Pricing & Engagement

We offer three models tailored to different needs. Project-based engagements have a fixed scope, timeline, and deliverables — ideal for blueprint deployments, migrations, or proof-of-concept builds. Retainer engagements provide a dedicated allocation of hours per month for ongoing development, optimisation, and support — suited for teams that need continuous AI engineering capacity. Advisory engagements offer strategic guidance through regular sessions, architecture reviews, and vendor evaluations — designed for CTOs and AI leaders who need an experienced sounding board without hands-on implementation.

Timelines vary by scope. A focused proof-of-concept or single blueprint deployment typically takes four to eight weeks. A full production AI platform build — including infrastructure, data pipelines, model deployment, and integrations — runs twelve to twenty weeks. Strategic assessments and architecture designs are usually two to four weeks. Retainer engagements are ongoing with monthly deliverable cycles. We scope every engagement with clear milestones and deliverables upfront so there are no surprises on timeline or budget.

Team composition scales with the engagement. A focused blueprint deployment might involve one to two engineers working alongside your team. A full platform build typically requires three to five people covering AI engineering, infrastructure, data engineering, and project coordination. Advisory engagements are one-on-one with a senior consultant. We deliberately keep teams small and senior — every person on the engagement is hands-on and capable of making architectural decisions, which means faster progress with less coordination overhead.

Every project engagement includes a discovery and architecture phase, implementation with iterative demos, comprehensive documentation, knowledge transfer sessions for your team, and post-launch support for a defined stabilisation period. Deliverables are specific to the engagement but typically include deployed infrastructure, application code with CI/CD pipelines, architectural decision records, runbooks for operations, and test suites. We do not deliver slide decks and walk away — our engagements produce working, production-grade systems.

Retainers provide a guaranteed allocation of engineering hours each month — typically 40, 80, or 160 hours depending on your needs. You get a dedicated team that understands your codebase, architecture, and business context deeply over time. Work is planned in two-week sprints with your input on priorities. Unused hours do not roll over, but we work with you to ensure full utilisation through proactive optimisation, technical debt reduction, and capability expansion. Retainers include a monthly review meeting covering progress, metrics, and upcoming priorities.

Reach out via email at victor@gebarski.com or through the contact form on our site. We will schedule an initial call — typically 30 to 45 minutes — to understand your goals, current infrastructure, and timeline. From there, we provide a scoping document outlining the recommended approach, team, timeline, and investment. If everything aligns, we begin with a paid discovery phase that produces a detailed architecture and implementation plan. There is no obligation from the initial conversation, and we are straightforward about whether we are the right fit for your specific needs.

We are happy to sign a mutual NDA before any detailed discussions. Many clients prefer this, especially in regulated industries or when the project involves proprietary data and intellectual property. We have a standard mutual NDA that legal teams typically approve within a few days. If you have your own NDA template, we can review and sign that instead. The NDA process never delays project kickoff — we can execute it concurrently with scheduling the initial discovery sessions.

We work primarily with mid-market and enterprise organisations — typically companies with 500 or more employees or those handling sensitive data at scale regardless of size. Our clients span financial services, healthcare, legal, manufacturing, energy, media, and government-adjacent sectors. The common thread is organisations where AI needs to meet rigorous security, compliance, and reliability standards. We do not take on consumer app development or early-stage startup MVPs, as our expertise is best applied to complex enterprise environments.

The discovery phase is a structured two to three week engagement where we assess your current state and design the target architecture. This includes stakeholder interviews to understand business objectives and success criteria, a technical audit of existing infrastructure and data assets, a gap analysis mapping current capabilities to desired outcomes, a reference architecture with technology choices and rationale, a detailed implementation plan with milestones and resource requirements, and a risk register with mitigation strategies. The discovery deliverable becomes the blueprint for the implementation phase.

This is our preferred model. We embed within your existing team structure, use your communication tools and development workflows, and contribute directly to your repositories. Rather than operating as an external vendor delivering black-box solutions, we function as an extension of your team — participating in standups, code reviews, and architecture discussions. This model accelerates your internal team capabilities through direct knowledge transfer while delivering production outcomes. Many clients find their internal team is significantly more capable after an engagement with us.