AI & Machine Learning
How to add AI to your product without the hype
A practical framework for choosing AI features that users actually value — and shipping them reliably, safely and on budget.
Noeal Rajeev Thaleeparambil · 6 min read
Services · 01
AI development that earns its place in your product.
Designing and shipping practical AI: assistants, automation, computer vision and machine learning models integrated into your products and workflows.
- LLM apps & AI agents
- Machine learning models
- Computer vision
- AI integration & automation
Overview
How we approach it.
We build AI features people actually use: assistants that answer from your own data, automation that removes repetitive work, and models that see, classify and predict.
Every engagement starts with the problem, not the model. We prototype quickly against real data, measure quality before launch, and ship with the monitoring, guardrails and cost controls production AI needs.
What we deliver
Capabilities.
- 01
LLM apps & assistants
Chat and copilot experiences grounded in your documents, products and policies, with citations and safe fallbacks.
- 02
Retrieval (RAG) pipelines
Ingestion, chunking, embeddings and vector search tuned for answer quality, kept fresh as your content changes.
- 03
AI agents & automation
Multi-step workflows that call your tools and APIs, with human approval where the stakes are high.
- 04
Machine learning models
Classification, forecasting and recommendation models trained, evaluated and served behind fast APIs.
- 05
Computer vision
Detection, recognition and tracking from cameras and images, from cloud inference to on-device models.
- 06
Evaluation & MLOps
Test sets, quality metrics, monitoring and cost tracking so the system keeps improving after launch.
Use cases
Where it helps.
- Customer support assistant that answers from your help centre
- Internal search across documents, tickets and wikis
- Document extraction for invoices, CVs and forms
- Maintenance and recommendation advisors inside an app
- Safety and quality checks from camera feeds
Technology
Tools we use.
- Python
- PyTorch
- TensorFlow
- OpenAI & Anthropic APIs
- Open-weight LLMs
- LangChain
- pgvector
- FastAPI
- OpenCV
- Docker
We choose tools for your constraints, not habit. If your team already has a stack, we work within it.
Related work
Case studies.
Platform
RevWiki
RevWiki is an intelligent automotive platform that BreaktroughF1 designed and built for the RevWiki team, helping people discover, evaluate and maintain vehicles. It merges AI-powered insights, verified owner data, and a vibrant community of enthusiasts to deliver transparent, data-driven automotive intelligence. From car comparisons and performance analytics to maintenance forecasts and genuine part sourcing, RevWiki serves as the digital backbone for modern vehicle ownership and discovery.
Read case study
Platform
CodeBreak
CodeBreak is an innovative learning platform designed for hands-on coding projects. It offers structured roadmap, essential skills training, and chatbot-assisted guidance to help developers build real-world projects with confidence.
Read case study
Application
UniView
UniView is an advanced AR/VR-based campus navigation system designed to enhance the way students, faculty, and visitors explore educational institutions. It offers real-time directions, immersive 3D views of the campus, and interactive details about key locations, making navigation effortless and engaging.
Read case studyHow we work
A clear process, from first call to launch.
- Step 01
Discover
We learn your business, users and constraints before a line of code.
- Step 02
Design
Flows, interfaces and architecture shaped and validated with you.
- Step 03
Build
Iterative delivery with regular demos, reviews and clear milestones.
- Step 04
Support
Launch, monitoring and ongoing improvements once you're live.
Should we use RAG or fine-tune a model?
Most products should start with retrieval-augmented generation: it keeps answers grounded in your current data and is cheaper to change. Fine-tuning helps when you need a consistent style or format, or a smaller, faster model. We often combine both.
Will our data be used to train public models?
No. We use providers and settings that exclude your data from training, and can run open-weight models in your own cloud when data must not leave your infrastructure.
How do you know the AI is good enough to launch?
We build an evaluation set from real examples before writing features, then track accuracy, refusal rate, latency and cost on every change. Launch happens when those numbers meet the targets we agreed with you.
How long does an AI proof of concept take?
Typically two to four weeks to a working prototype on your data, with a clear go or no-go recommendation and an estimate for production.
From the blog
Further reading.
AI & Machine Learning
RAG vs fine-tuning: which is right for your AI feature?
Retrieval-augmented generation and fine-tuning solve different problems. Here's how to tell which one your product needs.
Noeal Rajeev Thaleeparambil · 6 min read
Contact
Ready to transform your business?
Join us in pushing the boundaries of what’s possible. Tell us what you’re building and we’ll take it from there.