Your team can maintain what we build
We document models, write clear inference APIs, and train your team on how the system works. When we hand over the codebase, you own it, and your developers can extend it without calling us for every update.
Experts ready to start helping with your AI project
We cover the full build — from model selection and data engineering through to deployment and ongoing support. Every engagement starts with a genuine scoping conversation, not a predefined package.
Deep learning and machine learning models
We have experience in building neural networks, computer vision systems, natural language processing models (NLP), and recommender systems.
Data engineering
Data pipelines, labelling workflows, feature engineering, and the infrastructure that keeps models accurate over time — we handle all of it.
Every engagement starts with an honest scoping conversation. Before any model gets built, the use case, data readiness, and success criteria get defined, including whether a custom model is actually the right call or whether a simpler solution gets you there faster.
AI chatbots and virtual assistants
Multi-turn conversational systems with context memory, tool use, and escalation logic. Retrieval augmentation keeps answers accurate on domain-specific queries, so the system doesn’t hallucinate its way through your product catalogue or policy documents.
Object detection, image classification, OCR, facial recognition, and defect detection — built on OpenCV, YOLO, Detectron2, and custom-trained models. Put to work in manufacturing QC, retail inventory, medical imaging, and document digitisation.
Connecting a model to production is where most AI projects hit unexpected friction. REST APIs, message queues, cloud platforms (AWS, GCP, Azure), and existing SaaS tools — authentication, monitoring hooks, fallback logic, and performance benchmarking all handled.
Regulated environments (fintech, healthcare, insurance) need more than a working model. Audit trails, access controls, explainability features, and compliance documentation are built in from the start. SOC 2, HIPAA, and GDPR-aligned delivery is standard, not an add-on.
Natural language processing solution
Sentiment analysis, entity recognition, document classification, contract review, and multilingual text processing. We use spaCy, Hugging Face Transformers, and fine-tuned LLMs depending on the task complexity.
AI agents development
We build autonomous AI agents that plan and execute multi-step workflows (browsing, reasoning, calling APIs, writing outputs) with minimal human intervention. Used for research automation, document processing pipelines, customer support escalation, and internal operations.
We help companies across industries apply generative AI to automate workflows, generate code and documentation, enhance customer support, and launch new data-driven products.
We provide highly skilled AI engineers and data scientists to work as temporary or contract staff at your company.
We help companies using AI for natural language generation to craft high-quality prompts that produce coherent, consistent, and controllable responses.
Discovery
We spend the first phase understanding your data and creating AI strategy — what you have, what’s missing, how it’s structured, and whether it’s clean enough to build on. We define the use case, the success metric, and the acceptance criteria before writing a line of code. Most projects are delayed by skipping this step.
Data exploration
Your data sources get a full audit — gaps identified, labelling and preprocessing pipelines built for model training. If annotation is needed, we run that workflow. If augmentation is needed, we scope that separately. Either way, you get a data readiness report before model work begins.
Model development
Architecture selection is driven by your task (classification, regression, generation, retrieval, or a combination) then trained on your prepared data. Experiments run across multiple configurations, results tracked in MLflow or a comparable tool. You see the performance profile against your agreed success metrics before anything moves to integration.
Integration
The model gets wrapped in a production-ready API, connected to your target systems, and tested across latency, throughput, error handling, and fallback behaviour. Endpoints, expected inputs and outputs, and every edge case tested — all documented before handover.
Deployment
Target environment (AWS, GCP, Azure, or on-premises) gets CI/CD pipelines, logging, and alerting configured from day one. Model monitoring is set up so you can track inference quality and data drift over time, not just system uptime.
Support and maintenance
AI models degrade as data distributions shift. Scheduled model reviews, retraining pipelines, and performance reporting are all included. If accuracy drops below an agreed threshold, you hear about it — you don’t have to monitor it yourself.
Let’s create a system with computer vision to detect, recognize, classify, or filter objects, patterns, characters, and other data types for your business to bring more outcomes across different industries with our AI solutions.
Data collection
We can build the processes for data collection and analysis so you can own and benefit not only from the information but from the entire system solution, which brings you measurable results. You move from time-consuming, manually performed tasks to an automated, more cost-efficient, and flawless model for working with data.
Product development
Data segmentation, construction of basic prediction models, real-time automated processes, additional data quality control, and algorithms reduce the time to obtain insights from analytics and received data.
You can make data-based decisions about development and scaling. Our AI development services will supply your software with:
Data collection and annotation
We can automate the collection processes using AI services, selecting the most efficient out of the available options, determining the scalable legal format, and analyzing models to work with.
Development of solutions based on language models and knowledge graphs
Our experts will enhance it with named entity recognition (a key feature in AI algorithms that identifies names, dates, addresses, etc.), sentiment recognition (to find out whether it’s positive feedback or not), and chatbots (to support the image of a responsive business).
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
AI-driven-communication-assistant
Our client is a technology company specializing in providing services for industries with highly regulated communications. Their AI-based expertise allows to make those processes more simple. Geniusee was entrusted with migrating client’s infrastructure from Azure to Amazon, aiming to consolidate their crucial infrastructure in one place. The project involved migrating their database and leveraging Amazon services to optimize the system's scalability, security, and performance.
Case details
Languages
ML & AI frameworks
Orchestration
Foundation models
Vector databases
Cloud AI services
Genuisee’s versatile experience, gained over more than 8 years, has enabled us to form a team with a proven track record.
24/7 support
As your AI software development company, we build a dedicated project team to support you at every stage – from discovery and development to launch and ongoing post-deployment support.
Metrics-driven company
Our artificial intelligence services are designed to create meaningful and measurable outcomes for your project, so as artificial intelligence is all about math, we believe only in numbers.
Experienced AI professionals
Exquisite solutions can only be implemented by strong professionals passionate about their jobs. Our AI development services are already bringing marvelous results to global companies.
Agile approach
We create a dedicated cross-functional team for your AI development and take responsibility for matching our productivity with your timeline expectations.
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.
How much does it cost to hire an AI developer?
The price of the entire AI development service depends on the number of working hours required. According to Clutch’s survey, the median cost for an app is around $170K, with a range starting at $30K to $700K. So, it depends on the vendor you work with, the expertise of AI developers, the complexity of the product you want to have at the end, and the estimated time limitations you set. You can also count the approximate cost using our estimator.
How long does it take to implement AI-driven development solutions?
Artificial intelligence software development services do not take as long as you think. Depending on the scope of work, two or more weeks for POC and three to six or more months for the integration. Ask us to evaluate how long it will take to implement your AI solution into life!
Do you offer post-development support?
As an artificial intelligence development company, Geniusee offers 24/7 post-deployment support and maintenance not only for AI projects but also for all-out custom software solutions.
Your AI developers should offer you the best scenarios of constant improvement and a mind map for scaling your product in the future. So, to make it happen, you should take into account this perspective and check the actual version of your application on bugs, adjusting it with new functions and adapting it to new realities in the technology world, such as new screen sizes, etc.
How do AI models work?
Artificial intelligence models are computer programs that can learn, adapt, and make predictions without being explicitly programmed. They are exposed to large amounts of data, which AI algorithms use to detect patterns and make intelligent decisions when exposed to new data. Our AI developers train machine learning algorithms with massive amounts of data so they can learn on their own.
Do you offer consulting services?
Yes, we offer AI consulting services to help organizations get started with artificial intelligence. Our consultants can assess your data and business needs to determine good opportunities for applying AI. We provide advice on managing data, selecting appropriate AI and machine learning techniques, and planning AI projects.
Who owns the models and code after delivery?
You do. Full IP transfer is standard in our delivery contracts. We don’t retain rights to models, training pipelines, or application code. We’ll walk you through the handover checklist before the final invoice.
Do you build on foundation models or train custom models from scratch?
Both, the right answer depends on your data, budget, and what you’re actually trying to do. For most enterprise use cases, fine-tuning a foundation model (GPT-4o, Claude, LLaMA) on your data is faster and cheaper than building from scratch. At scoping, you’ll get a clear recommendation either way, with the trade-offs laid out plainly.
How do you handle data privacy and IP during development?
We sign NDAs before any data is shared. For sensitive industries (healthcare, fintech, insurance) we can work in your own cloud environment, avoiding any data transfer to third-party infrastructure. We document data handling procedures and can provide compliance evidence for your security review.
