Service · AI in your product, Agents & MCP
AI in your productIntegration, 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.
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.
Done right, MCP lets the AI:
- Read documents from Google Drive
- Update records in your CRM
- Send emails
- Run database queries
- Move tasks in Jira or Asana
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.
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
- 01
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.
- 02
Pick the right model
Claude, GPT, Gemini, an open model, or fine-tuning? Each has its strengths. We pick by need — not by hype.
- 03
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.
- 04
Development & testing
We build the integration and test on real cases. An AI giving inaccurate answers is worse than no AI at all.
- 05
Cost optimization
AI API calls cost money. We optimize: caching common answers, smaller models where they fit, efficient prompts.
- 06
Monitor & improve
AI isn't perfect. We monitor outputs, gather feedback, and improve prompts and architecture over time.
05 / A pilot before commitment
AI in your product, with a clear test process
Before AI goes into a product you need to know it performs the task at the level you need — on your data, not in a demo. This is what a pilot you can decide on looks like, and what it takes to turn it into a stable product.
Is AI right for the need? Four questions before a pilot
What the pilot includes
How quality and failures are tested
| Test | What is done | What must be defined |
|---|---|---|
| A scenario set | A collection of real questions or cases (approved for use) with the answer or outcome a person would accept, including edge cases and questions the sources cannot answer | Who builds the set, who judges, and which part of it is held back for the final check |
| Behavior when there is no answer | The system says it does not know, points to a source or a person — instead of inventing | The wording, when to escalate to a person, and how it is measured in the scenario set |
| Approval of sensitive actions | Every action that changes data, sends a message or charges money passes human approval in the pilot | The list of sensitive actions and who approves |
| Tracking and logging | Every question, retrieved source, answer and judgment is stored — so failure patterns are found, not single cases | What is stored, for how long, and who sees it |
What production requires
Relevant experience — exactly what was built
At OneAI, a conversational AI platform, we worked for five years as technology partner — from language AI APIs, through chat agents, to a voice AI system running business conversations, including a conversation scenario builder. The platform was acquired by monday.com in 2026. Exactly what was built is described on the case page — and nothing beyond it.
06 / Who it's for
Who this is for
07 / Why us
Why us
FAQ
Frequently asked questions
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.
Claude, GPT, Gemini, and open models (Llama, Mistral) when they fit. We pick by task.
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."
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.
There is no honest flat price for an AI integration: the budget follows scope. The drivers are the number of data sources and how clean they are, whether the model needs to act (tools, MCP) or only answer, how failures are tested and reviewed, and what production requires in terms of permissions, logging and cost controls. We scope the work first, then give a fixed, itemized quote — what is included, what is excluded and what would change the number. A pilot on real data is how we size the production work.
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.
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.
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.
Not in 2026. Claude, GPT and Gemini work well across major languages — understanding, writing and answering at a high level. What matters is that your evaluation set covers every language you serve, so quality is measured rather than assumed.
Two approaches: an external API (fast to build, needs internet) or on-device (maximum privacy, works offline). It depends on the case.
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.
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.
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.
A pilot tests one question — does the AI perform the defined task at the level you set, on your scenarios — with a small user group, defined information sources and human oversight. A stable product adds what a pilot does not need: permissions for every user, monitoring, a usage budget, a process for updating the model and the content, and support. A successful pilot is a condition for continuing — not the product itself.
The target is set with the process owner before the pilot, against the existing alternative: how many answers in the scenario set must be at a level a person would approve, how much time it saves compared with the manual process, and what the usage cost per action is. We do not commit to an accuracy percentage before measuring it on your data — the pilot's result is the number, and the decision is made on it.
Keep reading
- ServiceAI automation for businessThe process side of the same capability: specifying and building automation for repetitive manual work, wired into the CRM, ERP and systems you already run.
- Case studyCase study: OneAI — five years of AI, acquired by monday.comA long-term technology partnership: from a language AI and API platform, through chat agents, to a voice AI system — and the 2026 acquisition by monday.com.
- ComparisonVibe Coding vs production engineering — when each one fitsMVPs from Cursor/Lovable/Bolt vs production-engineered code. What AI tools handle, where they break, and how to migrate between the two.
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.