Role and Expected Outcome
The agent is given not abstract 'intelligence', but specific tasks, input data, completion criteria, and cases for human handover.
AI agent development
Custom AI development — United States
We design a controlled AI solution around a defined business process, approved data, measurable quality, and human oversight.
Working with this market We agree the engagement format and project stages after reviewing the requirements. Project estimates for this market are prepared in USD; the final payment method and taxes are confirmed before work starts.
Companies in our city have a dedicated page: AI agent development in Brest.
What an AI agent consists of.
A model is just one element. A functional agent emerges when it's surrounded by knowledge, tools, memory, rules, and control.
Model and Routing
LLM Pilot determines intent and route. The appropriate model performs dialogue, analysis, or result preparation, considering cost and privacy.
RAG and Company Data
The agent searches for facts in documents, catalogs, instructions, and other authorized sources, rather than substituting them with the model's general knowledge.
API and operational actions
CRM, email, calendar, documents, website, messengers, and internal services are connected as separate tools with limited rights.
Task context and history
The system stores the client's authorized context, intermediate results, and an activity log, allowing the process to be continued and verified.
Rules, Confirmations & Audit
Format checks, limits, data protection, and explicit confirmation points prevent the agent from silently performing risky actions.
How the Agent Handles a Client Task.
The journey can be seen, measured, and paused at each step.
Receives event
A client message, new lead, email, document, CRM change, or employee task triggers an authorized scenario.
Understands intent and gathers context
The agent defines the goal, checks mandatory data, and requests missing information, without pretending to know the answer.
Searches for Knowledge & Builds a Plan
Identifies relevant sources, selects tools, and defines action sequences within its authority.
Performs safe steps
Reads data, calculates, classifies, prepares a draft or creates an internal task; each action is recorded.
Requires confirmation
Before external publication, spending money, sending an important message, or changing critical data, it shows exactly what will happen.
Returns result and metrics
Reports outcome, sources, errors, and cost; the result goes into the working system, not lost in chat.
AI PILOT's own products confirm the practical framework: platform, Pilot Code and online audit. We showcase working systems, rather than attributing unverified client results to them.
Agents we develop
We don't sell the same shell under different names. The agent's composition depends on the process, data, permissions, and desired outcome.
Requests & Sales Agent
Answers product questions, clarifies tasks, collects contacts, qualifies inquiries, and provides context to the manager.
Customer Support Agent
Searches for an answer in the verified knowledge base, creates a request, and calls an employee if data is insufficient or the case is unusual.
Document Agent
Finds information, compiles a draft, checks completeness, and sends the document for approval without self-signing.
Internal AI Assistant
Answers employee questions regarding regulations, products, and instructions, based on company materials.
Orchestrator Agent
Breaks down the task into steps, calls authorized tools, saves the result, and indicates where human intervention is needed.
Agent for Code and Operations
Helps analyze code, prepare changes, and perform verifiable technical operations within defined limits.
AI PILOT Public Roles.
These are not nine separate subscriptions. Roles serve as clear entry points into the common platform and are configured for specific business data and processes. The availability of each scope is confirmed before implementation.
Liza
Task, calendar, and meeting organization, and routing inquiries to the right process or specialist.
Marina
Handling inquiries and leads: context gathering, qualification, preparing the next step, and handover to a manager.
Irina
Analysis of primary documents and data preparation for accounting. Tax and payment decisions remain with the responsible specialist.
Leon
Searches connected materials, checks completeness, and prepares drafts without promising to replace a lawyer.
Anna
HR tasks, vacancies, questionnaires, and internal instructions with personal data control and human decisions.
Kira
Planning and preparation of content, images, and videos via KREATIV; paid generation is shown and confirmed separately.
Daniil
Semantics, audiences, media plan, and ad drafts. Launch and budget expenditure require separate confirmation.
Vlad
Analysis of market, competitors, channels, and customer journey; conclusions must be based on sources and measurable signals.
Pilot Code
Agent tool for code analysis, change preparation, and checks in a developer or studio environment.
Internal platform control roles are not sold as characters: the client receives a verifiable process, an activity log, and the AI PILOT result.
Business Integration
The agent doesn't connect 'to everything at once'.
First, we create a separate technical account or official OAuth access with minimal permissions. Then, we connect a test environment, describe allowed read and write operations, add limits and logging. Production actions are enabled one by one after verification.
We do not collect client passwords in chat. Access can be revoked; secrets are stored separately from prompts and documents; sensitive data is not transmitted to external models without an agreed route.
Preparing Training
AI Agent Creation Course
We are planning a practical course: from task setting and model selection to RAG, tools, memory, security, quality assessment, cost, and production launch. Not a "magic prompts" course, but building a verifiable working agent.
Course not yet for sale. We're gathering interest and tasks from future participants to base the program on real scenarios.
LLM Pilot
Proprietary agent layer — independent of a single model.
LLM Pilot — our proprietary model and orchestrator for intent detection, tool invocation, and knowledge base interaction. It excels in agent chains and routing. For complex dialogues or large contexts, the system can select a more suitable external or self-hosted model.
This is a hybrid architecture: proprietary control, knowledge, and tools remain within AI PILOT, while the model is selected based on quality, cost, privacy, and regional availability.
How We Launch.
First, we prove the benefit on one process, then expand tools and capabilities.
Process Analysis
We define input data, result, exceptions, error cost, and actions that cannot be delegated to AI.
Gather Knowledge and Access
We connect documents, rules, API, and a test environment with the minimum necessary permissions.
Build and test agent
We configure routing, tools, limitations, and run real-world scenarios, including error handling.
Launch under supervision
We evaluate quality, time, cost, and human intervention; only then do we expand autonomy.
Related Solutions.
An AI agent becomes useful when it gains knowledge, tools, and a place in a real process.
Frequently asked questions
How does an AI agent differ from a chatbot?
A chatbot primarily conducts dialogue. An AI agent can work on a task: retrieve data, use authorized tools, prepare results, and submit risky actions to a human for confirmation.
Does the AI agent only work with your model?
No. LLM Pilot is used as its own agent and routing layer. Complex tasks are, if necessary, directed to a suitable external or self-hosted model.
Can the agent be connected to our systems?
Yes, after checking access and APIs: to the site, CRM, mail, documents, calendars, messengers, and other work services.
What does the agent do without confirmation?
Searches, analyzes, and prepares drafts within permissions. Publications, payments, and critical data changes are designed with human confirmation.