How much does AI consulting cost?

BlokAI's engagements range from $5,000 to $75,000 depending on scope. Simple automations and LLM integrations start around $5K–$15K. Custom AI agent systems and full data infrastructure projects run $30K–$75K. We scope every project before committing to a price.

What does BlokAI build?

BlokAI builds AI agents, LLM applications, RAG systems, automation pipelines, and data infrastructure for startups and SMBs. We take projects from architecture to production — not demos, not prototypes.

Is BlokAI based in Japan?

Yes. BlokAI is a solo studio run by Kent Tran, based in Tokyo, Japan. We work with clients globally in English.

What is RAG and why does my business need it?

RAG (Retrieval-Augmented Generation) lets AI systems search your own documents, databases, or knowledge bases before answering — instead of relying only on training data. It powers AI customer support, internal search tools, and sales enablement systems that are accurate and up-to-date.

When to Build In-House vs Hire an AI Consultant: A Decision Framework

"OpenAI's o3 preview scored 87.5% on ARC-AGI — the benchmark designed to measure general reasoning — surpassing the previous record of 55% and prompting widespread debate about the pace of AI progress." — OpenAI, November 2025

The question comes up in almost every early AI conversation with a new client: "Should we be building this capability internally, or does it make sense to work with you?"

It's the right question to ask. And the honest answer is that it depends — on where you are, what you're trying to do, and what your business looks like in 12–24 months.

Here's the framework we use to think through it.

What You're Actually Deciding

"Build in-house vs. hire a consultant" isn't really a binary. It's more like a spectrum of models:

Full in-house: Hire AI engineers, data scientists, ML ops — build and own everything internally
Hybrid: Internal AI lead + external specialists for specific projects or technology depth
Consultant-led: External team does the work, transfers knowledge, internal team maintains
Fully outsourced: External team owns and operates AI systems on your behalf

Most businesses end up somewhere in the middle. The goal is finding the right position on the spectrum for your current phase.

The Four Decision Variables

1. How urgent is the timeline?

Building an internal AI team takes 6–18 months to reach productive capability — hiring, onboarding, establishing practices. If you have a competitive or operational problem that needs AI in the next quarter, internal hiring won't solve it in time.

External consultants can be productive in weeks. If urgency is high, that speed has real value.

2. How specialized is the work?

General AI capability — prompt engineering, basic automation, workflow integration — is becoming easier to build internally. The talent pool is growing, and the tools have matured.

But specialized AI work — complex agentic systems, fine-tuned models, computer vision, audio processing, enterprise-scale data pipelines — requires depth that takes years to develop. If your use case is in that category, the expertise gap between a specialized consultant and an internal generalist hire is significant.

3. What's your AI roadmap look like?

If you're running one AI project, building an internal team is hard to justify economically. If you have 5–10 meaningful AI initiatives planned over the next two years, internal capability starts to make sense.

Do the math: a mid-level AI engineer costs $150–250K all-in annually. If you're only running one project per year, that's expensive compared to project-based consulting. If you have a continuous pipeline of work, the economics flip.

4. Is this a core competency or a supporting function?

Ask yourself: will AI be a competitive differentiator that's specific to how your business operates? Or is it a capability you need but that's not fundamentally proprietary?

If it's the former — if how you use AI is going to be a meaningful part of your competitive moat — you want to own it internally. If it's the latter, treating it like other specialized professional services (legal, accounting, infrastructure) and outsourcing to experts makes more sense.

When Consultants Are the Right Call

You need results in months, not years
You want to prove ROI before making permanent hires
Your use case requires depth you'd struggle to hire for
You need knowledge transfer so your team can maintain what's built
You're uncertain which AI investments are worth making and want expert guidance

When Building In-House Is the Right Call

AI is central to your product or core service (you're an AI-powered company)
You have a continuous, large pipeline of AI work
You've already validated the use cases and know what you're building
You have enough adjacent technical talent to hire into AI roles efficiently
You need to protect proprietary data and don't want it touching external systems

The Most Common Mistake

The most expensive mistake we see is companies trying to build in-house before they've proven the use cases. They hire an AI lead, spend six months figuring out what to build, and end up with expensive underutilization and no shipped product.

The better sequence: use consultants to move fast, validate what works, understand what you actually need to own, then make targeted internal hires for the things that are proven and core.

The second most common mistake is the opposite — hiring consultants repeatedly for the same category of work without ever building internal capability. That's a dependency problem and it's expensive long-term.

The Hybrid Conclusion

The businesses we see succeed fastest are usually in hybrid mode: one strong internal owner (a technical product manager, a "head of AI," or a curious senior engineer) who can direct external experts and absorb their knowledge, combined with external support for depth and speed.

It's not about build or buy. It's about sequencing the investment correctly.

Want to implement AI? Book a call at blok-ai.com