Generic AI tools left homeowners with inaccurate property answers, so a FAISS and LangChain RAG pipeline trained on real construction knowledge was built, delivering 60% faster resolution with 2x accuracy improvement.

• Photo-based AI diagnosis engine for instant property assessment
• FAISS and LangChain RAG pipeline with OpenAI for domain accuracy
• Curated construction knowledge base for repair and assessment data
• User dashboard with photo upload, query tracking and source attribution
• Feedback loop continuously improving AI response relevance and precision
• Scalable AWS deployment with Django, React.js and Celery task queue

Existing AI tools lacked construction-domain accuracy, returned generic responses to vague property queries, and had no scalable architecture to serve homeowners, DIYers and realtors across RAG pipelines, AI models and cloud infrastructure simultaneously.

• Replace generic AI responses with construction-domain accuracy
• Enable photo-based property diagnosis without expert intervention
• Scale platform across three distinct user types
• Integrate RAG, AI models, and cloud infrastructure

Audit → Strategy → Development → Optimization → Growth

We audited failures in generic AI tools, identified domain gaps causing inaccurate outputs, then built a specialized construction knowledge base and chose FAISS and Celery to deliver expert-level property answers with domain accuracy and scalability.

Our AI Engineering team built a construction-specific intelligence platform for domain-trained precision.

• Curated specialized construction knowledge base
• Engineered FAISS-LangChain RAG pipeline for accuracy
• Built photo diagnosis flow with feedback refinement
• Deployed full AWS stack via Django and Celery
  

Askitect launched with a 99% satisfaction rate, 60% faster query resolution, and 2x the accuracy of generic tools, with every response anchored in real construction knowledge, transforming vague homeowner queries into expert-level property assessments within seconds.