ChatGPT application development that ships a product, not a browser tab.

• 01

  Internal knowledge assistants
  An AI assistant that answers questions from your internal documents, policies, product knowledge, and SOPs, including content that lives in SharePoint. Staff get instant, accurate answers with sources cited, grounded in documents you control rather than a colleague's memory.
• 02

  Customer support AI
  An AI layer that handles the first line of customer queries using your product documentation, FAQs, and support history. Complex queries escalate to human agents with context, so support volume and response time drop while CSAT rises.
• 03

  Document processing and extraction
  AI that reads your documents, contracts, invoices, reports, and forms, and extracts structured data from them: reviewing contracts for specific clauses, classifying support emails by topic, or pulling line items from purchase orders. What took hours takes seconds.
• 04

  AI copilots for existing software
  An AI assistant embedded in your existing CRM, ERP, or project management tool that helps users draft communications, generate reports, answer questions, or suggest next actions. The AI knows the context of what the user is doing because it can see the application data.
• 05

  Content generation with approval workflows
  AI-generated content, product descriptions, email campaigns, social posts, and reports, with human review and approval before publishing. The AI does the first draft, humans edit and approve, and you get the volume without losing quality control.
• 06

  Conversational data interfaces
  Query your database in plain English instead of writing SQL or waiting on the BI team for a report. Business users ask questions in natural language and get answers from your data, and we build the query translation layer, the data access controls, and the result presentation.

1. Step 01
   01

   Use case scoping

   We define exactly what the ChatGPT application needs to do, inputs, outputs, accuracy requirements, and what happens when the AI gets it wrong. Most projects fail because the use case is too vague. We get specific before we design anything.

   • Use case definition with input/output specification

   • Accuracy requirements and acceptable failure modes

   • User workflow mapping (who uses this, when, and why)

   • Fixed-cost scope agreed before any development begins

2. Step 02
   02

   Data architecture & RAG

   If your application needs to answer questions from your proprietary data, we design the RAG pipeline, indexing your documents, database records, or knowledge base into a vector store that the model can search before generating a response. Your data, not public training data, drives the answers.

   • Knowledge base audit and ingestion pipeline design

   • Chunking strategy and embedding model selection

   • Vector store setup and indexing

   • Hybrid retrieval for maximum accuracy

3. Step 03
   03

   Prompt engineering

   We build and test the system prompts that define how the model behaves for your use case, what it answers, how it answers, when it declines, and how it cites sources. Good prompt engineering is the difference between a useful product and an inconsistent demo.

   • System prompt design and constraint definition

   • Few-shot examples for domain-specific behaviour

   • Guardrail design for out-of-scope inputs

   • Output format specification for downstream use

4. Step 04
   04

   Application build

   We build the application interface and backend, the UI your users interact with, the API layer that handles model calls, the approval workflows for human review, and the logging infrastructure for monitoring. The ChatGPT capability is embedded in a product, not a raw chat window.

   • Frontend interface design and development

   • API layer and request handling

   • Human review and approval workflows where needed

   • Usage logging and audit trail

5. Step 05
   05

   Testing & monitoring

   We test the application against real inputs, including the edge cases that break most AI products. Hallucination patterns, out-of-scope queries, and high-stakes decisions are all tested before launch. Post-launch monitoring tracks response quality, latency, cost, and user adoption.

   • Systematic hallucination testing

   • Edge case and adversarial input testing

   • User acceptance testing

   • Production monitoring for quality, cost, and latency

• 01

  RAG Pipeline Development

  Build the retrieval architecture that grounds ChatGPT responses in your documents and databases.

• 02

  Generative AI Integration

  Add AI capabilities using Claude, Gemini, or open-source models alongside or instead of GPT.

• 03

  LLM Integration

  Direct LLM integration without a full application build.

• 04

  AI Document Intelligence

  AI document reading and extraction for invoices, contracts, and forms.

• 05

  Business Process Automation

  Extend ChatGPT capabilities into end-to-end automated business workflows.

Go deeper

• 01

  Healthcare AI

  HIPAA-compliant AI for patient monitoring, clinical decisions, and care workflows.

• 02

  FinTech AI

  Fraud detection, document processing, and compliance intelligence.

• 03

  Logistics AI

  Route optimisation, demand forecasting, and exception handling.

• 04

  Insurance AI

  Claims automation, underwriting risk scoring, and compliance monitoring.

• 05

  Retail AI

  Personalisation, demand forecasting, and inventory optimisation.

• 06

  Hospitality AI

  Revenue AI, guest communication, and booking automation.