Generative AI Development

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

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.

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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.

Start the Conversation

If you have a use case in mind or a problem you think AI might help with and you’re not sure yet we’d like to hear about it. No overselling, no buzzword-heavy pitch. Just a practical conversation about what Generative AI can realistically do for your business.