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.

Multimodal AI for Business: Beyond Text — Images, Audio, and Documents — BlokAI Blog

"At Google I/O 2025, we introduced Gemini 2.5 Pro with native multimodal reasoning, Project Astra as a universal AI assistant, and AI Overviews expanded to over 1 billion users." — Google Blog, May 2025

For the first two years of the modern AI wave, "AI for business" largely meant text in, text out. You sent a document, you got a summary. You asked a question, you got an answer. The interface was essentially the same as a very smart email exchange.

In 2025, that's changing. Multimodal AI — models that work natively with images, audio, video, and structured documents alongside text — is moving from impressive demo to practical business tool. Not for everything, not uniformly, but for specific workflows where non-text data is currently slowing things down.

Here's where it's creating real value and what to think about if you're evaluating it.

What "Multimodal" Actually Means in Practice

The modern frontier models — GPT-4o, Claude 3.5, Gemini 1.5 Pro, and others — are multimodal natively. That means you can send them:

Images: Photos, screenshots, diagrams, charts, scanned documents
Audio: Voice recordings, meeting audio (via transcription pipelines)
PDFs and documents: Many models can process PDFs directly without text extraction preprocessing
Video: Frames from video can be processed, though full native video understanding is still limited

The model can reason across all of these modalities together. "Here's a chart and here's the accompanying report — what's inconsistent between them?" is a real, answerable query.

Where Multimodal AI Is Actually Creating Business Value

Document and Form Processing

The most immediate business application. Before multimodal AI, processing scanned documents, forms, invoices, or contracts required OCR (optical character recognition) as a preprocessing step, which introduced errors and required significant cleanup.

With models like GPT-4o, you can send an image of a document directly and ask: "Extract the invoice number, date, vendor name, line items, and total." No OCR preprocessing. Better accuracy on degraded documents and unusual layouts. Handles handwritten notes mixed with printed text.

We've replaced several OCR-based data extraction pipelines with direct image processing in the past six months. Accuracy improved on average by 15-25%, and the pipeline complexity dropped significantly.

Visual Quality Inspection

For businesses that produce or handle physical products, AI vision can be incorporated into quality control workflows. Not just "is there a defect" binary classification (that's been possible with smaller computer vision models for years), but more nuanced assessment: "Describe the condition of this item and note any issues that would affect its sale."

The combination of visual understanding and language means the system can generate descriptive text about visual inputs — useful for quality reports, condition assessments, inventory descriptions.

Chart and Graph Analysis

Financial reporting, data analysis, business intelligence dashboards — all produce charts and graphs that humans read and reason about. Multimodal AI can now read these charts directly and reason about them: "Here are last quarter's sales charts. What's the most notable trend?"

This sounds simple. For non-technical stakeholders who receive reports but don't have direct database access, it's genuinely useful — the AI can bridge between visual data representations and natural language questions about them.

Meeting and Voice Workflows

The pipeline: record meeting → transcribe audio → run AI analysis on transcript → extract action items, decisions, and key points → write to CRM or project management tool.

This has been possible for a while with separate transcription (Whisper, Deepgram, etc.) feeding into text AI. What's changing: tighter integration, better transcription quality, and models that can be given the audio directly rather than a text transcript, preserving tonal and contextual signals that transcription loses.

For businesses that run a lot of client or internal meetings, automated meeting intelligence is one of the highest-ROI multimodal applications.

Product Catalog and Inventory Management

For e-commerce, retail, or any business with a large product catalog: AI that can look at a product photo and generate descriptions, extract specifications, classify into categories, or identify similar products in the catalog.

The manual equivalent — humans photographing, describing, and categorising inventory — is a significant operational cost for product-heavy businesses. AI vision can handle a large fraction of this automatically, with humans reviewing edge cases.

What to Think About Before Building

Accuracy requirements: Multimodal AI is impressive but not infallible. For document extraction tasks, validate accuracy against ground truth before deploying at scale. For quality inspection, understand what error rate is acceptable and what the false negative/false positive costs are. Cost: Processing images costs more than processing text, in terms of both API costs and latency. A high-volume document processing pipeline needs careful cost modelling before it's built. Data privacy: Images often contain sensitive information — personal data, confidential business information, proprietary specifications. Apply the same data handling standards to images that you would to text. If you wouldn't send the textual content to an API, don't send the image of it. Hybrid approaches: For many document processing use cases, a hybrid of specialised computer vision models (good for structured extraction from known document layouts) and multimodal LLMs (good for unstructured, varied document types) outperforms either alone. Don't assume multimodal LLMs replace all existing vision tooling — they're often complementary.

The Practical Starting Point

If you're evaluating multimodal AI for your business:

Identify your highest-volume visual data flows. Where are people currently looking at images, documents, or charts and then manually entering or processing information? That's your candidate.

Run a quick experiment. Take 20-30 representative examples from that workflow and test with GPT-4o or Claude 3.5 directly via their web interfaces. Manual prompting, no code. Get a sense of accuracy and quality before committing engineering time.

Measure accuracy on your actual data. Don't assume benchmark performance transfers. Test on your specific documents, your specific images, your specific charts.

If accuracy is good enough: Scope the integration. How does the image get to the AI? Where does the output go? What's the exception handling for cases the AI gets wrong?

Multimodal AI is not magic. But for the right workflows, it removes friction that was previously unavoidable.

Want to implement AI in your business? Book a 30-minute architecture call at blok-ai.com.