// Service · AI in your product, Agents & MCP
AI in your product
Integration, Agents, MCP
Everyone says they're "now powered by AI." Few actually understand what that means.
In the past year, every software product started "offering AI." In most cases that means a ChatGPT wrapper bolted onto an existing UI. A chatbot that gives generic answers. A "summarize this" button that doesn't really understand the product. That's not AI in a product. That's set dressing.
Real AI in a product changes what the product can do. It understands context, accesses the right data, takes real actions, and serves the user in ways that weren't possible without it.
We build that. Not a wrapper — real integration.
// 01 · What we do
What we do
From integrating AI into existing products to autonomous agents and MCP Servers that connect AI to your world.
- 01
AI integration into existing products
We take a working product and add AI capabilities that actually improve it. Summaries, semantic search, task automation, content generation.
- 02
AI Agents
Not chatbots. Agents that execute multi-step tasks autonomously: send emails, analyze data, update systems, make decisions.
- 03
MCP Servers
Building MCP Servers that connect your AI to your systems. Google Drive, Salesforce, internal databases, enterprise systems. AI that acts in the real world — doesn't just answer.
- 04
RAG & knowledge bases
Connecting AI to your specific knowledge. Internal docs, product specs, contracts, data. AI that answers from your information — not Google's.
- 05
AI assistants in SaaS
A Copilot for your product. Guides users, answers questions, takes action mid-flow.
- 06
AI in the backend
Classification, text analysis, smart OCR, image analysis. AI that works behind the scenes.
- 07
Language & document processing
Contracts, invoices, medical records, emails. Extraction, classification, semantic search.
// 02 · MCP
What MCP is and why it changes the game
MCP — Model Context Protocol — is the standard Anthropic released in 2024, which became the industry de-facto standard in 2025.
The idea: AI is smart, but without access to your data and systems it's limited. MCP is the protocol that connects AI to your systems in a secure, structured way.
That turns it from "a chatbot that gives advice" into "an assistant that gets things done." We build dedicated MCP Servers that connect AI to your specific systems.
Done right, MCP lets the AI:
- 01
Read documents from Google Drive
- 02
Update records in your CRM
- 03
Send emails
- 04
Run database queries
- 05
Move tasks in Jira or Asana
// 03 · Technology
Our stack
Models, frameworks, vector DBs, MCP, embeddings, orchestration and fine-tuning. We pick the right tool for every integration.
- AI Models
- Claude (Anthropic)
- GPT (OpenAI)
- Gemini (Google)
- Open Models
- Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Vector DBs
- Pinecone
- Weaviate
- Qdrant
- pgvector
- MCP
- Anthropic MCP SDK
- Custom MCP Servers
- Embeddings
- OpenAI
- Cohere
- Voyage AI
- Orchestration
- Temporal
- n8n
- On-Device AI
- Core ML
- Ollama
- Fine-tuning
- OpenAI Fine-Tuning
- Hugging Face
// 04 · How we work
How we work
- 01STEP 1
Understand the business case
Not every problem needs AI. And not every feature gets better with AI. We start by understanding the problem — then decide whether AI is the right answer.
- 02STEP 2
Pick the right model
Claude, GPT, Gemini, an open model, or fine-tuning? Each has its strengths. We pick by need — not by hype.
- 03STEP 3
Design the architecture
How does AI connect to the product? When does it run? What context does it get? What actions is it allowed to take? Plan before build.
- 04STEP 4
Development & testing
We build the integration and test on real cases. An AI giving inaccurate answers is worse than no AI at all.
- 05STEP 5
Cost optimization
AI API calls cost money. We optimize: caching common answers, smaller models where they fit, efficient prompts.
- 06STEP 6
Monitor & improve
AI isn't perfect. We monitor outputs, gather feedback, and improve prompts and architecture over time.
// 05 · Who it's for
Who this is for
- 01
SaaS products
That want to add a Copilot, a smart assistant, or AI capabilities to existing features.
- 02
Service companies
Handling lots of repeat inquiries — AI can take care of most of them.
- 03
Organizations with internal docs
Knowledge bases, procedures, documents. AI can help employees navigate them.
- 04
Analytics products
That need to process text, surface insights, or summarize data.
- 05
Businesses that want to automate
Combining MCP and AI for real workflows — not just chat.
// 06 · Why us
Why us
- 01
We build — we don't just talk
Many companies talk AI. We work with it every day.
- 02
We track the market
Our CTO stays updated daily. New models, new MCP, new capabilities — we're on it.
- 03
We understand the whole stack
Good AI needs good infrastructure. Fast queries, caching, stable integrations.
- 04
Not in love with the tech
If AI isn't the right answer — we'll say so. We won't push a feature just because it's trendy.
// 07 · FAQ
Frequently asked questions
01.What's the difference between AI I can wire up myself (ChatGPT API) and a professional integration?
The ChatGPT API gives you a model. A professional integration builds everything around it: architecture, efficient prompts, caching, context management, integration with your data, cost monitoring. It's the difference between a toy and a product.
02.Which models do you use?
Claude, GPT, Gemini, and open models (Llama, Mistral) when they fit. We pick by task.
03.What's an Agent and how is it different from a chatbot?
A chatbot answers a question. An Agent acts. It can execute multi-step tasks: "Check what this customer ordered, send them a summary email, and open a ticket if there's an issue."
04.So what is MCP, really?
A protocol that connects AI to external systems securely. Instead of the AI knowing only what it was trained on — it can read documents, take actions, and update systems in real time.
05.How much does an AI integration cost?
Depends. A simple integration (chatbot with RAG) — $8K–$22K. An advanced in-product AI assistant — $22K–$85K. A complex AI agent with MCP — $40K–$140K.
06.There are ongoing costs too, right?
Yes. Every API call costs money. We optimize to bring it down (caching, efficient models, precise prompts), but there's still a running cost. Usually a small percentage of the value the product creates.
07.What about privacy? Does our data go to OpenAI?
Depends on the model and the configuration. OpenAI and Anthropic offer Enterprise tiers that don't use your data for training. And there are open models you can run on your own infrastructure. We help choose based on data sensitivity.
08.What is RAG?
Retrieval Augmented Generation. The AI doesn't rely only on what it knows — it searches your knowledge base in real time and answers from there. That's how it can address questions specific to your product.
09.We want AI that understands Hebrew — is that a problem?
Not in 2026. Claude, GPT, and Gemini work great in Hebrew — understanding, writing, and answering at a high level.
10.What about AI on mobile?
Two approaches: an external API (fast to build, needs internet) or on-device (maximum privacy, works offline). It depends on the case.
11.When is adding AI not worth it?
When the problem doesn't need it. If you can solve something with a simple "if-then" rule — AI is overkill. We'll say so straight.
12.Do you also do fine-tuning?
Yes. When there's a real need — a very specific domain, a precise response format, a unique style — fine-tuning can give a better result at a lower cost.
13.We want our own MCP Server to connect our internal tools. Do you build that?
Yes. It's one of the areas we work on most. We'll build an MCP Server that connects your specific tools to your AI.
// 08 · Let's talk
Real AI, not set dressing.
A 30-minute intro call. No strings attached. We'll figure out together if AI is the right answer — and if so, how to do it right.
Straight. To the point. On time.