AI Consulting Services –
Strategy, Implementation & Governance

Alice Labs offers end-to-end AI consulting covering strategy, implementation, and governance. Our services include AI strategy development and roadmapping, use-case identification and prioritization, data strategy and architecture, AI/ML model development and deployment, system integration, AI governance and EU AI Act compliance, change management and training, and ongoing advisory. We serve as a single partner across the full AI lifecycle—from initial assessment to production operations.

Evaluate AI consulting firms on five criteria: 1) Technical depth—can they build what they recommend? 2) Industry experience—do they understand your sector? 3) Delivery track record—ask for references and case studies. 4) Strategic + technical capability—strategy-only firms can't implement; technical-only firms may miss the bigger picture. 5) Cultural fit—AI consulting requires close collaboration. Alice Labs combines all five: we have senior engineers, industry expertise, 100+ deliveries, and both strategic and technical capabilities.

AI consulting is typically priced in three models: Project-based (fixed scope and price, €30,000-€300,000 depending on complexity), Time & Materials (€200-€350/hour for senior consultants), and Retainer (ongoing advisory at €10,000-€30,000/month). Strategy-only engagements are usually project-based. Implementation work often uses T&M for flexibility. Ongoing advisory is retainer-based. We recommend starting with a scoped assessment before committing to larger engagements.

Our engagement process follows four phases: 1) Discovery (1-2 weeks): stakeholder interviews, current state assessment, opportunity mapping. 2) Strategy & Design (2-4 weeks): use-case prioritization, architecture design, ROI modeling, roadmap creation. 3) Implementation (8-16 weeks): development, integration, testing, deployment. 4) Optimize & Scale (ongoing): monitoring, iteration, expansion to new use cases. Many clients start with just phases 1-2 before deciding on implementation.

Deliverables vary by engagement type. Strategy engagements include: AI opportunity assessment, prioritization matrix, architecture recommendations, ROI model, governance framework, and implementation roadmap. Implementation engagements deliver: production-ready AI systems, technical documentation, API specifications, monitoring dashboards, and team training. All engagements include executive-ready presentations and clear next-step recommendations.

Our typical project team includes: an engagement lead (strategic direction and stakeholder management), AI/ML engineers (model development and deployment), data engineers (pipeline and infrastructure), solution architects (system design and integration), and domain consultants (industry-specific expertise). Team size scales from 2-3 for strategy engagements to 5-8 for complex implementations. Every team member has 5+ years of relevant experience.

We work in the model that best serves each engagement. Strategy workshops and stakeholder alignment sessions are most effective onsite or in hybrid settings. Technical implementation work is primarily remote, with regular onsite presence for integration points and knowledge transfer. For Nordic clients, we can maintain regular onsite presence. For international clients, we use a remote-first model with strategic onsite visits at key milestones.

While we serve clients globally, we have deep expertise in Nordic and European markets. This means: native understanding of EU AI Act and GDPR compliance requirements, experience with Nordic business culture and decision-making processes, local presence in Sweden, knowledge of European data sovereignty considerations, and multilingual capabilities (Swedish, English, and more). For EU-based organizations, working with a European AI consulting firm reduces regulatory and data residency complexity.

Security and compliance are foundational to every engagement. We conduct data classification before any AI development, design systems with privacy-by-design principles, ensure GDPR-compliant data processing and storage, implement access controls and audit trails, address EU AI Act risk classification for all AI systems, sign NDAs and DPAs as standard practice, and can work within your infrastructure to avoid data transfer. Our consultants are trained in EU regulatory requirements.

We define success metrics collaboratively at engagement start. Common measures include: business KPIs (cost reduction, revenue impact, efficiency gains), technical metrics (model accuracy, system uptime, processing speed), adoption metrics (user engagement, process compliance), strategic metrics (time-to-value, portfolio coverage, governance maturity), and ROI realization (actual vs. projected returns). We provide regular reporting and conduct formal reviews at 30, 60, and 90 days post-delivery.

AI consulting and implementation services combine advisory and delivery in one engagement: strategy and use-case prioritization, data readiness assessment, model selection and development, MLOps and integration into existing systems, EU AI Act and GDPR governance, change management, and post-launch monitoring. Alice Labs runs all phases under a single accountable team, so strategy decisions translate directly into working systems instead of being handed off to a separate vendor. Typical scopes range from 12 weeks (single use case) to 12 months (enterprise-wide rollout).

AI implementation consulting services typically follow a four-stage delivery model: (1) Solution design and architecture (2-4 weeks), (2) Build and integrate — model development, API connections, security hardening (6-12 weeks), (3) Test and validate — functional, performance, and bias testing (2-3 weeks), and (4) Deploy and operate — production cutover, monitoring, and team handover (2-4 weeks). We staff each project with a senior engineer-led pod (architect, ML engineer, data engineer, domain consultant) and provide fixed-scope pricing for predictable budgets.

AI testing consulting services cover the validation layer that traditional QA cannot: functional testing of model outputs, performance and latency benchmarking, bias and fairness audits, adversarial and robustness testing, drift detection setup, and compliance testing against the EU AI Act risk classification. We build test suites that run continuously in CI/CD pipelines, so AI systems are validated not only at launch but on every model update. Test coverage is documented for audit purposes — critical for regulated industries like finance, healthcare, and public sector.

AI verification consulting confirms a model does what it was specified to do (correctness against requirements). AI validation consulting confirms the model does the right thing in the real world (fitness for purpose against business outcomes). Both are required under the EU AI Act for high-risk systems. Our process includes formal requirements traceability, statistical performance validation on held-out and production data, human-in-the-loop review for edge cases, and signed-off validation reports that satisfy auditor and regulator requests.

AI algorithm consulting is specialist advisory on selecting, designing, or tuning the underlying algorithms that power an AI system — choosing between classical ML, deep learning, foundation models, or hybrid approaches; designing custom architectures when off-the-shelf models underperform; and optimizing for cost, latency, accuracy, and explainability trade-offs. Most enterprises need it when stock models fail on domain-specific data, when regulatory constraints require explainable approaches, or when production cost makes large general-purpose models economically unviable.

AI process planning consulting maps the business processes where AI will create the highest leverage, then designs the future-state workflow including human-in-the-loop checkpoints, escalation paths, and exception handling. Our process: (1) document current state with process mining and stakeholder interviews, (2) identify AI insertion points using value-effort scoring, (3) redesign the workflow with AI in-the-loop, (4) define KPIs and governance gates, (5) build the change plan. The output is an implementation-ready blueprint, not a slide deck.

AI system implementation consultants deliver production AI systems, not prototypes. A typical Alice Labs engagement produces: deployed and integrated AI services running in your cloud or on-prem environment, full source code and documentation, CI/CD and MLOps pipelines, monitoring and alerting dashboards, security and compliance documentation (EU AI Act, GDPR, ISO 42001 alignment), runbooks for your operations team, and a 30/60/90-day post-launch support window. We hand over working systems your engineers can own.