AI Product Development

AI Product Development

Building an AI product is not about plugging in an API and calling it a day. The real challenge is figuring out where AI actually adds value in your product, designing an experience that feels natural to users, and building infrastructure that does not fall apart when real traffic hits it.

That is what we do at AlgorizeTech. We build AI-powered applications that solve real problems — not demos that look impressive in a pitch deck but break in production.

🔨 What We Build

• AI Chatbots and Virtual Assistants:
  Not the kind that frustrate your customers. We build conversational AI that actually understands context, handles edge cases gracefully, and knows when to hand off to a human. Powered by GPT-4, Claude, or custom fine-tuned models depending on your needs.

• Recommendation Engines:
  Whether you are running an e-commerce store, a content platform, or a B2B marketplace, we build recommendation systems that learn from user behavior and surface the right content at the right time. More engagement, higher conversion, less noise.

• Document Processing:
  Invoice parsing, contract analysis, medical record extraction, resume screening. We combine OCR, NLP, and structured data extraction to turn messy documents into clean, actionable data. If your team is manually copying information from PDFs into spreadsheets, we can fix that.

• Computer Vision:
  Quality inspection, object detection, image classification, visual search. We build computer vision systems for manufacturing, retail, healthcare, and logistics. Real-time processing when you need it, batch processing when that makes more sense.

• Custom LLM Integrations:
  RAG pipelines, knowledge bases, AI-powered search, content generation tools. We integrate large language models into your existing products in ways that actually make sense for your users and your data.

⚙️ How We Work

We start with a two-week AI feasibility sprint. This is not a sales exercise — it is a genuine technical assessment of whether AI is the right solution for your problem, which approach will work best, and what kind of accuracy and performance you can realistically expect.

If the answer is "AI is not the right tool here," we will tell you that. Honestly. We would rather lose a project than build something that does not work.

If AI is the right fit, we move into a build phase that typically runs 8 to 16 weeks depending on complexity. We deliver working software every two weeks so you can test with real users and real data early.

💰 Pricing

AI product development starts at $5,000 for a focused application like a chatbot or document processor. More complex projects involving custom model training, multi-modal AI, or enterprise-scale infrastructure typically range from $10,000 to $20,000.

We always scope carefully before quoting. AI projects have a nasty habit of ballooning in scope, and we would rather set realistic expectations upfront.

❓ FAQ

• Can you integrate AI into our existing product?
  Yes. Most of our AI work involves adding intelligence to products that already exist — not building from scratch.

• Do we need a lot of data to get started?
  It depends on the approach. LLM-based solutions can work with relatively little custom data. Traditional ML models need more. We will tell you what is realistic during the feasibility sprint.

• How do you handle data privacy?
  Very seriously. We can deploy models on your own infrastructure, use private API endpoints, and implement data anonymization. For healthcare and finance clients, we follow HIPAA and SOC 2 requirements.

• What is a RAG pipeline?
  Retrieval-Augmented Generation. It is a way to give AI models access to your specific data without retraining them. Think of it as building a really smart search engine that can answer questions in natural language using your documentation, knowledge base, or product data.

• Can you fine-tune models for our specific use case?
  Yes. When off-the-shelf models do not perform well enough, we fine-tune open-source models on your data. This gives you better accuracy, lower latency, and reduced API costs.