We help businesses integrate Generative AI in ways that are practical, responsible, and genuinely useful not just impressive in a demo.
GPT-4 • Claude • Gemini • LLaMA • Stable Diffusion
Generative AI Development
Generative AI Development Services
What We Do
Generative AI can write, summarise, reason, generate images, and hold conversations at a quality and scale that was not possible two years ago. But translating that capability into something that works reliably in your business integrated with your systems, grounded in your data, and performing consistently in production is a different problem from running a demo. That’s the problem we solve.
We work with GPT-4, Claude, Gemini, and open-source models including LLaMA and Mistral. We use LangChain and LlamaIndex to build RAG systems and agent workflows. And we build the data pipelines, APIs, and integrations that connect AI capability to the systems your business actually runs on.
Our Generative AI Services
LLM Integration GPT-4, Claude, Gemini & Open-Source
We integrate large language models into your existing products and workflows adding intelligent text generation, summarisation, question-answering, and reasoning where they create real value.
RAG systems AI grounded in your own documents and knowledge base
Document summarisation and intelligent content extraction
Automated report and content generation pipelines
Semantic search over large document collections
Fine-tuned models for domain-specific tasks
elf-hosted open-source LLM deployment for data privacy requirements
AI Chatbot & Virtual Assistant Development
We build AI-powered chatbots and virtual assistants that handle customer queries, support internal teams, and automate routine interactions with proper conversation design, fallback handling, and human escalation built in.
Customer support chatbots with product and policy knowledge
Internal HR, IT helpdesk, and knowledge base assistants
Sales and lead qualification conversational agents
WhatsApp, Slack, Teams, and web widget deployment
NLP & Text Analytics
We build NLP systems that extract structured insight from unstructured text classifying documents, identifying entities, detecting sentiment, and routing information automatically.
Sentiment analysis and customer feedback processing
Named entity recognition and information extraction
Document classification and intelligent routing
Contract and legal document analysis
Multilingual NLP for regional language requirements
Computer Vision & Image Generation
We build computer vision systems for quality inspection, document processing, and visual search and image generation pipelines for product imagery, catalogue automation, and visual content at scale.
AI image generation using Stable Diffusion and DALL-E
Product image variation and editing for e-commerce
Automated visual quality control for manufacturing
OCR and intelligent document and form processing
Facial recognition and identity verification
What Generative AI Actually Does for Your Business
Reduces repetitive knowledge work
Reading, writing, summarising, classifying, and extracting information from documents tasks that scale poorly with headcount can be automated or significantly accelerated with LLMs.
Improves customer experience without scaling support costs
Well-designed AI assistants handle a meaningful proportion of routine customer queries accurately and instantly, 24 hours a day.
Makes unstructured data usable
Most enterprise data is unstructured text, documents, emails. NLP and computer vision turn this into structured, queryable information your business can act on.
Enables smarter search and knowledge access
RAG systems let you build search and Q&A over your own documents finding relevant content based on meaning, not just keywords.
Accelerates content and creative workflows
Content drafting, report automation, image generation, and personalisation at scale when systems are designed carefully and outputs are reviewed appropriately.
Technology Stack
Image Generation
Stable Diffusion • DALL-E 3 • ControlNet
LLM Frameworks
LangChain • LlamaIndex • Haystack
NLP Libraries
Hugging Face Transformers • spaCy • NLTK • SBERT
LLM Models
GPT-4, GPT-4o (OpenAI) • Claude 3 (Anthropic) • Gemini (Google) • LLaMA 3, Mistral (open-source)
Computer Vision
OpenCV • PyTorch • TensorFlow • YOLO
Vector Databases
Pinecone • Weaviate • Chroma • FAISS • Qdrant
Evaluation & Monitoring
LangSmith • Ragas • Weights & Biases • PromptLayer
Cloud & Deployment
AWS Bedrock & SageMaker • Azure OpenAI Service • Google Cloud Vertex AI • Docker • Kubernetes
Industries We Serve
Healthcare
Clinical note summarisation, medical document processing, patient-facing symptom triage, and knowledge base Q&A for clinical staff.
Finance & Fintech
Contract analysis, regulatory document summarisation, financial report generation, customer service automation, and risk assessment tools.
Retail & eCommerce
Product description generation, customer service chatbots, visual search, automated catalogue image variation, and review sentiment analysis.
Logistics & Manufacturing
Visual quality control, automated operational documentation, predictive maintenance reporting, and internal knowledge assistants for operations teams.
Education
AI tutoring assistants, automated feedback on student work, content summarisation, and assessment generation.
Travel & Hospitality
Customer service chatbots, personalised travel content, multilingual communication, and automated review response generation.
How We Work
- 01. Use Case Assessment
- 02. Data & Infrastructure Review
- 03. Solution Design & Model Selection
- 04. Iterative Development
- 05. Deployment & Monitoring
- 06. Ongoing Optimisation
We evaluate whether Generative AI is the right approach before proposing anything.
We assess your data quality, structure, volume, and privacy requirements.
We choose the right model and architecture for your accuracy, cost, and latency requirements.
We build in sprints with regular evaluation checkpoints and systematic prompt engineering.
We deploy with monitoring in place tracking performance and detecting degradation from day one.
We retrain, update prompts, and upgrade models to keep performance strong over time.
Why Web Chip Armor
Honest about what AI can and cannot do
We assess every use case carefully, flag limitations upfront, and design with appropriate human oversight. You will never receive an AI demo from us that collapses in production.
Production-grade engineering, not prototype thinking
Building an AI demo is easy. Building a system that performs reliably at scale, handles edge cases, and can be monitored over time is harder. We build the latter.
Evaluation built in from day one
We establish clear metrics before we build and measure throughout. You will know whether the system is working not just at launch, but six months later.
Full-stack capability
Data engineering, model integration, API development, frontend, and cloud deployment we cover the full stack so nothing falls between teams.
Responsible AI practices
We take responsible use seriously particularly in Healthcare and Finance. Transparency, appropriate oversight, and data privacy are built in, not added as an afterthought.
Frequently Asked Questions
Which LLM should I use GPT-4, Claude, or Gemini?
It depends on your requirements. GPT-4 is a strong general-purpose choice. Claude performs particularly well on long-context tasks. Gemini has strong multimodal capabilities. For data privacy requirements, open-source models like LLaMA 3 and Mistral are viable self-hosted alternatives. We assess your accuracy, cost, latency, and privacy requirements and recommend accordingly.
What is RAG and when should I use it?
RAG (Retrieval-Augmented Generation) grounds an LLM’s responses in your own documents and knowledge base, reducing hallucination and keeping answers relevant to your specific context. It’s the right approach for most enterprise AI assistants, customer service chatbots, and internal knowledge tools where accuracy matters.
How do you prevent AI hallucinations?
We use RAG to ground responses in retrieved documents, prompt engineering to acknowledge uncertainty, output validation, human review for high-stakes outputs, and systematic evaluation using metrics like faithfulness and answer relevance. We don’t promise zero hallucination we design systems that minimise it and make it detectable.
Can Generative AI work with our sensitive or regulated data?
Yes, but it requires careful architecture. For sensitive data we typically recommend Azure OpenAI Service or AWS Bedrock for enterprise privacy guarantees, or self-hosted open-source models in your own infrastructure. We design the right architecture for your compliance requirements before any data touches an AI model.
How long does a Generative AI project take?
A focused chatbot or NLP pipeline typically takes six to twelve weeks. A more complex RAG system with deep data integration takes three to four months. We give you a realistic timeline after understanding your use case and data situation.
How much does it cost?
It depends on the complexity of the use case, data requirements, and engagement model. We provide a detailed estimate after a discovery conversation. We offer both fixed-price project and dedicated team engagement models.