Fully allocated AI team
Long-term product involvement
Continuous agent improvement
AI agent development company
Integrate AI agents into your internal systems to automate repetitive tasks, even within complex workflows, using your business data in a secure data-protection environment and in accordance with your operational rules. Work that previously grew in proportion to headcount can be shifted to agent-supported processes, helping your business handle larger workloads faster without expanding every routine role.
Benefits
Manual workflows growing? Missing resources for additional staff? Scale routine operations with custom AI agent development company
In routine workflows, companies often rely on employees to verify data, update records, and approve next steps. But as AI changes how work gets done, companies do not always need to rebuild teams or hire new AI-ready employees. With AI agent development services from KPS, you can configure agents around existing processes, move time-consuming tasks to agent-supported workflows, and process more work without expanding every routine role.
Process speed
Reduces delays in repeated workflow steps
Moves requests forward without constant follow-ups
Shortens response time for routine operations
Information access
Turns scattered knowledge into a usable working context
Helps teams find answers without searching several tools
Reduces dependency on individual employees for basic information
Decision consistency
Applies defined rules to repeated cases
Keeps routine decisions aligned across teams
Escalates exceptions instead of treating every case manually
System continuity
Adds automation without replacing core platforms
Keeps current tools involved in the workflow
Reduces disruption for teams already using existing systems
Action visibility
Shows what the agent did and when
Keeps logs for review and accountability
Makes sensitive actions easier to monitor
Team capacity without replacement
Takes over routine task steps
Keeps people in key decisions
Frees time for higher-value work
Considering developing an AI agent for your business?
KPS helps you choose where to start, plan how agents will connect to your systems, and estimate the scope of your first project.
Our Services
AI agent development services KPS provides to automate routine decisions and workflow steps
Our engineers cover the full project lifecycle, but the scope starts with understanding where an AI agent is actually needed. KPS reviews the workflow, data, systems, and expected value before development, so the agent supports a real business process instead of becoming another tool to maintain.
01
AI agent discovery and use case definition
Solution architects and business analysts review your operations to identify where AI agents create real value. This stage covers workflow mapping, data and tool inventory, and scope definition before any development begins.
02
Custom AI agent development
AI engineers design and build individual agents for specific business tasks, with defined tools, business logic, and clear behavior boundaries. Each agent is built around the workflow it serves rather than a generic template.
03
Multi-agent system development
For processes that involve several steps or cross-team coordination, our engineers design systems in which specialized agents share work and exchange context under defined rules. Suitable for cases where a single agent would become a bottleneck.
04
AI agent integration with internal systems
Backend and integration engineers connect agents to CRMs, ERPs, databases, internal APIs, and other business tools. The focus is on stable data exchange, secure access, and consistent behavior across connected systems.
05
Workflow automation with AI agents
Process specialists and AI engineers replace manual, multi-step workflows with agent-driven automation. This includes document handling, ticket routing, data movement between tools, and routine operational decisions.
06
Conversational AI agents and internal copilots
Our engineers build agents that work through natural-language interfaces: internal copilots that support employees in daily tasks and customer-facing assistants that handle requests, retrieve information, and trigger follow-up actions.
07
AI agent maintenance and ongoing support
After deployment, our teams handle prompt adjustments, tool updates, performance monitoring, and behavior tuning. This keeps agents aligned with changing data sources, business rules, and operational priorities.
08
Dedicated AI engineers
AI specialists and ML engineers who integrate into your team, follow your processes, and contribute to long-term agent development and support. It is useful when internal AI capacity needs to grow without adding fixed headcount.
Technology Stack
Technology stack supported by our AI agent development teams
Our technology choices for AI agent projects depend on the type of work each agent handles, the systems it connects to, and the level of control required for sensitive operations. KPS forms the stack around industries where AI agents are already used in daily processes, including finance, healthcare, retail, logistics, manufacturing, and enterprise operations. This means we account for security, access control, response speed, system availability, integration stability, and monitoring from the architecture stage.
AI models and LLM providers
Large language models: GPT, Claude, Gemini, Llama
AI platforms: OpenAI API, Anthropic API, Google Vertex AI, Azure OpenAI, Amazon Bedrock
Model deployment options: cloud-based APIs, private model deployments, open-source model hostingAgent frameworks and orchestration
Agent frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI
Workflow orchestration: Temporal, Apache Airflow, Prefect
Agent logic: tool calling, function calling, structured outputs, task routing, memory managementKnowledge retrieval and RAG
Vector databases: Pinecone, Weaviate, Qdrant, Milvus, Chroma
Search and indexing: Elasticsearch, OpenSearch
RAG components: document parsing, chunking, embedding generation, metadata filtering, retrieval pipelinesBackend development
Languages and runtimes: Python, Node.js, Java, .NET, Go
Frameworks: FastAPI, Django, NestJS, Express.js, Spring Boot, ASP.NET Core
API and communication: REST, GraphQL, WebSockets, RabbitMQ, Kafka, AWS SQSData and storage
Relational databases: PostgreSQL, MySQL, Microsoft SQL Server
NoSQL and in-memory stores: MongoDB, Redis, DynamoDB
Data practices: data modeling, access control, caching, synchronization, audit trailsSystem integrations
Business systems: CRM systems, ERP systems, helpdesk platforms, HR systems, analytics tools
Communication tools: Slack, Microsoft Teams, Google Chat, email services
Integration methods: APIs, webhooks, middleware, event-driven integrationsCloud infrastructure and DevOps
Cloud platforms: AWS, Google Cloud Platform, Microsoft Azure
Containerization and orchestration: Docker, Kubernetes, Helm
CI/CD and automation: GitHub Actions, GitLab CI, Jenkins, Azure DevOpsSecurity and governance
Access control: OAuth 2.0, OpenID Connect, role-based access control
Data protection: encryption at rest, encryption in transit, secure credential management
AI-specific controls: permission boundaries, approval rules, prompt injection checks, sensitive data filteringTesting and AI evaluation
Backend testing tools: PyTest, Jest, JUnit, NUnit
Agent testing: scenario testing, tool-use validation, response checks, regression testing
Quality practices: test datasets, edge case validation, human review workflows, automated evaluationMonitoring and observability
Monitoring and logging: Prometheus, Grafana, ELK Stack, Datadog
AI observability: prompt logs, tool-call traces, response quality checks, token usage tracking
Error tracking and diagnostics: Sentry, New Relic
Related Services
Supporting services for scaling AI agents across business operations
Data engineering and data preparation
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Software architecture assessment
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Backend development services
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Cloud and DevOps support
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Security and compliance support
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QA and testing services
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Post-launch support and optimization
Engagement Models
Ways to work with KPS on AI agent development
AI agent projects differ in scope, pace, and the extent to which you want to keep the work internal. Our three engagement models match these differences while keeping delivery predictable and responsibilities clear.
Dedicated AI agent team
AI team extension
Engineers join your team
Specific AI skill coverage
Work under your leadership
Managed AI agent development
Defined scope and timeline
KPS manages delivery
Suitable for clear use cases
Our Process
How KPS structures AI agent development from first scope to release
AI agent development requires clear decisions and visions before implementation starts. Our process follows a clear roadmap from discovery to production, with your team involved at every stage.
STEP 01:
Workflow and goal alignmen
Business stakeholders, product owners, employees who depend on the workflow, and KPS solution architects review the current process, existing problems, and points where human involvement is required. This step clarifies which part of the workflow the AI agent should support, what result it should deliver, and what limits must be considered before development starts.
STEP 02:
Agent role and scope definition
KPS business analysts and AI specialists define what the agent should handle, what data it can use, what actions it can perform, and when the process should involve a person. This creates a clear scope for development and prevents the agent from covering too many unrelated tasks.
STEP 03:
Architecture and integration planning
Our solution architects and backend engineers design the AI agent's technical structure. This includes model selection, data sources, integration points, API logic, access rules, and the systems the agent needs to work with.
STEP 04:
Agent development and configuration
AI engineers and backend developers build the agent, configure prompts, connect tools, set workflow logic, and integrate required systems. Development is done in stages, so you can review how the agent works before the full release.
STEP 05:
Testing and business validation
Together with QA engineers and AI specialists, you test the agent against real scenarios, edge cases, incorrect inputs, and integration failures. A small beta group from your company also tests the agent in the real working environment and shares feedback. This step checks whether the agent follows the agreed process, uses the right data, and produces outputs that match your business expectations.
STEP 06:
Deployment and ongoing improvement
DevOps engineers and support specialists prepare the release, configure monitoring, and support the agent after launch. Our team reviews logs, user feedback, workflow changes, and performance issues to adjust the agent if your business rules or systems change.
STEP 01:
Workflow and goal alignmen
Business stakeholders, product owners, employees who depend on the workflow, and KPS solution architects review the current process, existing problems, and points where human involvement is required. This step clarifies which part of the workflow the AI agent should support, what result it should deliver, and what limits must be considered before development starts.
STEP 02:
Agent role and scope definition
KPS business analysts and AI specialists define what the agent should handle, what data it can use, what actions it can perform, and when the process should involve a person. This creates a clear scope for development and prevents the agent from covering too many unrelated tasks.
STEP 03:
Architecture and integration planning
Our solution architects and backend engineers design the AI agent's technical structure. This includes model selection, data sources, integration points, API logic, access rules, and the systems the agent needs to work with.
STEP 04:
Agent development and configuration
AI engineers and backend developers build the agent, configure prompts, connect tools, set workflow logic, and integrate required systems. Development is done in stages, so you can review how the agent works before the full release.
STEP 05:
Testing and business validation
Together with QA engineers and AI specialists, you test the agent against real scenarios, edge cases, incorrect inputs, and integration failures. A small beta group from your company also tests the agent in the real working environment and shares feedback. This step checks whether the agent follows the agreed process, uses the right data, and produces outputs that match your business expectations.
STEP 06:
Deployment and ongoing improvement
DevOps engineers and support specialists prepare the release, configure monitoring, and support the agent after launch. Our team reviews logs, user feedback, workflow changes, and performance issues to adjust the agent if your business rules or systems change.
Clients' feedback
What clients say about technical delivery and collaboration with KPS
The comments below reflect different parts of cooperation with KPS: communication, delivery process, technical quality, and the ability to support your business needs over time.
Naomi Rubinstein
Founder at BettercareThey are the best team we have ever worked with. The application increased the speed of receiving data by 4 times. Data loss was reduced by 10%. Ineffective tasks decreased by 7%. Response rate to customer requests increased by 23%. Our customers have seen significant increases in efficiency.
Aleksandr Podolyan
Technical Specialist & Product Manager., RDO UkraineKultprosvet has executed deliverables perfectly and provided us with a high-quality application. They’ve fulfilled our requirements, and the product perfectly fits our needs. The team’s development efforts have helped our business immensely.
Oleksandr Zainchukivskyi
Head of Technology, AMACOWe've had a very good experience with them. We trust them, and we'll continue to work with them. If we ever need something done, they always deliver.
Luc Lecorre
Luc Lecorre, Co-Investor, Luxury Handbag CompanyKultprosvet was highly knowledgeable, and they made us aware of some issues we hadn’t considered. They explained everything very clearly and helped us understand the broader scope of the work.
Yulia Goldenberg
PhD Researcher, Ben Gurion University of the NegevThe work is always delivered on time, and they are very fair about the pricing. Kultprosvet is transparent, and we know that we can trust them; we are never surprised by anything that comes up.
Cameron Tope
Founder, Rooya (Polysurance)OUR TEAM
Want to know who you’ll work with?
Contact our client support team, which will provide you with a technical evaluation of your needs and give you details on the collaboration resources you will need.
Anton Trakht
CEO at Kultprosvet
Mykola Aleksandrov
Account Executive
We are ready to review your requirements and propose a practical next step.
Book a call
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Since working with Kultprosvet, our customers are much happier with the product and its UX. They’ve added flexibility where the system was previously rigid, and they take full responsibility for the project, quickly fixing any issues that arise.