Your processes change too often for rigid automation
Classic automation works well when every step is predictable, but many business workflows shift with customer context, new rules, market changes, or product updates. AI agents evolve more effectively in these environments because they can work with instructions, data, tools, and feedback loops rather than relying solely on fixed scripts.
IT experts are ready to start building AI agents for you
How agentic AI systems change how your business works
“AI agents are often misunderstood as technology that replaces people at work, but their real value is different: a well-designed AI system helps existing specialists work faster, handle more context, and spend less time on repetitive tasks.
When companies develop AI agents for real business operations, they can grow without hiring more people to handle routine requests, review documents, search for information, or manage data across multiple tools.
Your team stays in control of high-value decisions, while smart digital helpers complete certain tasks in seconds or minutes, including workflows that used to be too fragmented for classic automation.”
Taras Tymoshchuk
CEO, Founder
Start with the work your team repeats most: manual checks, research, system switching, document review, QA routines, or migration tasks. AI agent development services help turn those slow points into guided, automated workflows while your specialists keep control over approvals, exceptions, and final decisions.
You can start with agentic system PoCs and MVPs, then gradually deploy business-ready systems. We take care of all the technical layers between the agentic AI idea and release, from the initial concept to the infrastructure needed to keep AI agents working with your real business logic, data sources, software systems, and approval rules.
K’CIDADE Academy: How we built an AI-powered learning platform for AKF Portugal
K'CIDADE Academy is AKF Portugal's professional learning platform for civil society organizations, educators, and community leaders. Built on adaptive learning pathways, an AI virtual tutor, and a gamified progress system, it delivers personalized training to professionals working across social inclusion, education, and community development.
Case details
Scaling Edge AI & computer vision: QuantumEye real-time surveillance platform
Geniusee helped the QuantumEye AI platform that upgrades legacy CCTV with real-time detection, instant incident notifications, and decision-ready analytics.
Case details
Anatomy Financial: Automated financial management software for healthcare
Anatomy introduces an innovative solution designed to automate document processing, compare various documents and banking transactions, and handle the intricacies of physical check processing. By conducting a thorough analysis, the system identifies discrepancies between the declared prices of medical procedures and the actual amounts received by the clinic.
Case details
MartechCompose AI: Advanced content writing solution
We partnered with Compose AI to enhance their AI-powered writing platform through cloud migration, UX improvements, seamless integrations, and scalable feature development, upporting their growth from YC-backed startup to a widely adopted productivity tool.
Case details
MartechForethought: Enterprise AI agents for customer support
Forethought partnered with Geniusee to extend and integrate its AI-powered support platforms (Assist and Discover) into complex enterprise environments. We helped enhance real-time agent assistance, streamline multi-system connectivity, and deliver a unified support experience across channels. The result is a scalable AI agent ecosystem that accelerates resolution times, boosts team productivity, and powers more consistent customer experiences.
Case details
Enhancing FactMata’s AI to detect misleading content at scale
We partnered with Factmata to scale their AI platform for analyzing online content at volume, enhance data pipelines, and modernize their NLP models. Our team strengthened system performance, improved processing efficiency, and enabled Factmata to handle millions of records with higher accuracy and lower operational cost. By implementing our expertise in machine learning, natural language processing, and data analysis, we worked closely with FactMata to create web scrapers and optimize data pipelines and the project’s overall performance. The client addressed Geniusee to modernize their platform by combining real-time IoT data, AI analytics, and an intuitive user experience. The result: a scalable, secure, and analytics-driven ecosystem that helps enterprises make informed workspace decisions.
Case details
- Transaction support agents that help users check payment status, fees, failed transfers, and account details
- Compliance agents that scan documents, flag missing fields, and prepare cases for review
- Fraud triage agents that collect transaction context and route suspicious activity to analysts
- Onboarding agents that guide users through KYC steps, document uploads, and account setup
- Finance copilots that summarize reports and answer questions from approved financial records
- Support agents for banking, lending, trading, insurance, and payment platforms
- Learning assistants that answer student questions and explain course materials
- Tutor copilots that help prepare lesson notes, quizzes, feedback, and progress summaries
- LMS agents that guide learners through enrollment, assignments, deadlines, and certificates
- Admin agents that handle course access, account questions, and routine support requests
- Content review agents that check learning materials for clarity, structure, and duplication
- Analytics agents that summarize learner progress, engagement, and drop-off points
- Shopping assistants that help customers compare products and get personalized shopping hints
- Support agents that answer order, return, delivery, warranty, and payment questions
- Inventory agents that monitor stock signals and prepare replenishment suggestions
- Product data agents that update descriptions, categories, attributes, and catalog records
- Marketing agents that summarize customer behavior and prepare audience segments for review
- Back-office agents that connect e-commerce platforms, CRMs, ERPs, and support tools
- Property search agents that match buyers or tenants with listings by budget, location, and intent
- Lead qualification agents that collect requirements and route serious inquiries to sales teams
- Document agents that check leases, permits, inspection notes, and missing property details
- Investor support agents that summarize project data, timelines, and property documentation
- CRM agents that update lead records, schedule follow-ups, and prepare context before calls
- Listing agents that create, enrich, and maintain property content across websites and portals
Every AI agent depends on the workflow, data, software environment, and risk level that underpin it, so our process stays flexible rather than fixed to a single delivery template. Following the best standards of AI agent development services, Geniusee can start with a focused PoC, move into an MVP, and then scale the agent into a stable business system with the right integrations, monitoring, and infrastructure in place.
Intake
We begin with AI consulting to understand the business goal, users, data sources, existing tools, approval rules, and the work your team wants to automate. This stage helps us define what the agent should do, what it should avoid, and where human review is required.
Scope
We choose the smallest useful scenario that can prove value without turning the first release into a heavy AI transformation project. This may be a support assistant, QA agent, document review agent, internal knowledge agent, or workflow automation agent connected to a limited set of systems.
Build
Our engineers design the agent logic, select the right AI model, connect data sources, and create the first working version. Depending on the case, we add retrieval-augmented generation (RAG), API integrations, tool use, memory rules, role-based permissions, fallback behavior, and prompt logic.
Validate
We test the agent against realistic business cases, not polished demo prompts. The validation covers answer quality, tool-call accuracy, hallucination risks, edge cases, latency, cost, security limits, and the actual value for users or internal teams.
Scale
After validation, we prepare the agent for production with monitoring, LLMOps, cloud infrastructure, access control, and continuous improvement. This is where generative AI becomes part of your product, operations, or customer-facing service with clear ownership, measurable performance, and safe human oversight.
Certified AWS Partner delivering secure, scalable cloud-native solutions.
ISO-compliant processes ensuring quality, security, and reliability.
Trusted integration partner for financial data connectivity and open banking.
Team of ISTQB-certified QA engineers for world-class software testing.
Consistently rated ★5.0 by clients for reliability and delivery excellence.
Accredited partnership supporting advanced testing and continuous QA automation.
What does an AI agent company actually build?
An AI agent company builds software that can understand tasks, use connected tools, retrieve business data, and complete defined actions with controlled autonomy. For Geniusee, this can include support agents, internal copilots, document review agents, QA agents, code migration assistants, and workflow automation systems connected to your existing software.
Can Geniusee build AI agents from scratch?
Yes. Geniusee can build AI agents from scratch, starting with discovery, architecture, model selection, data access, prompt logic, integrations, testing, and deployment. This works best when the agent needs custom business rules, secure system access, approval flows, or behavior that cannot be handled by a ready-made chatbot or automation tool.
What are multi-agent solutions?
Multi-agent systems are systems in which several AI agents collaborate on different parts of a larger task. For example, one agent can collect data, another can analyze it, a third can check rules, and a fourth can prepare the final response or action for human approval. This approach combines automation and AI to handle workflows that are too complex for a single chatbot or a fixed script.
How long does AI agent development usually take?
A fully functional Proof of Concept (PoC) typically can be delivered in 2–4 weeks, while an MVP may take 6–10 weeks, depending on integrations, data quality, and approval logic. A production-grade agent with monitoring, security controls, and several connected systems may take 3–6 months or more.
How much do AI agent development services cost?
The cost depends on scope, data complexity, number of integrations, cloud setup, testing depth, and whether you need one agent or a multi-agent system. After initial AI consulting services, Geniusee can estimate the team, timeline, architecture, and delivery budget more accurately, rather than providing a generic package price. Contact us to discuss your project and get a rough estimate of costs, or use our Estimator.
When should a company deploy custom AI agents?
Companies usually deploy custom AI agents when manual work starts slowing down growth: support queues get longer, specialists spend hours on research, documents need repeated checks, or teams keep moving data between tools. A good agent handles repeatable steps while people stay responsible for approvals, exceptions, and final decisions.
How does Geniusee ensure AI agents work safely?
Geniusee can ensure AI agents operate within clear limits through role-based access controls, human-in-the-loop approvals, audit logs, prompt testing, fallback logic, and quality checks. For sensitive tasks, the agent can prepare recommendations or actions, while your team approves anything that affects users, money, compliance, or critical business records.
