We design the goal and scope of the agent
We start with one process that truly hurts and can be measured. The agent gets a clear goal, not “be smart".
Service: AI agents
Hire an AI agent that works while you sleep.
We build autonomous agents that take over repetitive work: customer service, outreach, research and content. They scale your team's capacity, without new hires and without breaks for sleep.
Free consultation. No commitment.
What does this service do?
AI agents: An AI agent is software that autonomously runs multi-step tasks: it understands a goal, plans the steps, uses your tools and data, and learns from outcomes. Unlike a chatbot, it doesn't wait to be asked, it acts and remembers what it did before. We build agents on proven open-source frameworks, with access to your data through RAG and human oversight over key decisions.
The problem we solve
Your team drowns in repetitive work: answering the same questions, manual research, copying data.
How we work on AI agents.
We connect your knowledge (RAG)
The agent uses your documents, product base and history to answer from your knowledge, not make things up. This is RAG: answers based on your data.
We add memory and learning
The agent remembers previous conversations and tasks, and improves over time. The longer it runs, the more effective it gets.
We define tools and integrations
The agent does not just talk, it acts: sends emails, updates CRM, creates documents, searches the web. We connect it to your tools.
We set safety checks and human oversight
The agent has limits, rules and points where it asks a human before an important decision. We do not let it run unsupervised where stakes are high.
We launch, measure and scale
We launch, measure saved time and quality, and expand the agent to more processes once the first one proves itself.
Technology stack.
LLM models
- Claude
- GPT
- Gemini
Agent frameworks and orchestration
- LangGraph
- CrewAI
- Vercel AI SDK
- OpenAI Agents SDK
- Hermes
- OpenClaw
RAG, memory and protocols
-
MCP
Explanation: Model Context Protocol, a standard for connecting AI models to company tools and data. -
RAG
Explanation: A technique where AI answers from your documents and data. - Pinecone
- Qdrant
- Mem0
We choose the framework and model for the process, data and level of human control over agent decisions.
Mindgram
AI in operations
At Mindgram, we implemented AI automations that replaced work equal to around 25 full-time roles. We bring the same knowledge of what can be automated, and what should not be, into your agent.
~25 FTEsaved through AI automation - Mindgram
Service-specific questions
How is an AI agent different from a chatbot?
A chatbot answers questions. An agent acts: plans, uses tools, completes multi-step tasks and remembers what it did. A chatbot talks, an agent does.
What data do I need to share and is it safe?
The agent only needs access to data required for the task. We build on frameworks that can run on your infrastructure, so data does not have to leave your environment. We agree GDPR compliance at the start.
What happens when the agent makes a mistake?
We set safety checks: action limits, rules and points where the agent asks a human before an important decision. In high-risk areas, the agent proposes and a human approves.
Will the agent replace someone on my team?
Usually no. It removes the repetitive, boring part of work from people. The team handles what needs a human: relationships, decisions and exceptions.
How much does it cost to build and run an agent?
Build cost depends on the process scope and integrations. Running cost is the model cost (you pay for usage) plus care. After the first call, we give ranges and a monthly cost estimate.
How long does it take to launch the first agent?
The first agent for one concrete process usually takes a few weeks. We start narrow, check the effect, then expand.
Which models and technologies do you build on?
We choose the technology for the task: cloud or local models, access to company knowledge and secure connections with tools. We do not lock you into one provider.
Will the agent speak and write in my company tone?
Yes. We set the persona, style and communication rules, and connect your materials so the agent sounds like your company, not like a generic bot.
What if the agent gets a question it does not know?
The agent says it does not know, or escalates to a human, instead of making things up. We set this deliberately, because making things up is more dangerous than saying “I do not know".
Can I start with a small scope?
Yes, and this is what we recommend. We start with one process, measure the effect, and only then expand the agent or add more agents.
Who owns the solution?
You do. The code, configuration, data and accounts belong to you. We build on open frameworks, so we do not lock you in.
How will I know the agent pays off?
Before we start, we agree what to measure: saved time, number of handled cases, cost per task. The report shows the return from the launch directly.
From the knowledge base