what you have instead of forcing a rip-and-replace, so there's no lock-in to us or to a vendor.
  • ✓
    Production-grade, not pilots. Every setup ships with guardrails, monitoring, and runbooks, and your team gets a handover. Pilots that never reach production are the default failure mode, and we build to avoid it.
  • ✓
    Knowledge transfer, not dependency. We upskill your team as we go and document what we build. The goal is that you own and run the setup, not that you stay on the phone with us forever.

How often is the honest answer "don't automate this"?

Often.

We would rather rule a process out than ship shelfware.

 Book a free discovery call
// proof

Proof, not promises

SKD Dresden

Live
Multiple Workshops: Education, Technical, Strategy

Web3 education, technical and strategy workshops plus consulting sessions across two years for a major museum network. No code …

ENTERPRISE 2023–2024

Twinsoft AI

Live
2 weeks To Sellable MVP

Took a vibe-coded prototype to enterprise pilot-ready, no shortcuts.

Python / Flask LLMs Terraform
AI 2026

Hyperstate AI

Wind Down
GPU Monolith → Microservices

Split a GPU-heavy monolith into orchestrated services and swapped self-hosted ML libs for scalable alternatives. Latency and cost …

Python / Django OpenAI API Neo4j
AI 2025

What clients say

LinkedIn

I was referred to Wavect by a colleague, and from the beginning, I was impressed with how Wavect focuses its services, generating a unique value proposition by combining web3 development and programming with education to contribute to the enrichment of this globally forming community. We all see the benefits of its continued growth and widespread adoption in various fields.

Kevin, the CEO and founder, has been incredibly fast and accessible from the start. He always demonstrates a willingness to support and has a high level of empathy to understand what his clients need and how he can contribute. He also shows great flexibility in approaching projects based on the stage and budget of his clients.

Wavect is an excellent option to have a highly qualified external team that is committed and participates as if they were an internal part of the team. They not only provide services in blockchain development, DApps, and smart contracts, among others, but also offer project management services to integrate all aspects of a complex web3 project. Additionally, they excel in providing education services.

In just a few months of interaction, Kevin has become a highly valuable advisor to Polibit, and we aim to continue expanding the scope of Wavect's services towards Polibit. We are confident in their expertise and experience.

Gabriela Mena Co-Founder & CEO, Polibit
Original
LinkedIn

Kevin is the most knowledgeable person about smart contracts I know! He is sharing his knowledge openly with everyone and strategically building his thought leadership in the Web3 space. I had this amazing opportunity to be a guest on his podcast and I love the way he interviews people and gives value to others! If I ever have a project to implement using blockchain technology, Kevin will be my number one choice to cooperate with. 🚀

Krystian Koronowski Founder, CaptureFlow
Original
LinkedIn

Working with Kevin is a real pleasure. He is curious in understanding challenges and at the same time very intrinsically ambitioned in finding the best solution. On top of his undisputed IT-competence his honest and disarming open mind leads to very inspiring relationships. What I value very much is his personal humility towards people. Kevin is a highly recommendable Software-Engineer and a good person who makes an impact.

Stefan Pirchmoser Head of Brand Management
Original

Independently rated 5.0/5 on Clutch Read the reviews

FAQs

Honest answers about rolling AI out inside your company

End any week, with one message. No notice period, no exit interview, no fine print. We invoice weekly, so the most you’re ever committed to is the current week.
It’s in your contract: tell us, and we refund that week. No questions, no invoices to dispute, no calls to escalate. The only rule: refunds apply to the most recent week.
Because hours are the wrong metric. If we’re optimizing for hours billed, we’re not optimizing for your outcome. The deal is simpler: every week, we earn the next one. If we don’t, you don’t pay. We’re free to spend zero hours or sixty. What matters is whether you’re blown away.
We work with operators, not lottery winners. If a request would require breaking physics, the law, or a third party’s systems, we say so, and if we can’t align, we walk. The guarantee is mutual: you can fire us any week; we can also fire ourselves.
AI development is about building AI products for your customers (agents, agentic SaaS, LLM apps). AI Enablement is about your own team: workshops, internal process automation, and AI tooling set up on your infrastructure. Different buyer, different goal. Many clients eventually do both.
We start from your processes, not a generic slide deck. A session can cover what AI can and can’t do in your industry, prompt and context basics, automating a specific workflow, evaluating tools, or compliance and cost. We bring in a domain expert from our network when the topic needs one, and you leave with a concrete shortlist of what to do next.
Yes. We work in your stack: your cloud (AWS, GCP, Azure, or self-hosted), your CRM, your ERP, your internal tools, via their APIs. We don’t ask you to migrate everything to ours. Where a tool genuinely can’t do the job we’ll tell you, but the default is to fit what you already run.
Often yes. For sensitive cases we run open-weight or local models inside your own environment so data never leaves it, keep data resident where it must stay, and map the setup against GDPR and the EU AI Act. The compliance question is usually solvable; it just has to be designed in from the start instead of bolted on.
Possibly. The Act regulates deployers, not just builders, and high-risk obligations apply from 2 August 2026. Most internal productivity use is limited or minimal risk, but uses like hiring, performance evaluation, or access to essential services can be high-risk and carry documentation, human-oversight, and logging duties. We map each use case to its risk tier up front and build only what that tier requires, so you are neither exposed nor over-engineering compliance you don’t need.
We design for it. Token and context budgets per task, semantic routing so trivial work goes to a cheap model and only hard work hits an expensive one, caching, and fallbacks. We size the expected runtime cost before we build, so the bill doesn’t surprise you in month two.
We tell you. Part of the audit is ruling things out. If a process is better served by a rules engine, a form, or leaving a human in the loop, that’s the recommendation, even though it means less work for us. Automating the wrong step is how AI projects quietly fail.
You own it. We hand over documentation and runbooks and walk your team through running it. If you want us to keep monitoring, tuning, or extending the setup, we offer that as an optional retainer, but it’s a choice, not a dependency we build in.
No. Most clients start with a workshop or a short audit, see where the real opportunities are, and decide from there. It’s a low-risk way to find out what’s worth doing before committing budget to a build.
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