AI Agent Development Services
Use AI agent development services to build and fine-tune intelligent systems that automate complex, labor-intensive business operations and solve industry challenges with precision.
75
client locations
18
years in the IT market
500
employees
650
delivered projects
Our custom AI agent development services
01
AI agent strategy consulting
SoftTeco offers AI agent strategy consulting services to help companies figure out where and how AI agents will deliver real business value. We identify high-impact use cases, assess AI readiness, design the AI agent architecture, build a detailed roadmap with risk evaluation, and set deliverables at every stage.
02
Custom AI agent development
We design custom AI agents powered by major LLM platforms, such as GPT-4 and Claude, or set up open-source models for rapid implementation. Our ML engineers select the most suitable tools, technologies, as well as design and build single- or multi-agent infrastructure and backends based on your business needs.
03
AI agent training and fine-tuning
SoftTeco’s experts train the ML model from scratch or fine-tune an already existing LLM in order for an AI agent to reduce manual effort and automate processes. During this phase, we feed the model domain-specific data, adjust its parameters for better performance, choose a fine-tuning method, and design an optimization strategy.
04
AI agent optimization
If your AI agent system is already up and running, you may come up against issues that can disrupt its operation. Whatever the request, we can optimize the AI model’s performance through prompt engineering, minimize hallucinations, speed up response times, or improve workflow coordination among AI agents in a multi-agent system.
05
AI agent security and compliance
To ensure your AI agent works as expected and meets stringent security standards from the ground up, count on SoftTeco. Our engineers conduct all needed security tests by using automated tools, handle human-in-the-loop (HITL) evaluation and comply with relevant regulations, like HIPAA and GDPR.
06
AI agent integration
We help companies integrate an AI agent with the tools and services they need, such as CRM, ERP, financial systems, or custom-built platforms, via secure APIs, SDKs, and webhooks. SoftTeco’s engineers choose the best orchestration method and handle workflow automation, and data synchronization to ensure uninterrupted agent operations.
07
AI agent maintenance and monitoring
After deployment, we provide ongoing support, maintenance, and updates for your AI agent to perform post-deployment optimizations and minor fixes. If needed, our experts can retrain models, add new features and integrations, as well as optimize system performance for the best possible work.
Build multi-functional agents with our strategic
and expert assistance.
Types of AI agents we develop
Rule-based/simple reflex agents
Build simple reflex agents that carry out engineer-defined rules based on domain-specific data to respond to environmental conditions. Such agents cannot take past or future events into account when deciding on an action.
Use cases:
- Simple chatbots
- Factory safety monitoring
- Decision-making scenarios
Model-based reflex agents
Choose model-based reflex agents that rely on predefined rules and combine current perceptions with data stored from the past interactions for real-time action. They have memory and the ability to understand the environment context.
Use cases:
- Autonomous warehouse robots
- Game AI characters
- Cleaning robots
- Dynamic pricing systems
Goal-based agents
Opt for goal-based agents that are able to respond to the environment and adjust its actions in order to select the most effective way to reach the predefined goal. They are capable of solving complex problems, predicting future scenarios, and adapting to changing conditions.
Use cases:
- Robotics
- Resource management
- Patient monitoring
- Autonomous vehicles
- Ecommerce recommendation systems
Utility-based agents
Take advantage of utility-based agents that mathematically predict the usefulness of all the potential actions the AI agent can take to choose the next most beneficial decision. To do this, the agent needs a utility function, which is a way to measure how good each option is.
Use cases:
- Smart homes
- Self-driving cars
- Tracking management systems
- Energy management systems
- Logistics automation
- Supply chain automation
Learning agents
Consider using learning agents that interact with their environment to acquire knowledge and adapt their behavior. Unlike other agents, learning ones continuously update their responses, allowing them to better cope with complex, dynamic, and uncertain situations.
Use cases:
- Customer service chatbots
- Financial fraud detection
- Dynamic pricing systems
- Individualized treatment planning
- Personalized recommendation systems
Retrieval-augmented generation (RAG) agents
Utilize retrieval-augmented generation agents that can obtain large amounts of data from external sources, such as databases, financial systems, or documents, and combine it with generative capabilities to produce accurate responses. RAG is preferred for complex flows and domain-specific scenarios.
Use cases:
- Data management
- Business workflow automation
- Monitoring equipment and production processes
- Answering patients’ questions
Multi-agent systems (MAS)
Create a multi-agent system that consists of many AI agents that work together within a shared environment to perform complex tasks on behalf of a user or another system. Each agent has a role and specific capabilities: one may plan, another may retrieve data, another may analyze, or another may act.
Use cases:
- Traffic and transportation solutions oversight
- Industrial automation
- Supply chain management
- Security system reinforcement
Hierarchical agents
Develop hierarchical agents that are structured into a multi-level system, where high-level agents coordinate the actions of mid-level and low-level ones. Such solutions break complex tasks into small, manageable subtasks to enable more organized control, decision-making, and scalable execution.
Use cases:
- Manufacturing control systems
- Building automation
- Smart factories
- Robotics
AI agents use cases across industries
Healthcare
We design AI agents to assist physicians in automating tedious, time-consuming operations, reducing staff workloads, and adapting treatment plans as clinical conditions change.
- Medical documentation assistance
- Healthcare data analysis
- Scheduling and resource management
- Automated patient notifications
Finance
We create custom AI agent solutions for financial institutions that streamline banking operations and asset management, improve the customer experience, and minimize business risks.
- Real-time transaction data analysis
- Credit and lending control
- Wealth and portfolio management
- Automated regulatory compliance
Telecom
SoftTeco builds virtual assistants to help telecommunications companies manage and improve network performance, provide timely customer support, and ensure service stability.
- Network incident analysis
- Traffic management
- Generating customer invoices and processing payments
- Recommendations for individual service plans
Manufacturing
We design manufacturing intelligent agents that help them automate product lifecycle management, enhance quality control, and optimize supply chains.
- Inventory management
- Demand prediction
- Enhanced supplier management
- Automated predictive maintenance
Logistics
Our AI assistants for logistics businesses automate many supply chain operations, monitor risks, and adjust inventory, production, and replenishment plans.
- Intelligent procurement
- Optimized warehousing
- Rerouting trucks when needed
- Assessing potential disruptions
Oil and gas
We deliver autonomous solutions to oil and gas companies that automate many processes, such as geological data analysis, drilling outcome prediction, or asset monitoring.
- Exploration and reservoir management
- Automated drilling operations
- Self-driven pipeline monitoring
- Optimized production schedules
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Reasons why you need AI agents
Improved operational efficiency
AI agents automate 52% of repetitive, complex operations, such as log analysis, process automation, and operations management.
Fast issue resolution
39% of companies say that AI agents help prevent issues by predicting potential problems, such as system failures or increased downtime, and taking corrective actions as fast as possible.
Wise decision-making
AI agents analyze massive amounts of data in real-time, providing business with insights from customer, operational and sales data, helping them improve decision-making by up to 65%.
Reduced operational costs
Workflow automation with agents can significantly reduce operating costs by 52% eliminating inefficiencies and errors by 69% in manual processes.
Excellent customer support
Unlike traditional teams, AI agents are able to provide round-the-clock customer support across different channels, improving customer satisfaction by 45%.
High level of scalability
AI agents can simultaneously monitor thousands of systems without compromising quality. During campaigns or peak loads, these systems deliver fast, accurate responses.
Improved security
Artificial intelligence agents can detect and respond to attacks in real time, significantly enhancing an organization’s security.
AI models we work with
LLMs (Proprietary / API-based)
LLMs (Open-source / Self-hosted)
NLP Models (Encoder-based / NER / embeddings)
Speech & Audio
Image Generation
Video Generation
Embedding Models
Our AI agent development tech stack
As experts in agentic AI, we leverage advanced technologies, such as machine learning, natural language processing, computer vision, and cognitive computing. We apply proven AI frameworks and tools during AI development.
Infrastructure
Dagster, OpenAI, Anthropic, OpenRouter, vLLM, Ollama, AWS, Microsoft Azure, Google Cloud Platform (GCP)
Intelligence
LangChain, LlamaIndex, Haystack, Pinecone, Chroma, Milvus, Qdrant, Claude
Engineering
PyTorch, Lightning, LangSmith, Sourcegraph, codeanywhere, Arize, Weights & Biases, braintrust, ContextQA, JigsawStack
Observability and governance
PydanticAI, LangServe, Traceloop, Guardrails AI, LLM Guard, WhyLabs
Agent consumer
Github Copilot, Cursor, Sourcegraph, Continue, Codemod, Lovable
Our AI agent projects
An AI-powered chatbot for customer support
SoftTeco created an AI-powered chatbot that provides round-the-clock customer support. During development, our team used a robust RAG system to train the bot and the GPT-4 model for information processing. The solution uses the company’s proprietary corporate data as its primary source. With it, the company was able to double the speed of customer service operations, increase response accuracy by 25%, and lead generation by 14%.
AI hotel concierge assistant
SoftTeco built an AI-powered virtual assistant for Cisco Webex Desk Pro devices, widely used in hotels, airports, and similar settings. We developed the backend using Agentic AI and a Vector database for RAG storage, communicating with the frontend via a pipeline using ReAct agents. By supporting voice-driven conversations, the assistant provides a more intuitive user experience. It also offers secure payment processing and QR scanning for automation of transactions.
Our awards
Our AI agent development process
Our AI agent development company follows rigorous and transparent processes to keep you informed during each stage of system building.
Step 1. Discovery and project planning
Based on your business needs and pain points, we help you define the purpose of the AI agent, its tasks, and functionality. Our specialists also analyze your architecture and data sources and prepare estimated budget and time estimates for your project.
Step 2. Data collection and preparation
We help you determine data types and sources, collect, clean, label, and preprocess the data to identify errors, fix missing values, and ensure data consistency. If you already have the information, our ML experts help you analyze it, determine the necessary entries, and organize it for further use.
Step 3. Model setup and training
Our specialists help you decide whether to train the model from scratch or use a pre-trained large language model. Regardless of the choice, we fine-tune the ML model on domain-specific data and adjust its parameters to improve performance and accuracy.
Step 4. Model development and integration
We design AI models tailored to your objectives, selected language model, and architecture. During this phase, our experts integrate an AI agent with the necessary systems and services, including CRMs, databases, financial, and enterprise tools.
Step 5. Testing and validation
Our QA specialists carry out multi-layered testing, including integration, end-to-end, regression, and security tests, along with human-in-the-loop evaluation. We ensure that an AI agent operates with a high level of security, performance, and accuracy.
Step 6. Deployment and maintenance
Our engineers deploy an AI agent to the production environment, and set up CI/CD to automate testing and delivery. We also implement system monitoring and conduct ongoing maintenance to fix errors, retrain models, add new features, or optimize system performance.
Challenges we help solve during AI agent development
Why choose SoftTeco?
Client testimonials about our AI agent development services
FAQ
What is the cost of building an AI agent?
How long does it take to develop an AI agent?
What AI agent development frameworks do you use to build robust systems?
How do you ensure the quality and performance of AI agents?
What is the difference between conversation AI and AI agents?
In contrast, AI agents are more sophisticated ML models that can operate as independent systems, capable of setting goals, planning, and executing tasks on their own. They are suitable for complex tasks across multiple contexts.
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