If you’re searching for AI consultants in Greece, you’re either tired of generic “digital transformation” decks or you’ve already burned budget on a tool that never made it past the IT department. This guide is the reference we wish existed when we started Proxima — what AI consulting actually looks like in the Greek market in 2026, what it should cost, how ΕΣΠΑ funding fits in, and how to tell a serious partner from a reseller with a ChatGPT wrapper. Read it end to end, or jump to the section that matches where you are: scoping a first project, comparing vendors, or planning a conversational AI rollout.

TL;DR: What to know before hiring AI consultants in Greece

The Greek AI consulting market in 2026 is in a strange place: demand from SMEs has roughly tripled since 2023, but supply is split between two extremes — Big-4 advisory firms quoting six-figure transformation programs, and freelancers selling “ChatGPT training” for a few thousand euros. The sensible middle — boutique consultancies that ship working systems in 4–12 weeks — is small but growing. According to ELSTAT and EU Digital Economy and Society Index data, fewer than 8% of Greek SMEs have deployed any production AI workload, despite roughly 40% having experimented with generative AI tools. That gap between experimentation and production is exactly where AI consultants in Greece earn their fee.

Realistic numbers for 2026: an SME project (10–250 employees) typically lands in the €5K–€50K range for a scoped use case. Enterprise programs start at €50K and run well past €200K when you include integrations, change management, and governance. ΕΣΠΑ programs and EU recovery funds (RRF) can cover 40–70% of eligible costs depending on company size and region — and a serious consultant prepares the documentation for you, not the other way around.

Three red flags that should kill the conversation in the first meeting: (1) no discovery phase before a proposal, (2) no measurable KPIs tied to a baseline, (3) no integration plan beyond “we’ll connect it via API.” If you’re hearing all three, you’re talking to a reseller, not a consultant.

When to hire what: a freelancer or fractional consultant works for scoping, internal alignment, and AI literacy. A boutique consultancy (5–25 people) works for end-to-end delivery up to ~€80K projects. A large agency or system integrator makes sense when you need governance, multi-country rollout, or regulated-industry compliance. You need an AI consultant if: your team has tried tools and stalled, leadership wants ROI numbers before approving more spend, or you have a specific bottleneck (lead qualification, customer support, document processing) and don’t know what’s technically possible at your budget.

Table of contents

• What an AI consultant actually does — Awareness · 4 min
• The Greek AI consulting landscape in 2026 — Awareness · 4 min
• Hiring a single AI consultant in Greece — Evaluation · 4 min
• AI consulting companies in Greece: how to evaluate them — Evaluation · 5 min
• Engagement models and pricing — Evaluation · 4 min
• Conversational AI consultant in Greece — Evaluation · 4 min
• Conversational AI consultants vs. agencies — Evaluation · 4 min
• Building a conversational AI consultancy practice — Decision · 3 min
• ΕΣΠΑ and EU funding for AI projects — Decision · 3 min
• How Proxima approaches AI consulting — Decision · 3 min
• FAQ — Decision · 4 min
• Next step: scope your first AI project — Decision · 2 min

What an AI consultant actually does (and what they don’t)

An AI consultant’s job is to turn a business problem into a working AI system that someone in your company actually uses every day. That’s strategy, implementation, and adoption — three jobs, not one. If a consultant only does strategy (“here’s a 40-slide roadmap, good luck”), you’ll never see production. If they only do implementation (“here’s a chatbot, we’re done”), nobody will use it.

The core deliverables a competent AI consultant produces, in order:

1. AI Readiness Assessment — a structured review of your data, systems, processes, and team capability. Output: a maturity score (typically on a 1–5 scale) and a list of viable use cases ranked by ROI and feasibility.
2. Use-case prioritization — picking 1–3 starting points based on data availability, business impact, and integration complexity. Most Greek SMEs try to start with too many; a good consultant kills 80% of the ideas in the room.
3. Proof of Concept (PoC) — a 2–4 week sprint that tests the riskiest assumption with real data, not a sandbox demo.
4. Production rollout — integration with your CRM, ERP, helpdesk, or operational systems. This is where most projects die.
5. Change management and adoption — training, documentation, KPI tracking, and a feedback loop. Without this, your AI system becomes shelfware in 60 days.

An AI consultant is not a data scientist. Data scientists build models from raw data. AI consultants integrate existing models (OpenAI, Anthropic, Mistral, open-source LLMs) into your business workflows. An AI consultant is also not an automation agency, although in the Greek market the lines blur — see our piece on AI Agents vs. traditional automation for the technical distinction. And an AI consultant is not IT outsourcing: they don’t manage your servers or your help desk.

What you should not expect at SME budgets: training a foundation model from scratch (that’s a €1M+ exercise), building a labeled dataset from zero (€50K+ for anything serious), or fine-tuning a custom LLM unless you have very specific data and a clear ROI. 95% of viable Greek SME use cases are solved with retrieval-augmented generation (RAG), prompt engineering, and integration plumbing — not custom models.

One example from our own work, anonymized: a mid-size logistics company in Attica was drowning in inbound emails — booking confirmations, ETA questions, invoice queries. They wanted “AI to answer everything.” We killed that. Instead, we built a triage agent that classified emails into 6 categories, auto-drafted responses for 4 of them, and routed the other 2 to humans with context. €18K project, 4 weeks, ~62% reduction in agent handle time within 90 days. That’s what AI consulting in Greece looks like when it works.

The Greek AI consulting landscape in 2026

The Greek market for AI consulting splits cleanly into three categories, and each one solves a different problem:

1. Big-4 and large advisory firms (Deloitte, PwC, EY, KPMG, Accenture). These firms run AI strategy engagements for the top of the Greek market — banks, telcos, insurance, large retail groups. Their strength is governance, regulatory expertise, and the ability to staff a 20-person program team. Their weakness for SMEs is obvious: minimum engagement sizes typically start at €100K–€150K, decision cycles are long, and the senior partner you meet in the pitch is rarely the person who ships your code.

2. System integrators and IT services companies (Performance Technologies, Uni Systems, Space Hellas, Quality & Reliability, Cosmos Business Systems and similar). They sell AI as part of a larger infrastructure or ERP modernization. Strong on Microsoft, AWS, and Oracle ecosystems. Good fit if you’re already deep in their stack. Less suited to greenfield AI use cases that don’t touch existing enterprise software.

3. Boutique AI consultancies (Proxima and a handful of peers). 5–25 people, founder-led, focused on shipping production systems in 4–12 weeks. We typically work with companies in the 20–500 employee range with project budgets between €5K and €80K. The trade-off: we don’t have a 50-page governance framework or a global delivery center. We have a small team that ships.

Why most Greek SMEs are underserved by Big-4: a €25K AI project doesn’t pay for a senior manager’s three-month time, let alone a team. So Big-4 firms either decline these engagements or assign them to junior consultants with a templated deck. The result is the worst of both worlds — high day rates and shallow delivery. This is the gap boutiques exist to fill.

Market maturity is the other context that matters. Based on what we see in discovery calls and what aligns with EU DESI 2024/2025 data, most Greek companies sit at AI Readiness level 1 or 2 of 5: they’ve experimented with ChatGPT or Copilot, maybe automated a couple of workflows in Make or Zapier, but they have no AI strategy, no data governance, and no internal owner. Level 3 (one or two production AI workloads with measurable KPIs) is where the top quartile sits. Level 4–5 (cross-functional AI platforms, retraining loops, AI-native processes) is rare outside the largest Greek banks and a handful of well-funded scaleups.

Sectors moving fastest in Greece right now: e-commerce (product enrichment, customer service, returns triage), logistics and 3PL (route optimization, document processing, ETA prediction), professional services (legal, accounting, consulting — document drafting and research), and finance (KYC, fraud, internal copilots). Sectors lagging: construction (data is in the wrong format and on the wrong systems), hospitality (with notable exceptions in larger hotel chains experimenting with conversational concierges), and the public sector (procurement cycles measured in years, not weeks).

Hiring a single AI consultant in Greece (freelancer or fractional)

Sometimes you don’t need a team. You need one experienced person to walk into your business, listen for two days, and tell you what’s worth doing. That’s a fractional or freelance AI consultant, and for the right scope they’re the best money you’ll spend.

When a one-person engagement makes sense:

• Scoping and assessment. You want a second opinion before committing to a vendor or a multi-month program.
• AI literacy and leadership alignment. Your management team needs a half-day workshop to stop arguing about hypotheticals and start agreeing on priorities.
• Internal champion coaching. You have an internal team that’s technically capable but needs an external advisor to validate decisions.
• Vendor selection. You’re running an RFP and need someone to keep the agencies honest.

Typical day rates in Greece in 2026: €500–€1,500 per day for senior profiles (10+ years of relevant experience, real production deployments). Below €500/day you’re either getting a junior or someone moonlighting from a full-time job — fine for some scopes, risky for others. Above €1,500/day in Greece you’re paying for either a recognizable name or someone who’s just back from London or Berlin and hasn’t recalibrated yet.

The risks of a single-consultant engagement are predictable and worth naming:

• Bus factor. One person on holiday means the project stops. There’s no backup.
• No implementation muscle. A solo consultant can design the roadmap but can’t realistically build, integrate, and operate a production system across CRM, ERP, helpdesk, and data warehouse on their own.
• No vendor relationships. A consultancy has standing partnerships with HubSpot, Make, AWS, Microsoft, OpenAI. A freelancer is paying retail for everything.
• Accountability. If the project fails, who do you call? An LLC with a contract, or a person who can ghost you?

Where to find solid freelance AI consultants in Greece: LinkedIn (filter for “AI consultant” + Greece + posts in the last 6 months — silence is a signal), accelerator and startup networks (Found.ation, Endeavor Greece, EIT Digital), and direct referrals from peer founders. Cold marketplaces (Upwork, Fiverr, Malt) rarely surface the senior end of this market in Greece.

If you want the full deep-dive on solo engagements — contract structure, deliverables, sample SOWs — read our companion piece on Hiring an AI consultant in Greece (deep dive).

Eight questions to ask in the first call with any solo AI consultant:

1. Walk me through the last AI system you put into production. Who’s using it today?
2. What’s the smallest engagement you’ll take? What’s the largest you’ve delivered solo?
3. What integrations have you personally built (not just specced)?
4. How do you handle data privacy and GDPR for Greek-language customer data?
5. If this project needs 3 people for 6 weeks, what’s your plan?
6. What’s a use case you’ve turned down, and why?
7. How do you measure success after delivery?
8. What does month 2 look like if we engage you?

AI consulting companies in Greece: how to evaluate them

A real AI consulting company brings four things a solo consultant can’t: a delivery team that survives one person’s holiday, governance and process maturity, vendor partnerships with leverage, and an SLA you can actually enforce. The trade-off is cost and friction — you’re paying for the org, not just the work.

Red flags when evaluating an AI consulting company in Greece:

• No published case studies, or only logo walls. A serious firm publishes at least anonymized results — “reduced handle time by 62% across a 25-agent support team in 90 days” — not just a Vodafone logo with no context.
• Vague pricing. “It depends on your needs” is the right answer to the exact total. It’s the wrong answer to “what’s a typical engagement range.” If they can’t give you a band, they’re either inexperienced or hiding margin.
• ChatGPT-wrapper portfolio. If every “AI solution” they’ve shipped is essentially a custom GPT or a thin Streamlit app over the OpenAI API, you’re paying premium rates for work you could do in-house.
• No mention of integration partners. Real AI projects touch CRMs, ERPs, helpdesks, data warehouses. If their site doesn’t mention HubSpot, Salesforce, Microsoft, AWS, Make, n8n, or similar, they probably haven’t shipped much.
• “We can do everything.” A boutique that claims expertise in AI, web development, mobile apps, blockchain, marketing, and IT support is a body shop. Pick one.

Green flags:

• Documented methodology with named phases and deliverables (not just “Discover-Design-Deliver”).
• ΕΣΠΑ experience. They know which programs are active, what’s eligible, and what documentation the auditor will want 18 months from now.
• Named integration partnerships — even better, formal partner status with HubSpot, Microsoft, AWS, or specific automation platforms.
• Honest scope-out behavior. The best signal in a sales call is when the consultant tells you a use case isn’t worth doing.
• Transparent team structure. You know who’ll actually be on your project, not just the partner who pitched.

Here’s how the three categories compare on the dimensions buyers actually care about:

Running a fair RFP without burning three months: shortlist three vendors maximum (one from each category if you genuinely want options), give them the same 1-page brief with a clear problem statement and budget range, ask for a 60-minute working session not a slide-deck pitch, and require a fixed-price scoped phase one. If a vendor refuses to commit to a fixed price for a defined discovery phase, that’s a tell.

For named comparisons of specific Greek AI consulting firms, our spoke article on AI consulting companies in Greece compared goes deeper. You may also want to review our case studies as one example of how a boutique presents its work.

AI consultancy in Greece: engagement models and pricing

Most AI consultancy work in Greece in 2026 fits into one of four engagement models. Pick the wrong one and you’ll either overpay for what you needed, or under-scope and leave the system half-built.

1. AI Readiness Assessment. 1–3 weeks. Workshops with leadership and operational teams, data and systems audit, use-case longlist, prioritization matrix, and a roadmap. Indicative price: €2K–€5K. The right starting point if you have no AI strategy and want to avoid spending €30K on the wrong project.

2. Sprint (4 weeks). Pick one use case, ship a working PoC integrated with at least one production system. Output: a system that real users can touch, plus a measured baseline KPI. Indicative price: €8K–€15K. The right model when you already know what you want to test and need proof before scaling.

3. Roadmap engagement (8–12 weeks). Multi-use-case rollout. Typically 2–4 workflows automated end-to-end, integrated with CRM/ERP/helpdesk, with documentation and team training. Indicative price: €20K–€50K. The most common engagement for Greek SMEs ready to commit beyond a single PoC.

4. Managed AI partnership. Ongoing retainer. Monthly improvements, monitoring, prompt versioning, new use cases as they emerge. Indicative pricing: €2K–€8K/month. Right when you have multiple AI workloads in production and don’t want to staff an internal AI team yet.

How ΕΣΠΑ and Ψηφιακός Μετασχηματισμός ΜμΕ funding interacts with consulting fees: most active programs in 2026 treat consulting fees as eligible costs up to a percentage of the total project budget (typically 20–30%), with the rest going to software licenses, integrations, and training. Funding rates of 40–60% for SMEs are realistic, depending on region (higher in less-developed regions per EU regional aid maps) and company size. A serious consultant prices the project so the eligible portion is maximized, and prepares the documentation alongside delivery — not as an afterthought.

Fixed-price vs. T&M: use fixed price for assessments, sprints, and well-defined PoCs where the scope is bounded. Use T&M (time and materials) for roadmap engagements where the second use case depends on what you learn from the first, and for managed partnerships. Avoid open-ended T&M for greenfield projects with no scope cap — that’s how a €30K project becomes €90K.

What’s typically NOT in scope of an AI consulting engagement (read the SOW carefully):

• Third-party API costs (OpenAI, Anthropic, Twilio, Make, etc.) — usually billed at cost or pass-through
• Software licenses (HubSpot, Salesforce, n8n self-hosted infrastructure)
• Data cleanup beyond a defined scope — if your CRM is a swamp, fixing it is its own project
• Hardware, hosting beyond a defined cloud budget
• Ongoing model usage costs once the system is live

For the full pricing teardown across each model, see our spoke piece on AI consultancy in Greece: pricing and engagement models. Many of these engagements overlap with our broader automation services when the use case is more workflow than language model.

Conversational AI consultant in Greece: a specialist niche

Conversational AI — chatbots, voice agents, internal copilots — is its own discipline. Hiring a generalist AI consultant for a conversational AI project is like hiring a generalist software engineer to ship an iOS app: technically possible, but you’ll lose six weeks to lessons the specialist already learned.

What makes conversational AI different: the failure modes are linguistic and behavioral, not just technical. A model that returns the right answer 92% of the time can still feel broken if the 8% of failures are unhinged hallucinations, switches to English mid-sentence, or addresses the customer with the wrong formality.

Greek-language NLP challenges that bite teams who didn’t budget for them:

• Tokenization inefficiency. Greek text consumes 50–80% more tokens than English in most LLMs, which directly affects cost and latency. Pricing your project assuming English-equivalent token counts will blow your budget.
• Slang, regional variation, and code-switching. Real customer messages in Greece mix Greek, English, Greeklish (Greek written in Latin characters), and emoji freely. Your retrieval and intent layers need to handle all of it.
• Formal vs. informal register (πληθυντικός ευγενείας). A B2B legal copilot needs πληθυντικός. A consumer fashion brand chatbot needs ενικός. Getting this wrong is brand damage.
• Domain vocabulary. Greek-language financial, legal, medical, and technical terms aren’t always well represented in foundation model training data. RAG with a curated Greek corpus matters.

Use cases that work in Greece today — meaning we’ve seen real deployments with measurable ROI, not vendor demos:

• Customer support deflection. Tier-1 question handling for e-commerce, telecom, utilities, and hospitality. Realistic deflection rates: 25–55% depending on catalog complexity and content quality.
• Sales qualification. Inbound lead triage, calendar booking, qualification scoring before the human SDR sees the lead.
• Internal HR and IT helpdesks. Answering policy, leave, equipment, and access questions. Often the highest-ROI internal use case because the audience tolerates imperfect answers more than external customers do.
• Document copilots. Legal, accounting, insurance — searching internal knowledge bases in Greek with cited sources.

Skills to look for in a conversational AI consultant: dialog and conversation design (not just prompt writing), LLM evaluation and benchmarking (how do they prove the system works before you trust it with customers?), retrieval architecture (vector databases, hybrid search, reranking — RAG is a craft, not a checkbox), and GDPR / data residency literacy (where does the data go, who processes it, what’s the DPA).

Common mistakes we see when companies skip the specialist:

• Skipping intent and flow design — going straight to “let the LLM handle it”
• No graceful fallback to a human, with full context handoff
• No analytics loop — nobody is reading the conversations and improving the system weekly
• No evaluation set — you have no way to tell if a prompt change made things better or worse

For the full hiring guide for this specialty, see Hiring a conversational AI consultant in Greece.

Conversational AI consultants vs. agencies in Greece

You need an agency, not a person, when the project crosses any of these lines: omnichannel rollout (web + WhatsApp + Viber + voice + internal Teams), multi-language (Greek + English + Bulgarian for a Balkan-facing operation, for example), regulated industry (banking, healthcare, insurance), or a deployment that needs to handle more than ~5,000 conversations per month from day one.

How agencies typically structure conversational AI projects:

1. Discovery (1–2 weeks). Stakeholder interviews, conversation log review (if the bot is replacing humans), scope definition, success metrics.
2. Conversation and flow design (2–3 weeks). Intent map, sample dialogs, persona/tone guide, fallback paths, escalation rules.
3. Build and integration (3–6 weeks). System integration with CRM, helpdesk, knowledge base, channel APIs. RAG pipeline if knowledge-grounded. Evaluation harness.
4. Pilot (2–4 weeks). Soft launch to a subset of users or internal team, daily review of transcripts, prompt and retrieval tuning.
5. Production rollout and tuning (ongoing). Full launch, weekly performance reviews, content and prompt iteration.

Pricing benchmarks for a production-grade Greek conversational agent: €15K–€40K for a single-channel deployment with one or two integrations, scaling to €60K+ for omnichannel, multi-language, or regulated deployments. Add ongoing costs of €1K–€5K/month for managed operations.

Integration realities that determine real cost: Greek market deployments commonly need WhatsApp Business (Meta-approved BSP, template message rules, opt-in compliance), Viber Business Messages (significant in Greece for older demographics and certain B2C verticals), Facebook Messenger, web chat, and increasingly internal channels like Slack and Microsoft Teams. Each integration is its own learning curve, its own approval process, and its own line item.

Why “Greek-first” design matters more than people admit: a flow designed in English and translated produces awkward Greek. Real conversations need to be designed in Greek, with native-speaker testing, native-speaker tone calibration, and content (FAQs, knowledge base) that exists in Greek before you wire up retrieval. Agencies that skip this step ship bots that work in demos and embarrass the brand in production.

If you’re at the agency-selection stage, our spoke article Conversational AI consultants in Greece: agency selection walks through the shortlisting and RFP process specifically for this category.

Building a conversational AI consultancy practice (or buying one)

The difference between a project-based vendor and a full conversational AI consultancy is what happens after launch. A project vendor ships and walks away. A consultancy stays — because conversational AI systems are not “set and forget.” They drift, they hallucinate in new ways as edge cases arrive, and they get worse as your product, prices, and policies change unless someone is maintaining them.

The ongoing components that distinguish a real consultancy practice:

• Prompt versioning. Treating prompts like code: version control, changelog, rollback. Without this, debugging a regression becomes an archaeology project.
• Hallucination monitoring. Sampling production conversations daily, flagging hallucinations, feeding them back into the evaluation set.
• Retraining and re-indexing cadence. When does the knowledge base get re-embedded? When are prompts re-evaluated against the latest model release? Weekly? Monthly?
• Content operations. Someone needs to own the underlying knowledge — pricing changes, new policies, deprecated products. Often this is where projects fail: the bot is fine, the content behind it is six months stale.

Most Greek companies underestimate this side. They budget for the build and forget the operating cost, then are surprised six months in when the system’s quality has visibly degraded. A realistic operating budget for a production conversational AI system is 20–35% of the build cost per year.

Procurement model: the cleanest structure is a fixed monthly retainer covering a defined number of hours and SLAs, plus usage-based pass-through for model API costs. Avoid pure usage-based pricing for the consultancy work itself — it incentivizes the wrong things (more conversations, not better ones).

Internal vs. outsourced: bring conversational AI in-house when you have at least three production workloads, a clear product-led use case (your own SaaS, for example), and the ability to hire a senior conversational AI engineer (€60K–€90K base in Athens for a strong profile in 2026). Below that threshold, a consultancy retainer is cheaper and faster.

For more on the operations side, see Conversational AI consultancy in Greece: ongoing operations.

ΕΣΠΑ, Ψηφιακός Μετασχηματισμός and EU funding for AI projects

Greek AI projects can be partially funded — sometimes generously — through national and EU programs. The catch is that the funding landscape changes constantly, eligibility rules are detailed, and documentation is unforgiving.

Active programs in 2026 relevant to AI and automation: the family of “Ψηφιακός Μετασχηματισμός ΜμΕ” calls under the national ΕΣΠΑ 2021–2027 framework, sector-specific Recovery and Resilience Facility (RRF) calls under “Ελλάδα 2.0,” and EDIH (European Digital Innovation Hub) services available at low or no cost for early-stage assessments. Specific call IDs, budgets, and deadlines change quarterly — always cross-reference with the official portal at espa.gr.

Eligible cost categories typically include: software licenses (SaaS subscriptions for a defined period), consulting and integration services, training, and in some calls hardware and cloud infrastructure. Each call defines maximum percentages per category.

Realistic funding rates: 40–60% for SMEs in most calls, with higher rates (up to 70% in some cases) for micro-enterprises and companies in less-developed Greek regions. The remaining 30–60% is your own contribution, usually paid upfront with funding reimbursed after milestone audits — important for cash-flow planning.

Documentation a serious consultant prepares:

• A technical proposal aligned to the call’s scoring criteria (innovation, maturity, impact, sustainability)
• A budget breakdown matching the eligible-cost templates exactly
• Timeline and deliverable list compatible with the call’s reporting milestones
• KPIs the auditor can verify 18–24 months later
• Supplier quotes, CVs, company tax and insurance certificates

Common reasons applications get rejected: wrong NACE code for the call, missing or expired tax/insurance documents (φορολογική και ασφαλιστική ενημερότητα), budget categories that exceed allowed ratios, vague KPIs that fail scoring, and timelines that don’t match the call’s payment milestones. None of these are about the AI itself — they’re about paperwork. Choose a consultant who treats this seriously.

For governance and ethical AI implications relevant to public funding and regulated industries, the EU AI Act overview is the reference document. For peer benchmarking against other EU countries, the OECD AI Policy Observatory is useful.

How Proxima approaches AI consulting in Greece

Proxima is a boutique AI and automation consultancy based in Athens. We work with Greek and European SMEs and scaleups in the 20–500 employee range, with project budgets typically between €5K and €80K. Founder-led, small team, ship-first.

Our methodology is the same five steps in every engagement:

1. AI Readiness Assessment — 1–2 weeks, fixed price, leaves you with a maturity score and a prioritized list whether you continue with us or not.
2. Use-case prioritization — joint workshop, kill 80% of the ideas, agree on 1–3 to test.
3. Proof of Concept — 2–4 week sprint with real data, integrated with at least one production system, measured against a baseline.
4. Production rollout — full integration, documentation, training.
5. Adoption and improvement — KPI tracking, monthly reviews, iteration.

Why we work in 4–12 week engagements, not 12-month transformations. Greek SMEs don’t need a strategy deck — they need a working system in production this quarter. Long programs lose momentum, change leadership, and run out of budget. Short engagements with measurable outcomes build trust and create the case for the next phase.

Languages and integrations we ship in: Greek-first (with all the NLP, register, and tokenization considerations above), English-fluent, and we have working pipelines on HubSpot, Salesforce, Microsoft 365, AWS, Make, n8n, Zapier, OpenAI, Anthropic, and a handful of Greek-specific tools where the market needs them.

What a typical first 30 days look like: week 1 is discovery — interviews, systems review, data sample. Week 2 is the readiness deliverable and the use-case prioritization workshop. Weeks 3–4 are the start of the first PoC with daily progress visibility. By day 30 you have either a working PoC or a very clear answer about why a use case isn’t worth pursuing.

How we price: transparent ranges published upfront (assessment €2K–€5K, sprint €8K–€15K, roadmap €20K–€50K), fixed-price for bounded scopes, ΕΣΠΑ-compatible documentation included where applicable.

If you want context, see our case studies, our core AI Agents service, our broader digital transformation services, and the team behind it on About Proxima.

Frequently asked questions

How much do AI consultants in Greece cost in 2026?

Day rates for senior AI consultants in Greece range from €500 to €1,500. Project-based engagements range from €2K–€5K for assessments, €8K–€15K for 4-week sprints, €20K–€50K for 8–12 week roadmap programs, and €50K–€200K+ for enterprise programs. Big-4 advisory engagements start at €100K. Solo freelancers below €500/day exist but typically aren’t senior.

Can ΕΣΠΑ cover an AI consulting project?

Yes — most active ΕΣΠΑ and RRF calls in 2026 treat AI software, consulting, integration, and training as eligible costs. Funding rates are typically 40–60% for SMEs, sometimes higher for micro-enterprises or companies in less-developed regions. Always check the current call at espa.gr and have a consultant confirm eligibility before pricing the project around it.

How long does a typical AI project take?

An assessment is 1–3 weeks. A focused PoC is 2–4 weeks. A multi-use-case roadmap is 8–12 weeks. Enterprise transformations run 6–18 months. Anyone selling you a “12-month AI transformation” without a working PoC inside the first 30 days is selling you a slide deck.

Do I need a data scientist or an AI consultant?

For 95% of Greek SME use cases in 2026, you need an AI consultant — not a data scientist. Data scientists build and train custom models from raw data. AI consultants integrate existing foundation models (GPT, Claude, etc.) into your business workflows. Hire a data scientist when you have proprietary data, a clear modeling problem, and budget above €100K for that specific workstream.

What’s the difference between AI consulting and automation?

Automation moves data and triggers actions between systems based on rules (Zapier, Make, n8n). AI adds judgment, language understanding, and decision-making to that flow. Most real projects combine both — automation handles the plumbing, AI handles the parts that need to “understand” something. Read our deep-dive on AI Agents vs. traditional automation for the full distinction.

Can AI consultants work in Greek-language use cases reliably?

Yes, but with caveats. Modern foundation models handle Greek well — far better than three years ago — but Greek tokenization is more expensive, register matters (formal vs. informal), and Greek-specific RAG requires curated content. A consultant who has shipped Greek-language production systems is meaningfully different from one who’s only worked in English.

How do I evaluate an AI consultant’s track record without case studies?

If a consultant has no published case studies (NDAs are common in Greece), ask for: (1) anonymized metrics from a recent project, (2) a 30-minute walkthrough of the architecture they built, (3) a reference call with a previous client, (4) a sample deliverable from a similar engagement. Anyone who can’t produce three of these four hasn’t shipped much.

What’s a realistic ROI timeline for an AI project in Greece?

For SME-scale projects with a clear operational use case (support deflection, lead qualification, document processing), measurable ROI typically arrives 60–120 days after production launch. Internal copilots and enablement use cases take longer because adoption is the bottleneck — budget 6–12 months for measurable productivity impact at the team level.

Next step: scope your first AI project

If you’ve read this far, you have the framework: how the Greek AI consulting market is structured, what the engagement models cost, how ΕΣΠΑ funding fits in, what conversational AI requires that generic AI work doesn’t, and what red flags to watch for in any vendor conversation.

Three readiness signals you should already be tracking before you start: (1) you have a specific bottleneck with a measurable baseline (handle time, conversion rate, processing cost per document), (2) the data the AI would need lives in systems you control or can connect to, and (3) you have an internal owner who will run the system after launch — not just sponsor the project.

If those three are in place, you’re ready for an AI Readiness Assessment or a focused PoC. If they’re not, that’s the work to do first. Either way, talk to us — or to anyone else from the criteria in this guide — before you commit budget. See our AI Agents service for the core offer, our case studies for how this plays out in practice, or book a discovery call.

Έτοιμος να βγάλεις το πρώτο σου AI project από το slide-deck και να το βάλεις σε production; Κλείσε ένα 30′ discovery call με την Proxima και θα σου δώσουμε ένα honest assessment — με ή χωρίς συνεργασία.