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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Turn your data into an AI that actually knows your business
Custom Retrieval-Augmented Generation pipelines that ground AI responses in your actual data — documents, knowledge bases, databases, and internal tools. No hallucinations. Just your data, made searchable and conversational.
10+
RAG systems built
95%
Avg. retrieval accuracy
4 wks
Avg. implementation time
500K+
Documents indexed
Generic chatbots hallucinate. A properly built Retrieval-Augmented Generation (RAG) system grounds AI responses in your actual data: documents, knowledge bases, databases, and internal tools.
Most RAG implementations fail because of bad chunking, wrong embedding models, or no evaluation pipeline. This specialised service handles the full stack — from data ingestion and chunking strategy through embedding selection, retrieval optimisation, generation layer with hallucination guardrails, and automated evaluation against ground-truth Q&A pairs.
I build custom RAG pipelines that let your team or customers ask questions and get accurate, sourced answers from your own content. Whether you need a document Q&A system, an internal knowledge assistant, a customer-facing AI support chatbot, or a semantic search engine — every system is built with measurable accuracy from day one.
Upload PDFs, docs, or knowledge bases and get accurate, cited answers. Intelligent chunking strategies, hybrid search (vector + keyword), and citation-backed responses with source references for every answer.
AI that searches across your company's Notion, Confluence, Google Drive, Slack archives, or custom databases. Role-aware access control so users only see content they are authorised to view.
Chatbots grounded in your product docs, help center articles, and support ticket history. Automatic escalation to human agents when confidence scores fall below threshold.
Replace keyword search with meaning-based retrieval across large content libraries. Understands user intent, synonyms, and contextual meaning — not just exact keyword matches.
Combine vector search, keyword search (BM25), and structured database queries for maximum retrieval accuracy. Re-ranking, metadata filtering, and query expansion for precision at scale.
Automated testing pipeline against ground-truth Q&A pairs with recall, precision, and faithfulness metrics. Continuous monitoring dashboard so you can measure and improve accuracy over time.
We audit your data sources, content formats, and access patterns. Determine optimal chunking strategy (semantic, recursive, or agentic), embedding model selection, and vector database choice based on your scale, accuracy, and latency requirements.
Build the data ingestion pipeline: parsing, cleaning, chunking, embedding generation, and vector indexing. Production-grade pipeline with incremental update support, error handling, and reprocessing capabilities.
Implement the retrieval pipeline with hybrid search (vector + BM25 + structured), re-ranking, and metadata filtering. Build the generation layer with prompt engineering, citation formatting, confidence scoring, and hallucination guardrails.
Integrate the RAG system into your product surfaces (chat UI, search bar, API endpoints). Run automated evaluation against ground-truth dataset. Deploy with monitoring, logging, and a 90-day accuracy tracking period.
EduPilotPro needed an AI attendance agent that could process natural language queries across thousands of student records, attendance logs, and academic policies — returning accurate, cited answers in real time. Traditional keyword search could not handle the variety of query formulations or link across disparate data sources.
We built a hybrid RAG pipeline using Supabase pgvector for vector storage, OpenAI Embeddings for semantic representation, and BM25 keyword search as a fallback layer. A LangChain orchestration layer handled query routing, retrieval fusion, and generation with citation formatting. The system was integrated into EduPilotPro's existing admin dashboard as a chat interface.
Many of our best projects combine two or more services from the list below.
Ship AI agents that actually work in production
A production-ready healthcare operations platform deployed in 12 weeks
A production-ready fitness operations platform deployed in 12 weeks
Let's build something great
Book a free 45-minute scoping call. Walk away with a clear picture of what we would build, how long it would take, and how much it would cost — regardless of whether you move forward.
Average response time: 2 business hours. No commitment required.
"App Corp delivered a production-ready AI platform in 11 weeks that our internal team estimated would take 9 months. The architecture is clean, the docs are thorough, and the product works exactly as specified."
Emily Park
CTO, PetScreening
50+
MVPs
98%
Retention
10wk
Avg. launch