Artificial intelligence is no longer a feature reserved for large enterprises. In 2026, it is a practical tool that small and medium businesses can deploy to automate repetitive work, improve decision-making, extract value from data, and build products their competitors have not shipped yet.
The challenge is not whether AI can help your business. It almost certainly can. The challenge is knowing which problem to solve first, which technology to use, and how to integrate it cleanly into the systems you already rely on.
That is what Lycore does. We are not an AI research firm. We are a software development company with 20 years of engineering experience and a team that has been building with AI tools since they became production-ready. We combine that engineering depth with honest advice about what is feasible, what the timeline looks like, and what the ROI should be.
What We Build
AI integration for existing applications
The fastest path to value is usually adding AI capabilities to software you already have. We integrate large language models (LLMs) like GPT-4, Claude, and Gemini into your existing Django, React, or .NET applications via API. Use cases include intelligent search, document summarisation, automated report generation, customer-facing chatbots, and content classification.
AI agents and automated workflows
AI agents go beyond simple API calls — they plan, reason, and take sequential actions to complete multi-step tasks without human intervention. We build agents that can process incoming emails and route them automatically, analyse documents and extract structured data, monitor systems and trigger actions based on what they find, and coordinate workflows across multiple tools and APIs.
Custom AI-powered applications
When your requirements go beyond what existing platforms offer, we build from scratch. We have delivered custom AI applications including automated trading systems with ML-based signal detection, intelligent compliance management tools, AI-assisted scheduling and resource allocation systems, and data pipelines that turn raw operational data into actionable insight.
LLM fine-tuning and prompt engineering
Getting reliable, consistent output from a large language model requires more than a basic API call. We design robust prompt systems, implement retrieval-augmented generation (RAG) for grounding AI responses in your own data, and fine-tune models where the use case demands it.
AI-powered data analysis and reporting
Your business data is an untapped asset. We build pipelines that connect your data sources, apply machine learning models to identify patterns and anomalies, and surface findings through dashboards and automated reports. From predicting customer churn to detecting fraud signals in transaction data, we turn raw numbers into decisions.
Our Technology Stack
We build AI solutions using the tools and frameworks that have proven reliable in production environments:
AI & LLM frameworks: OpenAI API (GPT-4o, GPT-4), Anthropic API (Claude), Google Gemini, LangChain, LlamaIndex, Hugging Face
ML & data science: Python, scikit-learn, TensorFlow, PyTorch, Pandas, NumPy
Backend: Python, Django, Django REST Framework, FastAPI, Node.js, ASP.NET
Cloud & infrastructure: AWS (Lambda, SageMaker, Bedrock), Microsoft Azure (Azure AI, Azure OpenAI Service), Elastic Beanstalk, Docker
Databases & vector stores: PostgreSQL, MySQL, Pinecone, ChromaDB, pgvector
Frontend: React, Vue.js, Flutter (for mobile AI interfaces)
Industries We Have Delivered AI Solutions For
Fintech & trading — Automated trading signal detection, risk analysis pipelines, transaction anomaly detection, financial data extraction from documents
Healthcare & compliance — Intelligent document processing for care records, automated compliance checks, incident classification and routing, patient scheduling optimisation
E-commerce & retail — AI-powered product recommendations, automated customer support triage, demand forecasting, intelligent inventory management
Education & e-learning — Personalised learning path generation, automated content tagging, intelligent tutoring system components, assessment analysis
SaaS platforms — AI features as product differentiators, LLM-powered search, automated onboarding flows, usage analytics and churn prediction
Why Businesses Choose Lycore for AI Development
We build for production, not for demos. It is straightforward to build an AI prototype that works in a controlled environment. It is harder to build something that handles edge cases, scales reliably, stays within cost budgets, and integrates cleanly with existing data and authentication systems. Our engineers have been through that process enough times to know where the traps are.
We are honest about what AI can and cannot do. We will not oversell the technology. If your problem is better solved by a simpler algorithm or a well-designed database query, we will tell you. If AI is the right tool, we will tell you that too — and explain why, what it will cost, and how long it will take.
We work in your existing stack. Most AI development firms want to rebuild everything in their preferred tools. We work with what you already have — whether that is a Django backend, a .NET application, an AWS environment, or an Azure infrastructure — and add AI capabilities without forcing a full rebuild.
We stay involved after launch. AI systems require monitoring, prompt refinement, and periodic retraining as your data evolves. We offer ongoing support arrangements so your AI investment stays productive over time.
How We Approach an AI Project
1. Discovery — We start by understanding the business problem, not the technology. What decision or task are you trying to improve? What data do you have? What does success look like in measurable terms?
2. Feasibility assessment — We evaluate whether AI is the right approach, what quality of output is achievable, what the integration complexity looks like, and what the realistic cost and timeline are.
3. Proof of concept — Before committing to a full build, we typically deliver a working prototype in 2–4 weeks that demonstrates the core AI behaviour in your actual environment. This de-risks the investment significantly.
4. Build and integrate — Full development using our agile process, with regular demos and feedback loops. We integrate the AI components into your existing systems, test thoroughly, and document everything.
5. Deploy and monitor — We handle deployment to your cloud environment and set up monitoring for AI output quality, latency, and cost. AI systems can drift over time — we make sure you know when that happens.
Frequently Asked Questions
How much does an AI development project cost? Scope varies significantly. A straightforward AI integration into an existing application — for example, adding GPT-powered document summarisation — typically starts from $5,000. A custom AI agent or data pipeline is usually $15,000–$50,000 depending on complexity. We provide a detailed estimate after a free discovery call.
How long does an AI project take? A proof of concept is typically 2–4 weeks. A production-ready integration is usually 6–12 weeks. A full custom AI application is 3–6 months. We scope this precisely after understanding your requirements.
Do I need a large amount of data to use AI? Not necessarily. Many AI applications — particularly those built on LLMs like GPT or Claude — work well with minimal proprietary data by leveraging the model’s pre-trained knowledge. Projects involving custom ML models for prediction or classification typically require more data, and we assess this during discovery.
Can you integrate AI into our existing software without rebuilding it? Yes. This is most of what we do. We connect AI capabilities to your existing systems via APIs and avoid unnecessary rebuilds.
Do you work with OpenAI, Anthropic, and other AI providers? Yes. We work with all major LLM providers and select the right one based on your requirements, cost profile, and data privacy needs. We also have hands-on experience with open-source models for scenarios where data cannot leave your infrastructure.
