OUR SERVICES
AI Products That See, Search and Decide
We build image recognition, vector search, machine learning pipelines and custom AI features that help products work with documents, images and business data.
Overview
AI is most useful when it is part of the product, not a separate experiment. Computing Yard builds AI solutions around real business work: finding the right document, recognising what is in an image, ranking results, or turning messy data into a decision.
We work with vector databases for semantic search, OpenCV for image recognition, and NumPy-based pipelines for preparing and transforming data. Models, embeddings and APIs are wired into the same web and backend systems your team already uses.
The goal is a reliable AI feature your users can depend on — with clear inputs, testable outputs and a path to improve as the data grows.
BENEFITS
Benefits
Useful Inside the Product
AI is designed as a feature users can actually complete work with, not a demo that stays in a notebook.
Works With Your Data
Images, documents and records are prepared, embedded and stored so search and recognition stay grounded in your business.
Faster, Clearer Decisions
Vector search and recognition reduce time spent hunting through files, photos and unstructured information.
Built to Improve
Pipelines, embeddings and evaluations are set up so the system can get better as more data arrives.
FEATURES
What We Deliver
01
Image Recognition
Detect, classify and locate objects in photos and video using OpenCV and computer-vision models, then surface the result in the product UI.
02
Vector Databases
Store embeddings in Pinecone, Weaviate or similar vector databases so users can search by meaning, not just keywords.
03
NumPy Data Pipelines
Prepare, clean and transform datasets with Python, NumPy and Pandas before training, embedding or serving a model.
04
Semantic Search & RAG
Retrieve the right passages from knowledge bases, policies and product docs, then pass them to a model with the right context.
05
Custom AI Product Features
Add scoring, recommendations, tagging and decision support to existing web or mobile products instead of shipping a standalone tool.
06
Model & API Integration
Connect OpenAI, PyTorch, TensorFlow or other model APIs to your backend, storage and user workflows with production-ready interfaces.
TECHNOLOGIES
Technologies
- Python
- NumPy
- Pandas
- OpenCV
- PyTorch
- TensorFlow
- scikit-learn
- Pinecone
- Weaviate
- OpenAI
- PostgreSQL
- AWS
HOW WE WORK
Our Process
- 01
Discover
We map the job to be done, the data you have, and whether image recognition, search, scoring or another AI approach is the right fit.
- 02
Data & Feasibility
We inspect images, documents and structured records, then define labels, embeddings and the NumPy-based pipeline needed to use them.
- 03
Design the Feature
We design how the AI result appears in the product — detections, ranked documents, confidence, and what happens when the model is unsure.
- 04
Build
We implement OpenCV or model inference, vector database indexing, APIs and the product interface around a testable pipeline.
- 05
Evaluate
We measure accuracy, retrieval quality and edge cases with real samples before the feature is exposed to users.
- 06
Launch & Improve
We deploy the feature, monitor results and keep improving embeddings, prompts and pipelines as usage grows.
FAQs
Frequently Asked Questions
Chatbots are one service. AI Solutions covers image recognition, vector search, machine learning pipelines and other AI features inside products.
Ready to Put AI to Work in Your Product?
Tell us about the images, documents or decisions you want to automate. We will outline a practical AI approach.