AI SOLUTIONS

AI SOLUTIONS

What is an AI agent and how is it different from ChatGPT?

ChatGPT is a universal assistant that can answer questions, write texts, and explain concepts. But it doesn’t know your business, doesn’t have access to your CRM, and can’t perform actions on its own—it can only give advice.

The AI ​​agent is ChatGPT (or Claude, Gemini), which:

• Knows your business — trained on your data, products, customers, processes.
• Has access to tools – CRM, Google Sheets, Notion, mail, Telegram, API of your systems.
• Performs actions — adds a lead to CRM, sends an email, creates a task, generates a report, analyzes data.
• Works 24/7 without weekends, sick leave or holidays.
• Scales instantly — one agent can handle 10 or 10,000 requests per day.

In simple terms: ChatGPT is a smart consultant to talk to. An AI agent is an employee who performs the work himself according to the algorithm you have given him.

What AI agents are we implementing?

Ready-made solutions for the most common business tasks. Each agent is customized to the specifics of the client – integrates with their tools, learns from their data and processes.

AI assistant on the website

A chatbot that communicates with website visitors in real time: answers questions about the product, qualifies leads, transfers the request to CRM with the context already collected. Works 24/7, speaks the client’s language (Ukrainian, English, Polish, etc.), does not get confused in complex issues.

Typical tasks: qualifying leads before the manager’s first call, FAQ for customers, scheduling a consultation/demo, informing about the order status.

AI-sales

AI agent for commercial communications: processes new leads from forms and chats, qualifies them according to criteria (BANT, MEDDIC or client’s own methodology), schedules calls, responds to initial customer inquiries. Suitable for businesses with a large volume of incoming leads, where managers do not have time to process all of them in the first hour (critical time in B2C and B2B).

Typical tasks: instant response to new leads, initial qualification, sending materials (price list, commercial offer), scheduling meetings, follow-up on cool leads.

AI assistant for the owner

A personal AI assistant for a manager, integrated into his work stack: mail, calendar, Google Drive, Notion, CRM. Summarizes correspondence for the day, prepares a digest of important events, creates a brief before a meeting with a client (extracts the history of interaction with CRM), searches for information in internal documents.

Typical tasks: morning digest, meeting preparation, document search, quick analytical queries (“how much did we sell of product X this quarter”).

AI agent for reporting

Automates the preparation of regular reports – for the manager, investor, team, client. Collects data from various sources (GA4, Meta Ads, Google Ads, CRM, Google Sheets), generates a structured report with comments and conclusions. Publishes to Data Studio (Looker) Power BI, Slack, email or Telegram.

Typical tasks: weekly summary of marketing metrics, monthly report for the manager, report for investors, automatic alerts for significant deviations.

AI agent for data analysis

Processes large amounts of unstructured data — customer reviews, support conversations, call transcripts, social media comments, surveys — and extracts insights from them: key customer pain points, complaint patterns, trends, and opportunities.

Typical tasks: analyzing reviews for a year to understand what customers praise and criticize; quarterly analysis of sales calls to identify topics where some deals are lost; monitoring brand mentions on social media.

AI agent for social media analysis

Monitors social networks – yours and your competitors. Analyzes which posts “got through”, which topics are winning, which formats are growing. Prepares a regular report with recommendations for content strategy. Especially useful for brands with active SMM and for agency clients who run several projects.

Typical tasks: weekly analysis of competitors’ content, identification of trending topics in the industry, monitoring of brand mentions with a negative tone.

AI agent SMM manager

Generates ideas and drafts of posts based on the topic, brand tone of voice, and previous successful publications. Does not publish automatically (our position is that the final decision is up to the person), but prepares up to 80% of drafts that the SMM manager refines and publishes.

Typical tasks: generating a monthly content plan, rewriting publications for different platforms (Instagram/LinkedIn/Facebook), adapting messages for different audiences, quick content for trends.

Custom AI solutions

If your specific needs require an agent that is not on the standard list, we will design one for the task. For example: AI for automating invoice processing, an agent for analyzing resumes in HR, an agent for training newcomers using the internal knowledge base.

During the brief, we discuss the task, assess whether it is realistic to solve it using AI (sometimes the honest answer is “no, classic automation is needed here”), and form the terms of reference and budget.

What technology stack do your AI agents run on?

Modern AI stack 2026. The specific choice depends on the task:

• — LLM — OpenAI GPT (GPT-4o, GPT-5), Anthropic Claude, Google Gemini. We choose for the task — for complex analytical — Claude, for quick dialogues — GPT-4o Mini, for working with the Google ecosystem — Gemini.
• Agent frameworks — LangChain, LangGraph, AutoGen, Proplexity, N8n, CrewAI for multi-agent systems.
• Vector databases — Pinecone, Weaviate, Qdrant — for RAG (Retrieval-Augmented Generation) so that the agent knows your knowledge base.
• Process automation — n8n, Make (Integromat), Zapier for integrating agents with business tools without custom code.
• Integrations — CRM (HubSpot, Salesforce, Pipedrive, Keycrm), Telegram Bot API, WhatsApp Business API, Google Workspace API.

For simple tasks, we use low-code tools — faster and cheaper. For complex ones — custom development through code. Based on the brief, we choose the optimal stack.

What does the process of implementing an AI agent look like?

Stage 1. Brief and task evaluation.

We discuss your problem. The main question is whether it is realistic to solve it using AI. If the problem is not suitable (for example, it requires complex human judgment or working with the physical world), we will honestly say so and offer an alternative.

Stage 2. Vehicle and prototype.

We prepare a technical specification: what data the agent uses, what tools we integrate, what actions it performs, how we validate quality. For most tasks, we make a rapid prototype in 1–2 weeks — we test the concept on real client data.

Stage 3. Training and configuration.

We train the agent on your data: product texts, customer communication history, knowledge base, brand tone of voice. We configure prompts, rules, restrictions. We integrate with your tools.

Stage 4. Testing with the client team.

Before launching in production — 2–3 weeks of testing: your team asks the agent real requests, we fix errors, and refine the logic. Without this stage, the AI ​​agent in 50% of cases gives unexpected answers in the first week of operation.

Stage 5. Launch and monitoring.

The agent works in production. We monitor the quality: the percentage of correct answers, the percentage of cases where the agent failed, customer feedback. We configure filters and the logic for transferring complex requests to a live person.

Stage 6. Iterative improvement.

An AI agent is not “set and forget.” It is a system that requires regular maintenance, otherwise the quality degrades over time – customer needs change, new products appear, business processes are updated, new versions of LLM are released. What exactly is included in iterative improvement:

• Monthly log analysis.We review the agent’s dialogues: where he got confused, where he transferred the request to a person, where the client was dissatisfied. Based on this, we identify problem scenarios and retrain the agent – add examples, clarify instructions, and adjust logic.
• Knowledge base update.If you have a new product, prices have changed, or your return policy has been updated, we update the agent’s knowledge. Without this, the agent starts giving outdated information, which is worse than not having an agent at all.
• Scenario expansion.If the team notices that customers regularly ask questions that the agent cannot answer, we add a new scenario. This way, the agent gradually closes more and more typical cases.
• A/B testing of prompts.We test alternative wording, tone of communication, response structure. Often, changing one sentence in a prompt can increase conversion or customer satisfaction by 10-15%.
• Migration to new models.Every 3-6 months we evaluate whether it is advisable to switch to a new version of LLM. GPT-5 may be more accurate than GPT-4o for your task, or vice versa – Claude Haiku 4.5 will be cheaper without losing quality. This is a separate technical work with re-testing.
• Technical monitoring.We monitor response speed, API stability, and LLM token costs. If something breaks or costs suddenly increase, we react before the client notices it.
• Quarterly report with metrics.Every 3 months — a structured review: how many requests the agent has processed, what percentage was resolved without transferring to a person, what is the ROI in monetary terms, what has improved, what needs to be changed. Based on this report, we decide together with you where to develop the agent further.

From experience: a properly supported AI agent, after 6 months of operation, shows 1.5–2 times higher quality than at the time of launch. Without support, it gradually degrades and starts giving strange answers to non-standard requests.

How long does it take to implement?

Depends on the complexity of the agent:

• A simple AI assistant on the website (FAQ, lead qualification) — 1–2 weeks from brief to launch.
• AI sales with CRM integration — 2–3 weeks.
• Reporting agent with integration of multiple data sources — 1–2 weeks.
• Data analysis agent with large-scale training — 2–4 weeks.
• Custom solution with non-standard logic — from 4 weeks, estimated individually.

Plus 2–4 weeks of testing with the client team before production launch. This is not extra work — it is a critical part of the process. AI that has not been tested with real client requests will give unexpected answers in 50% of cases in the first month.

How much do AI solutions cost?

The cost is formed from two components:

Implementation (one-time payment).

It depends on the complexity of the agent, the depth of integrations, the volume of training data. Simple agents start at a few hundred dollars, complex custom solutions start at a few thousand. We will form a specific figure after the TOR.

Monthly payment.

Includes: LLM-API cost (OpenAI, Anthropic, Google — paid by the client directly or by us), agent hosting cost, technical support and monitoring, additional training. For most agents — a fixed rate calculated based on the expected volume of requests.

LLM-API is a separate expense item. Its size depends on the volume of requests and the model. For the assessment, we forecast the expected monthly volume in the brief and provide a transparent calculation.

Who owns the AI ​​agent and the data?

Customer. Always. Specifically, this means:

• All accounts (OpenAI, Anthropic, Pinecone, etc.) are registered in the client’s name, and access is granted to the client.
• Prompts, agent logic, configurations — we document and transfer them to the client.
• The data on which the agent is trained remains with the client and is not transferred to third parties.
• If the client terminates the cooperation, the agent continues to work. We transfer all the documentation so that another contractor can pick it up.

Separately about data security: for clients with sensitive data (medicine, finance, legal services), we configure agents on closed models — Anthropic Enterprise, OpenAI Enterprise, self-hosted LLM (Llama, Mistral) — where data is not used to train models.

Will an AI agent replace my team?

In the vast majority of cases, no, and we deliberately do not sell AI as a “human replacement.” A more correct frame is that AI frees the team from routine so that it can focus on high-value tasks.

Practical examples:

• — The AI ​​assistant on the site handles 70% of typical questions. Managers receive only qualified leads and complex cases. The team is the same, but closes 2–3 times more deals.
• — An AI reporting agent produces standard weekly reports in 10 minutes instead of 4 hours for a marketer. The marketer spends those 4 hours on strategy and hypothesis testing.
• — AI sales processes new leads in 30 seconds instead of 30 minutes for a manager. Managers receive leads with context and start the conversation from the right point.

The exception is businesses where routine tasks accounted for 80%+ of the work (mass document processing, simple client inquiries). There, AI can really replace part of the team. But there are fewer such businesses than you might think.

What risks and limitations of AI agents should be understood?

To avoid disappointment, we honestly state the risks in the brief:

AI is hallucinating.

In 1–5% of cases, LLM can invent a fact that sounds plausible but is false. For critical tasks (medical consultations, legal advice, financial recommendations) – not suitable without human verification. For lead processing, FAQ, reporting – the risk is acceptable and manageable.

Dependence on API providers.

If OpenAI or Anthropic change their pricing or policies, it will affect your costs. We design agents to be able to quickly migrate between providers (GPT → Claude → Gemini) without a complete redesign.

The cost of scaling.

The first month an agent costs one thing, and as the volume scales, it costs another. We always forecast costs for increasing volumes and warn you.

Cultural acceptability.

In some segments, customers don’t like to communicate with bots. For such cases, we set up a hybrid model – AI handles simpler requests, and a human connects to complex ones.

Where to start implementing AI in your business?

There is a typical route that we recommend most clients take:

• Step 1. Identify the most routine and repetitive task in the team. Often this is processing incoming requests, preparing reports, answering FAQs.
• Step 2. Choose one agent for this task — not “AI for everything at once.”
• Step 3. Launch the pilot, measure ROI in 2–3 months.
• Step 4. If the pilot is successful, scale to neighboring tasks. If not, reconsider the approach or abandon the idea.

This approach avoids the common mistake of “implementing AI for everything at once,” which often ends in failure. Small successes are the foundation for major transformations.