More revenue. Higher margins. Same team. AI that runs in production.

For mid-sized companies in the renewable-energy sector

We identify the economically strongest AI use cases in your processes, take them safely into production within 30–60 days and keep improving them with your team.

  • Clarity on which use case creates economic value first – instead of expensive tools and pilot graveyards
  • Less searching, transferring and manual checking – more time for clients, projects and decisions
  • A safe framework instead of shadow AI – with clear guardrails and ownership
  • One partner for implementation and operations – without leaving your team to carry the transformation alone

AI agents in productive use · Full-day AI analysis workshops in mid-sized companies · Currently implementing for a leading wind-turbine manufacturer

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Statex logo
GoSalty logo
LocaMarine logo
PrimeSurf logo
Helsi logo
Universität Hamburg Marketing GmbH logo

Waiting is no longer a neutral decision.

A relevant share of daily work consists of tasks AI can support reliably today: finding information, transferring data, checking documents, preparing tickets and producing recurring reports.

While this work stays manual in your company, others are standardising their processes. They handle more projects with the same team, react faster and protect their margin better.

The question is no longer whether AI matters. The question is where it changes the economics – and how to anchor it safely for the long term.

You know AI matters. What's missing is a defensible first step.

Day-to-day business keeps running while it stays unclear where AI genuinely changes the economics – and how to anchor it safely in your company.

  1. 01

    Plenty of ideas and tools, no prioritisation

    Which use case first? Without an economic assessment the answer stays a matter of taste.

  2. 02

    Knowledge sits in heads, inboxes and separate systems

    As soon as someone is overloaded, on holiday or gone, the process stalls.

  3. 03

    First AI tools run without shared guardrails

    Well-meant initiative turns into shadow AI that nobody feels responsible for.

  4. 04

    Pilots never leave the test stage

    The prototype works technically. Without integration, operations and ownership it still creates no value.

Waiting does not save money. It only postpones the change – while competitors handle more projects with the same team, react faster and protect their margin better.

Clarity first. Then production. Then secure operation.

Three phases that build on each other – and a loop that does not end at go-live. We combine your internal process knowledge with sector understanding, engineering depth and a clear view on security.

  1. 01

    Clarity & prioritisation

    AI analysis workshop

    Together with you and your specialists we examine where time is lost today, which processes depend on individuals and where unnecessary cost or lost opportunity is created.

    • Economic value
    • Effort and technical feasibility
    • Risk and regulatory requirements
    • Employee acceptance and involvement
    You walk away with

    A prioritised basis for decision and implementation instead of a loose list of ideas.

  2. 02

    Production implementation

    Implementation sprint

    We take the most relevant use case into the real workflow. First we check whether an existing solution should be used, integrated, adapted or custom-built.

    • Connect data, systems and real workflows
    • Test under real conditions, not in demo mode
    • Data protection, roles and approvals from the start
    • Involve and train people, keep decision authority in-house
    You walk away with

    An application in daily productive use – not an isolated demo.

  3. 03

    Operations, enablement & scale

    AI Ops

    Models, vendors, regulation and processes keep moving. We run operations and continuous improvement – and build internal capability so your team can steer it.

    • Monitoring, maintenance and technical adjustments
    • Adaptation to new models and providers
    • Governance, data protection and EU AI Act
    • Training, office hours, internal AI competence centre
    You walk away with

    A capability inside your company instead of dependency on an external vendor.

The workshop is the standard entry point. If you already bring a clearly defined use case, budget, data access and decision authority, we go straight into an implementation sprint.

Where AI pays off fastest in the energy sector

Recurring patterns from real projects. We are standardising these packages so every project makes the next one faster.

  1. Commercial asset management

    Invoice verification, reconciliation of contract and operating data, prepared investor reporting.

  2. Regulatory & tender agent

    Continuously monitor relevant requirements and tenders and classify them by portfolio and company relevance.

  3. Document & permitting workflows

    Extraction, completeness checks, deadline tracking and clean handovers between everyone involved.

  4. Internal knowledge assistant

    Make company knowledge from approved documents, policies and process descriptions securely queryable.

  5. Ticket & service agents

    Enrich, prioritise and prepare incoming cases for specialist handling.

  6. CI & research agents

    Monitor approved sources, check defined parameters and correlations, prepare recurring reports.

Trust layer

Data protection, EU AI Act and security are part of the design – not an afterthought.

EU Artificial Intelligence Act
DSGVOPrivacy by design
  • Handle sensitive data under controlClear rules on which data may be processed where – instead of public chat experiments without approval.
  • Define roles and ownership earlyWho may use what, who decides on the specialist side, who owns operations. Settled before go-live, not after.
  • Compatible with your IT realityThe setup fits existing systems, approval processes and security requirements – not the other way round.

What we can prove – and what we can't yet.

We work without invented percentages and without unapproved references. This is the honest state of play.

  1. 01

    Proven

    Several AI analysis workshops in mid-sized companies

    The starting point was similar every time: relevance understood, plenty of ideas and pain points, but no defensible prioritisation. The feedback confirms above all the value of clarity about which problems to solve first.

  2. 02

    In implementation

    Straight from workshop into implementation

    For a company in the medical sector we are building a Jira agent that enriches incoming tickets and prepares them for handling. The use case came directly out of the prioritised workshop result.

  3. 03

    Analysis only

    Potential identified, built by someone else

    At a printing company we identified the relevant potential and use cases in the workshop. The implementation was later done by another partner. The case proves the quality of the analysis – not our delivery.

Three perspectives on the same problem

Sector proximity, entrepreneurial scrutiny and engineering depth. All three of us can run the first conversation – and we bring in whoever fits your question.

A small senior team instead of a bloated consultancy. Short paths, direct communication and ownership all the way into productive use.
Portrait of Stefan Hain

Sector & delivery lead

Stefan Hain

Combines the energy sector, commercial thinking and delivery leadership. He translates AI potential into concrete use cases, defensible roadmaps and decision material for management and specialist teams.

MBAI · PSPO I · PSM I · 10+ years in product and strategy in the energy sector
Portrait of Timo Rogge

Entrepreneurial scrutiny

Timo Rogge

Tests critically whether a venture makes economic sense, separates hype from real value and brings focus into complex decisions. Technology only counts when it solves a real problem better.

Engineering understanding · Business development · Energy industry experience
Portrait of Chris Meinl

Engineering & operations

Chris Meinl

Owns architecture, integration, data protection, security and productive operation – and explains technical decisions so they stay commercially decidable.

8+ years in software engineering and architecture · CTO/tech co-founder · 15,000-user product

What changes for you.

  1. 01

    You work on the right levers.

    No speculative tool purchases and no pilot graveyard. You know which application should create value first – and why.

  2. 02

    Your team achieves more without working more.

    Repetitive preparation is automated. Experience stays where it matters: clients, exceptions and decisions.

  3. 03

    You get a safe framework instead of shadow AI.

    Clear guardrails, defined ownership and controlled handling of sensitive data from day one.

  4. 04

    You get a partner, not a project handover.

    Operations, monitoring, improvement and market observation do not land back on your team after go-live.

Is this a fit?

This is right for you if …

  • you lead or own responsibility in a mid-sized renewable-energy company
  • you want a structured path instead of more tool discussions and isolated experiments
  • you are ready to involve the process knowledge of your specialists
  • security and controlled operations matter as much as speed

This is not right for you if …

  • you only want to buy a tool or book a one-off training session
  • you expect AI to work without involving your team
  • you primarily want to use AI to reduce headcount

How we get to know each other.

  1. Book a time

    Enter your name, email and phone number and choose a suitable slot in the calendar.

  2. 20-minute AI potential call

    We discuss your current situation, goals and bottlenecks by phone. You get an honest first view on whether there is meaningful potential – and whether we fit.

  3. Focused advisory conversation

    If the potential is clear, we discuss approach, stakeholders, outcome and investment range. The next step is the analysis workshop or, when the prerequisites are clear, an implementation sprint.

Ready for the first economically sensible AI step?

What decision makers ask us before starting

Does our data need to be perfect first?

No. A defined knowledge area or a clearly scoped process is enough to start. The analysis workshop makes visible what is usable right away and what should be prepared first. Clean data helps later – it is not an entry barrier.

Will AI replace jobs here?

That is explicitly not the point. AI takes over repetitive preparation, structures information and supports checks. Your people keep decision authority and gain time for clients, exceptions and decisions. The goal is more capacity with the same team, not a smaller team.

Do we need to be technically strong?

No. We only bring in technical complexity where it is genuinely needed for security, integration or impact. What matters most is one contact in IT. We handle the bulk of the technical detail.

Can this run in our existing IT environment?

Usually yes. We build on your existing systems and choose solutions that - work in the DACH context, - respect EU hosting and GDPR and - fit sensibly into your landscape. We keep software, setup and operations clearly separated and discuss security and hosting early.

Does this make sense for 20 to 200 employees?

Especially then. Mid-sized companies benefit most from a clear, bounded entry point, because they do not want months of consulting but visible results. Our approach is cut to work with manageable internal effort – without an in-house AI team.

How much internal effort does this take?

For a typical start you need: - 1–2 owners (e.g. management or a business unit plus IT), - a few workshops and alignment sessions and - some time for feedback and testing. We handle design, setup and implementation. The goal is productive first results without stalling your daily business.

Do we always have to start with the analysis workshop?

No, but it is the standard route. The workshop is ideal when the target picture and priorities are still fuzzy. If you already bring a clearly defined use case, budget, data access and decision authority, we go straight into an implementation sprint.

What happens after the workshop?

Afterwards it is clear - which use cases are economically worth it, - which one should be implemented first, - which data, systems and people are needed and - which security and governance guardrails apply. You decide the next step. There is no hidden obligation to a large transformation.

How do you handle data protection, the EU AI Act and critical infrastructure?

We do not sell legal advice, but we do deliver a clean, documented rollout: policy building blocks for internal use, role and access logic, documentation support for data protection and IT security, and AI literacy requirements taken into account. We make no blanket critical-infrastructure claims. Where such requirements apply, we evidence them per project.

Are we locked into specific tools or vendors?

No. Salty Labs is not a platform product and not a reseller. We work with proven, mostly EU-hosted solutions and choose with you what fits your IT, your compliance requirements and your budget. It matters to us that you can keep running the solution without us.

Do you only advise – or do you actually build?

Implementation and operations are the core of our business model, not an add-on. We build setups ourselves, support go-lives and see real usage in teams. We recommend nothing we have not already built, used or integrated into workflows in practice.

Where does AI genuinely create value in your company?

An honest first orientation. No obligation and no hype.

Find out which AI step makes economic sense for your company.

20 minutes on the phone. We look at where your company stands, which goals and bottlenecks matter most, where the first AI entry points could be and which next step fits your situation. The call promises no finished roadmap and does not replace a workshop. You get an honest first orientation. If we see no sufficiently relevant lever, or we are not the right partner, we will say so just as openly.

Prefer email? Send us your company, the area in question and your biggest open question at hello@saltylabs.ai.

  • which area has the biggest bottleneck
  • which systems and data are involved
  • who would be part of the decision internally