Dedicated to process
Agent knows company context, integrates with systems, executes actions. Doesn't just suggest — actually supports the process.
AI Agent — dedicated assistant for a specific process in your company
We deploy AI agents based on Claude (Anthropic), OpenAI or Microsoft Copilot Studio. Each agent designed for a specific process: customer service, documents, HR, sales. Connects AI with company systems and knowledge base. Go-live in 4–10 weeks.
01
Generic AI doesn't know company context
02
Employee loses time explaining the same context to AI
03
AI doesn't execute actions in systems — only suggests
Customer problem
Generic AI doesn't fit your processes — you need a dedicated agent
ChatGPT or Copilot are great general-purpose tools — but they don't know your processes, documents, customers. Every time, the employee has to explain context, provide documents for analysis, copy answers. AI doesn't execute actions in systems — only suggests.
A dedicated AI agent solves this completely. Designed for a specific process, knows your knowledge base (procedures, customers, products), integrates with company systems (CRM, ERP, calendar), executes actions (bookings, data updates, document generation). You pick the model: Claude for longer documents, OpenAI for broad integrations, Copilot Studio for M365 scenarios.
Why it matters
Generic AI doesn't know company context
Employee loses time explaining the same context to AI
AI doesn't execute actions in systems — only suggests
No governance: employees use randomly and riskily
Mismatched model for scenario (expensive, weak)
What we deliver
What we deliver in the implementation
A dedicated AI agent is a project: model selection, knowledge base, integrations, governance, effectiveness monitoring.
01
Process audit and agent selection
Identifying the process where AI agent gives biggest value. Goal, users, KPIs definition.
02
Model selection: Claude, OpenAI or Copilot Studio
Claude — longer contexts, multi-page documents. OpenAI — broad integrations, ChatGPT. Copilot Studio — M365 scenarios. Justified choice.
03
Knowledge base preparation
Structuring knowledge sources (SharePoint, Confluence, documentation, FAQs). Indexing via Azure AI Search or equivalent.
04
Agent persona and scenario design
Agent persona (tone, communication style), top scenarios, boundaries (what the agent won't do), escalation policy.
05
Company system integrations
Agent integrates with CRM (Monday, Salesforce), calendar (Outlook), order systems, knowledge base. Executes actions, doesn't just suggest.
06
Governance and AI policy
Agent usage policy, query monitoring, answer audit, conversation retention policy. AI Act and GDPR compliance.
07
User interface
Agent available in Teams (Copilot Studio), on website, as standalone app, or via API. Matched to team's work style.
08
Monitoring and 30 days of support
Dashboard with agent usage metrics: number of queries, top topics, answer quality, NPS, knowledge gaps. 30 days support with calibration.
Technology stack
Technologies we use
We pick the stack for the specific scenario — Claude, OpenAI or Microsoft Copilot Studio.
Claude (Anthropic) — long contexts, documentsOpenAI GPT-4 — broad integrations, ChatGPTMicrosoft Copilot Studio — M365 scenariosAzure OpenAI Service — enterprise hostingAzure AI Search — knowledge baseMicrosoft Teams / Power Apps — interfaceCRM / ERP integrations (REST API)
Your solution
Typical AI agents in companies
HR Agent — employee question answers
Employee asks about leave, benefit, policy. Agent answers based on current HR procedures. Submits requests on behalf of employee.
IT Helpdesk Agent — diagnosis and solution
Employee reports issue (computer, printer, access). Agent diagnoses, suggests solution, creates Jira ticket if needed.
Sales Agent — sales support
Sales rep asks about product, price, discount policy, customer history. Agent answers based on CRM and product docs.
Document Agent — analysis and search
Employee asks about procedures, contracts, documentation. Agent searches SharePoint, summarizes, cites specific sections.
Solution fit
Sprawdźmy, które elementy rozwiązania najszybciej ograniczą pracę manualną i uporządkują procesy w Twojej organizacji.
Impact and metrics
Effects of an AI agent rollout
Clients we have deployed dedicated AI agents for report similar effects in the first 2–3 months.
5h+
weekly per employee reclaimed
24/7
agent availability
70%
queries handled without human
100%
AI Act and GDPR compliance
Business benefits
Model choice for scenario
Claude for documents, OpenAI for broad integrations, Copilot Studio for M365. Each model used where it's best.
Full governance
Query monitoring, AI policy, retention, audit. Safe and controlled AI use in company.
Who this is for
Who this is for
Companies with recurring team questions
Organizations where HR, IT, sales get the same questions daily — AI agent takes them over.
Companies with extensive documentation
Organizations with 500+ documents (procedures, contracts, products) — AI agent gives fast access.
Companies seeking specific AI
Organizations for whom generic ChatGPT or Copilot isn't enough — need a dedicated solution for a specific process.
Companies with strong governance requirements
Regulated organizations (legal, finance, medical), where ChatGPT isn't an option — need controlled, auditable AI.
Implementation process
AI agent implementation process
We implement the solution in a structured model that clarifies project stages, integration with the current environment and further development across the organization.
Stage01
Audit and agent selection (1–2 weeks)
Identifying process with biggest potential. Model selection (Claude / OpenAI / Copilot Studio) justified by scenario.
Stage02
Knowledge base preparation (1–2 weeks)
Knowledge source structuring, Azure AI Search indexing, update policy.
Stage03
Agent build and integrations (2–4 weeks)
Persona and scenario design, prompt engineering, CRM/ERP/company system integrations. Teams / web interface.
Stage04
Pilot with reference group (1–2 weeks)
Pilot with 20–50 users. Quality monitoring, answer calibration, knowledge base completion.
Stage05
Go-live and 30 days of support
Full launch. Training. 30 days of active monitoring with agent calibration.
Stage 1 of 5
Process audit for AI
Model recommendation for scenario
Agent deployment plan with concrete ROI
FAQ
FAQ about AI agent
Claude or OpenAI or Copilot Studio?
Claude for long documents (100+ pages) and demanding analyses. OpenAI for broad integrations and ChatGPT scenarios. Copilot Studio for M365 scenarios (HR Bot, IT Bot available in Teams). Choice always justified by specific scenario.
How long does AI agent take to implement?
Typically 4–10 weeks. Simple agent (FAQ with knowledge base) — 4 weeks. Complex (integrations with 3 systems, actions, approvals) — 8–10 weeks.
Does the agent make up answers (hallucinations)?
Not when configured properly. Agent uses RAG (Retrieval Augmented Generation) from company knowledge base. If no answer — says so directly and escalates to human.
Will our data end up training models?
No. Azure OpenAI, Claude on Azure Bedrock, Copilot Studio — all have enterprise contracts guaranteeing customer data isn't used for training.
Does the agent execute actions or just reply?
Executes actions. We configure the agent to integrate with systems: create tickets, submit requests, update CRM records, book meetings. With appropriate permissions.
How does AI agent maintenance and scaling work?
Claude/OpenAI is billed per token, Copilot Studio per user — so the cost model scales smoothly with conversation volume. On our side it's typically a few consultant hours per month — monitoring answer quality, expanding the knowledge base and calibrating the agent.
What happens after 30 days of support?
We can offer development retainer (calibration, knowledge base completion, new scenarios) or leave you with ready agent under your IT's care.
Industry implementations
Agent AI for 15 industries
Each industry has its own use cases, ROI and implementation patterns. Check details for your industry.
Produkcja i przemysł
Agent AI w produkcji
See implementation
Handel hurtowy i dystrybucja
Agent AI w handlu hurtowym
See implementation
E-commerce i sklepy internetowe
Agent AI w e-commerce
See implementation
Logistyka i magazynowanie
Agent AI w logistyce
See implementation
Transport i spedycja (TSL)
Agent AI w transporcie
See implementation
Księgowość i biura rachunkowe
Agent AI w księgowości
See implementation
Kancelarie prawne
Agent AI w kancelariach prawnych
See implementation
Medycyna i przychodnie
Agent AI w medycynie
See implementation
Hotelarstwo i HoReCa
Agent AI w hotelarstwie
See implementation
Fintech i usługi finansowe
Agent AI w fintech
See implementation
Ubezpieczenia i brokerzy
Agent AI w ubezpieczeniach
See implementation
Agencje marketingowe i kreatywne
Agent AI w agencjach marketingowych
See implementation
Agencje HR i rekrutacyjne
Agent AI w agencjach HR
See implementation
Usługi IT i software house
Agent AI w firmach IT
See implementation
Doradztwo i konsulting
Agent AI w firmach doradczych
See implementation
Related materials
Related solutions
Related knowledge base articles
AI agent types and business applications
Different AI agent categories and when to use each
How to pick an AI model for business
OpenAI, Claude, Gemini comparison for enterprise
How to deploy an AI agent in a company
Practical deployment guide
AI agents vs chatbots — differences
How AI agent differs from chatbot
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In-depth analysis
AI Agent for business — what to know
Chatbots answer. Agents act. That's one of the most important distinctions in AI terminology in 2025. A chatbot grounded in company docs will reply: „Returns policy is 14 days from delivery date”. An AI agent, given the same query, will reserve a return slot in the warehouse system, generate the shipping label, email it to the customer and create a support ticket. It has tools, it has permissions, it makes decisions based on organizational knowledge. That's why agent rollouts actually move the needle on hours reclaimed for the team — not just on response time.
The stack choice matters and depends on the scenario. Claude (Anthropic) beats the competition on tasks requiring deep document understanding and precise citation — contract analysis, due diligence, compliance work. OpenAI GPT-4o has the broadest ecosystem of tools and integrations — it wins where the agent must call many external APIs. Microsoft Copilot Studio is the natural pick for companies deeply embedded in M365 — SharePoint, Teams and Outlook integration works without an extra layer. Within a single organization we often deploy different agents on different stacks.
In enterprise, standing the agent up isn't enough — it has to be anchored in governance. Every action the agent takes must be auditable (who, when, based on what). Every permission respects Azure AD and SharePoint roles. Every knowledge base has an owner, an expiry date and an update process. Without that compliance layer the agent won't pass internal audit, and in regulated industries (finance, healthcare, energy) it won't even be allowed near production. That's why our rollouts include build + governance + monitoring from day one, not as an afterthought.