AI agent development
Custom AI Agent Development Services
AI agents that make it to production.
Move beyond demos with custom AI agents designed for real business workflows. We build support agents, internal copilots and workflow automation, with integrations, evaluation and human handoffs.
AI agent development solutions we build
Customer-support agents
Answer, triage and resolve across chat, email and WhatsApp, with human handoff.
Internal copilots
Assistants over your documents, CRM and databases, so your team stops digging.
Workflow automations
Multi-step processes (quotes, onboarding, reporting) run by agents with approvals where they matter.
Agent integrations
Add AI to the software you already use via APIs — no rip-and-replace.
Practical applications
AI agent use cases by business workflow
Start with a workflow that has clear inputs, measurable outcomes and an escalation path. These are examples of potential solutions, not claims about completed client projects.
Customer service and support
Retrieve approved answers, classify requests and prepare responses across support channels. Escalate sensitive or uncertain cases to an agent with conversation context.
Sales and CRM operations
Summarize enquiries, enrich lead records and draft follow-ups using permitted CRM data. Require human approval before customer-facing or high-impact actions.
Document-heavy operations
Extract information from documents, compare it against business rules and flag exceptions for review. Integrate outputs with existing internal systems.
Internal knowledge and IT workflows
Help employees find relevant policies, troubleshoot common requests and create structured tickets with citations to the underlying knowledge source.
Need an agent connected to existing software? Explore our Explore our backend development and API integration services.
Our AI products and production experience
Explore software developed by our studio. Products may use different AI capabilities; not every product is an autonomous agent. ChatCactus is a practical starting point for evaluating our AI support experience.
Explore ChatCactus: AI support in action
Try the public-facing experience and assess response quality, interaction flow and fit for your support needs. Detailed architecture, integration scope and performance metrics can be discussed using approved project evidence.
How we build
Discovery and scoping
We map the workflow, the data and the risks; you get a scoped pilot proposal.
Pilot and evaluation
We prototype a bounded workflow and test it against agreed success criteria. The schedule depends on data access, integration complexity and review requirements.
Production
We plan permissions, security review, monitoring, fallbacks and human handoffs before deployment.
Operate & improve
We can review quality, operating costs and failures, then plan improvements according to the agreed support scope.
Planning your investment
How much does AI agent development cost?
There is no reliable one-size-fits-all price. The budget depends on what the agent must do, which systems it can access, how its results are checked and how it will be operated after launch.
Discovery and feasibility
Map workflows, available data, integrations, constraints, security needs and acceptance criteria before estimating delivery.
Focused pilot
Limit the first release to a defined workflow, a small set of tools and a measurable evaluation plan.
Production deployment
Account for access controls, integration testing, observability, failure handling, human approvals and rollout.
Ongoing operation
Plan for model/API usage, infrastructure, monitoring, maintenance and future workflow changes.
Share your workflow, systems and desired outcome to receive a scoped estimate rather than an unsupported fixed-price promise.
Request an AI project estimate (opens in a new tab)Production readiness
Security, evaluation and human oversight
Reliable AI agents need more than a strong model. During solution design, we consider the controls needed for each workflow and confirm them during project scoping.
Data access and tool permissions
Define what the agent can read and change, which credentials it uses and how sensitive data is protected.
Evaluation and regression testing
Use representative tasks and failure cases to check answer quality, tool calls and workflow completion before release.
Human approval and escalation
Identify actions that require a person to approve, review or take over, especially when decisions are consequential.
Monitoring and recovery
Plan logging, alerts, fallbacks and a way to investigate or reverse incorrect actions where supported by the underlying systems.
These are engineering considerations, not a claim that every safeguard is included in every project. The agreed architecture and scope determine implementation.
Explore our broader AI automation and data science services.
AI agent technology stack and integrations
Depending on requirements, an agent architecture may combine OpenAI, Anthropic (Claude) or open-weight models with retrieval-augmented generation (RAG), vector search, tool calling, API integrations and evaluation tooling. Deployment options and model choices are selected during technical discovery.