Your team is using ChatGPT manually when it should be automated into your workflow?
Generic AI chatbots giving wrong answers because they don't know your business?
ChatGPT is a model, not a product. What you need is a product built on top of it -- with your data, your use cases, your safeguards, and your user experience. Generic ChatGPT integrations fail because they bolt the model onto an existing workflow without redesigning the workflow around what AI can actually do.
We build custom ChatGPT applications that are designed around your specific use case -- from the prompt architecture to the data layer to the interface your users actually interact with.
GPT-4o, GPT-4 Turbo, and OpenAI API integration with your proprietary data
Custom prompt engineering and fine-tuning for your domain
RAG (retrieval-augmented generation) for accurate, source-backed responses
20+ AI products shipped using OpenAI models
Most businesses start with ChatGPT.com. Teams use it manually -- someone copies a document into the chat, asks a question, and pastes the answer somewhere else. That works until you want consistency, scale, or the AI to act on your data rather than public training data.
A custom ChatGPT application replaces the manual step with a product. The model receives your documents automatically, your data is indexed and searchable, the prompts are engineered for your use case, and the output flows into your workflow without anyone copying and pasting.
The difference between "using ChatGPT" and "building with ChatGPT" is the gap between a tool and a product.
If your use case requires retrieving accurate answers from a large document library, we build the RAG pipeline that connects GPT to your data. For integrations using Claude, Gemini, or open-source models, see our generative AI integration service.
An AI assistant that answers questions using your internal documents, policies, product knowledge, and SOPs. Staff get instant, accurate answers without searching through SharePoint or asking a colleague. Sources are cited. Wrong answers are visible because they're grounded in documents you control.
An AI layer that handles the first line of customer queries using your product documentation, FAQs, and support history. Complex queries are escalated to human agents with context. Support volume drops. Response time drops. CSAT goes up.
AI that reads your documents -- contracts, invoices, reports, forms -- and extracts structured data from them. Review contracts for specific clauses. Classify support emails by topic. Extract line items from purchase orders. What took hours takes seconds.
An AI assistant embedded in your existing application -- a CRM, an ERP, a 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.
AI-generated content -- product descriptions, email campaigns, social posts, reports -- with human review and approval before publishing. The AI does the first draft. Humans edit and approve. You get the volume without losing quality control.
Query your database in plain English. Instead of writing SQL or waiting for a report from the BI team, business users ask questions in natural language and get answers from your data. We build the query translation layer, the data access controls, and the result presentation.
Tell us what you want to automate or improve with AI. We'll design the architecture and give you a fixed cost to build it.
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
Tell us your use case and we'll design the prompt architecture, data pipeline, and application interface. Fixed cost.
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
RAG Pipeline Development — Build the retrieval architecture that grounds ChatGPT responses in your documents and databases
Generative AI Integration — Add AI capabilities using Claude, Gemini, or open-source models alongside or instead of GPT
LLM Integration — Direct LLM integration without a full application build
AI Document Intelligence — AI-powered document reading and extraction for invoices, contracts, and forms
Business Process Automation — Extend ChatGPT capabilities into end-to-end automated business workflows
Tell us your use case and your data. We'll design the prompt architecture, RAG pipeline, and application interface at fixed cost.
We build custom applications that use OpenAI's GPT models for specific business use cases: AI assistants for customer support or internal knowledge search, document processing tools that summarise, extract, or classify content, AI copilots embedded in existing software, automated content generation tools with approval workflows, and conversational interfaces for complex data queries. The use case determines the architecture -- not all ChatGPT applications are chatbots.
Out-of-the-box ChatGPT doesn't know your business. We make it accurate through three approaches: (1) RAG (retrieval-augmented generation) -- the model retrieves relevant documents from your knowledge base before generating a response, so answers are grounded in your actual data. (2) System prompts and fine-tuning -- we engineer prompts that constrain the model's behaviour and, where appropriate, fine-tune a model on your domain-specific examples. (3) Guardrails -- we build validation layers that catch and handle responses that fall outside expected parameters.
Yes. We build RAG pipelines that index your internal documents -- PDFs, Word files, SharePoint content, database records, website content -- into a vector store that the model can search before generating responses. The model doesn't guess based on its training data -- it retrieves the right information from your sources and uses that to generate the response. Answers include source citations so users can verify them.
Hallucination is a real risk with any language model. We reduce it through RAG (answers are grounded in your actual documents, not model memory), confidence scoring (responses that don't find relevant sources are flagged or escalated), human review workflows for high-stakes decisions, and response logging so you can identify and fix systematic errors. We don't promise zero errors -- but we design systems with the failure modes in mind.
A focused first application -- for example, an internal knowledge search assistant or a document summarisation tool -- typically runs $20,000--$50,000. A full AI platform with multiple use cases, custom integrations, and a user interface typically runs $60,000--$150,000. The cost depends on the number of use cases, the complexity of the data pipeline, and the user interface requirements. We scope every project before pricing it.
Yes, you can. The API is well documented and the basic integration is straightforward. The hard part is: designing prompts that produce consistent, accurate results for your use case; building the RAG pipeline that connects the model to your data; handling failure modes (timeouts, rate limits, wrong answers); building the user interface and approval workflows around the AI capability; and testing and monitoring the system in production. These are engineering and product problems, not just API calls. We've solved them 20+ times.
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