Generative AI Development
Building intelligent AI experiences powered by next generation language models
We engineer production grade generative AI systems: from standalone LLM applications and AI chatbots to full RAG architectures, multimodal solutions, and custom Copilot experiences. Every system we ship is fine tuned on your domain data, tested against real world edge cases, and deployed with guardrails and monitoring from day one. Our team builds and maintains MoveChat.Ai in production, so every recommendation comes from running these systems ourselves.
What is included
LLM Application Development
Custom applications built on top of large language models for your specific business workflow. We handle model selection (OpenAI GPT 4o, Anthropic Claude, Llama 3, Mistral), API integration, output structuring, and production deployment. Use cases include document summarisation engines, contract analysis tools, internal knowledge assistants, and content generation platforms. Every application ships with error handling, rate limit management, fallback logic, and usage monitoring.
AI Chatbot Development
Conversational AI chatbots that go beyond scripted responses. We build intent aware, context retaining chatbots that deploy on WhatsApp, web widget, Telegram, Instagram, and Facebook Messenger. Includes natural language understanding, multi turn conversation management, handoff to human agents when confidence is low, and analytics dashboards tracking resolution rate, top queries, and escalation patterns. Our proprietary product MoveChat.Ai is built on this exact stack.
RAG Systems Development
Retrieval Augmented Generation pipelines that let LLMs answer questions using your private company data instead of general training knowledge. We handle document ingestion (PDF, DOCX, CSV, databases, APIs), chunking strategy design, embedding model selection, vector database setup (Pinecone, Weaviate, pgvector, Chroma), retrieval evaluation, and source citation in generated responses. The result is an AI system that gives accurate, verifiable answers grounded in your actual data.
Multimodal AI Solutions
AI systems that process and generate across text, images, audio, and video. Use cases include document OCR with intelligent extraction, image classification and tagging for product catalogues, audio transcription with summarisation, and video content analysis. We integrate vision models (GPT 4o vision, Claude vision), speech models (Whisper), and image generation models into unified workflows that accept multiple input types and produce structured outputs.
AI Copilot Development
Custom Copilot style assistants embedded directly into your application or internal tool. These are context aware AI sidebars that help users draft content, analyse data, navigate complex workflows, and make decisions faster within the tool they are already using. We build the plugin architecture, context injection layer, and user interface components so the Copilot feels native to your product, not bolted on.
AI Model Fine Tuning
When a base model does not perform well enough on your domain, we fine tune it using your proprietary data. This includes training data preparation and cleaning, fine tuning on OpenAI, Anthropic, or open source models (Llama, Mistral), evaluation against baseline benchmarks, and deployment of the fine tuned model with version management. Fine tuning is recommended when the domain is highly specialised (legal, medical, financial) or when output formatting requirements are strict.
Technologies We Use
OpenAI GPT 4o
Anthropic Claude
Llama 3
Mistral
LangChain
LlamaIndex
Pinecone
Weaviate
Docker
Kubernetes
Who this is for
SaaS companies adding AI features
You have an existing product and want to add AI powered search, summarisation, chat, or content generation without rebuilding your stack.
Operations teams automating knowledge work
Your team spends hours each day answering the same questions, processing documents, or searching internal knowledge bases. A RAG system or chatbot can handle 60 to 80 percent of this volume.
Enterprises building private AI
You need AI capabilities but cannot send proprietary data to external APIs. We deploy on your cloud, fine tune open source models on your data, and keep everything within your security perimeter.
Our Process
Discovery
Use case discovery and feasibility audit (week 1 to 2)
Prototype
Architecture design and model selection (week 2 to 3)
Build
Core build with RAG, chatbot, or Copilot (week 4 to 8)
Launch
Fine tuning, guardrails, and edge case testing (week 9 to 10)
Support
Production deployment with monitoring dashboard (week 11 to 12)
Ready to build with generative AI?
Book a free scoping call and get a tailored proposal within 48 hours.
Which LLM should we use for our project?
It depends on your accuracy requirements, latency tolerance, data privacy constraints, and budget. We benchmark GPT 4o, Claude, Llama 3, and Mistral on your actual data during the architecture phase and recommend the model that delivers the best results for your specific use case. Many projects use different models for different tasks within the same system.
How do you prevent hallucinations in production?
Three layers. First, RAG grounds the model's responses in your verified data rather than general knowledge. Second, we implement output validation checks that catch unsupported claims before they reach the user. Third, confidence scoring triggers human review when the system is uncertain.
Can the AI system work with our existing tools and databases?
Yes. We build integration layers that connect to any system with an API or database connection: CRM, ERP, ticketing, document management, internal wikis, SharePoint, Confluence, and custom databases.
How long does it take to see results?
A working prototype is typically ready in 3 to 4 weeks. Production deployment with guardrails and monitoring takes 10 to 12 weeks total. Simpler implementations like a FAQ chatbot or document search can be live in 6 weeks.