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Services

Choose the AI transformation service that fits how the business needs to move.

LimeShift helps companies adopt AI across leadership, departments, and wider company execution, then adds specialist systems support when privacy, control, or deployment needs become part of the brief.

The core offer is still better execution, stronger adoption, and clearer leadership visibility, not disconnected technical projects.

Three core routes Assessment, department transformation, or company-wide rollout
Specialist support available Open-source systems, deployment, hardware, and fine-tuning in one grouped service
Built for real teams Suitable for compact businesses, growing companies, and enterprise environments

Core services

The main business stays centred on AI transformation inside live execution.

Most clients start with one of these three routes, depending on how clear the need already is and how broad the first move should be.

  • Core service

    AI Transformation Assessment

    Clarify where AI can create the most operating leverage, what the first move should be, and which rollout route makes commercial sense.

    • Best when the right starting point is still unclear
    • Aligns leadership on leverage, readiness, and risk
    • Creates a practical recommendation, not a vague roadmap
  • Core service

    Department AI Transformation

    Redesign one team’s highest-value workflows, launch the right AI operating layer, and create a visible result the wider business can build on.

    • Strong for marketing, sales, finance, operations, and enablement teams
    • Balances workflow redesign, adoption, and quality control
    • Built to produce a first useful win quickly
  • Core service

    Company-wide AI Transformation

    Create one coherent operating model across leadership and the teams that shape execution, visibility, and growth.

    • Designed for broader cross-functional rollout
    • Combines leadership sponsorship, governance, and delivery
    • Keeps expansion deliberate instead of chaotic

How the services fit together

The sequence stays commercial and operational before it becomes technical.

Tooling choices matter, but they should follow the workflow, operating model, and adoption decision, not replace them.

  1. 01

    Clarify the business need

    Start with the operating pressure, leadership objective, and commercial constraint before discussing tools, models, or infrastructure.

  2. 02

    Choose the right transformation route

    Decide whether the business should begin with an assessment, one department, or a broader company operating model.

  3. 03

    Implement the working layer

    Ship the workflows, context, operating habits, and review points that make AI useful in live execution.

  4. 04

    Add specialist support when needed

    Bring in open-source infrastructure, deployment, or fine-tuning guidance only when privacy, control, performance, or economics genuinely call for it.

Specialized services

One grouped specialist service covers infrastructure, model choice, deployment, and fine-tuning support.

When a client needs more control over the stack, LimeShift can support the specialist layer as one coherent service rather than splitting it into five separate offers.

Grouped specialist service

Open-source AI systems advisory and deployment support

This service is for businesses that need sharper guidance on private or local AI environments, open-source model choices, deployment realities, hardware planning, or fine-tuning. It stays tied to the operating outcome so the technical work supports the business instead of becoming a distraction.

Infrastructure and deployment guidance

Assess whether open-source or local deployment is appropriate, shape the environment, and support setup decisions without turning the project into infrastructure theatre.

  • Private or hybrid deployment options
  • Deployment and configuration support
  • Practical guardrails for reliability and maintainability

Model, hardware, and performance fit

Match model choices and hardware sizing to the business use case, data reality, expected load, and the level of control the client actually needs.

  • Open-source model selection
  • Hardware sizing and setup guidance
  • Right-sized advice for compact teams and enterprises

Adaptation and fine-tuning support

Guide the adaptation layer when base models need sharper task fit, domain behaviour, or controlled fine-tuning tied back to real workflow outcomes.

  • Fine-tuning and adaptation guidance
  • Evaluation logic grounded in business use
  • Handover support so the stack remains usable after launch

What keeps the offer coherent

Even the specialist work is shaped to support adoption, governance, and execution.

This keeps the services hub expandable later without turning it into a fragmented list of unrelated technical offers.

  • The business outcome stays first

    Even when specialist systems work is involved, the engagement is still anchored in how leadership, teams, and workflows perform better.

  • Useful for small businesses and enterprises

    The specialist layer can support a compact local setup, a privacy-sensitive mid-market team, or a larger enterprise environment with stricter control requirements.

  • One grouped specialist service, not five separate projects

    Infrastructure, model choice, hardware, deployment, and fine-tuning sit inside one specialist offer so the work stays coherent and easier to expand later.

FAQ

Questions that usually come up when comparing the service routes.

A few clarifications for leaders deciding whether they need a core transformation engagement, specialist support, or both.

Should we start with the services hub or go straight to one service page?

If the starting point is already obvious, go straight to the relevant service page. The hub is most useful when leadership wants to compare routes and see where specialist support fits.

Is the specialist service only for companies that want private AI infrastructure?

No. Private or local deployment is one common reason to use it, but the same grouped service also covers model selection, hardware sizing, deployment support, and fine-tuning guidance when those become important.

Do you treat fine-tuning as a standalone offer?

No. Fine-tuning sits inside the specialist service when it supports a real business case. LimeShift does not position it as a separate product detached from the operating outcome.

Can specialist support sit alongside a department or company transformation engagement?

Yes. That is often the cleanest model. The transformation work stays centred on workflow and adoption, while the specialist layer supports the technical choices required by privacy, control, or performance needs.

Choose the right starting point

Book the assessment call and map which service route fits best.

Use the first conversation to decide whether the business should start with a focused assessment, a team-level launch, a broader operating model, or a transformation engagement with specialist systems support included.