Build smarter with
artificial intelligence
that actually works.
Custom AI chatbots, computer vision pipelines, predictive ML models, and intelligent automation — built for real business problems. Test our AI live on this page.
pipelines delivered
on client datasets
Groq LPU architecture
delivery time
Six AI capabilities, one partner
From chatbots you can test on this page right now, to custom ML models trained on your data — we build AI that solves actual problems.
Custom AI Chatbots
Intelligent chatbots trained on your business data — product info, FAQs, policies, pricing. Embedded on your website, app, or internal tools.
- RAG — answers from your own documents
- Groq LLaMA / GPT-4o / Claude backend
- CRM handoff for qualified leads
- Multi-language support
RAG & Knowledge Base AI
Connect an LLM to your private documents, PDFs, wikis, or databases — answers come from your actual data, not training guesses.
- Upload PDFs, docs, URLs as knowledge
- Vector database (Pinecone / Chroma)
- Zero hallucinations on your data
- Internal or customer-facing
Computer Vision
Systems that see and understand images or video — for quality control, document scanning, face recognition, or product identification.
- Object detection (YOLO, DETR)
- OCR & document data extraction
- Defect detection for manufacturing
- Real-time video analysis
Predictive ML Models
Train models on your historical data to forecast churn, demand, price, revenue, or customer behaviour.
- Sales & demand forecasting
- Customer churn prediction
- Price optimisation models
- Anomaly detection systems
NLP & Text Intelligence
Classify support tickets, extract contract data, analyse sentiment, or summarise long documents at scale.
- Sentiment & intent classification
- Named entity recognition
- Auto-summarisation pipelines
- Contract & document review
AI Integration into Apps
Add AI to your existing website, SaaS, or internal tool — smart search, auto-tagging, content generation, recommendations.
- AI-powered search & recommendations
- Auto-tag and categorise content
- Smart data extraction
- AI content generation features
How we build AI that works in production
Most AI demos look good. Production AI needs to handle edge cases, bad data, and real users.
Problem Definition & Data Audit
We start by understanding the exact problem, the data available, and what “success” looks like in measurable terms. Most failed AI projects fail here — before any code is written.
Proof of Concept (48h)
Before committing to a full build, we deliver a working prototype using your actual data within 48 hours. You see real results — not slides — before any significant investment.
Build, Train & Evaluate
Full model or pipeline development with rigorous evaluation — train/test splits, cross-validation, bias testing, and performance benchmarking against your baseline.
Deploy, Monitor & Improve
Deployed via API or embedded directly in your product with monitoring, logging, and a feedback loop so the model improves over time rather than degrading.
Built on the best AI infrastructure available
We choose models and infrastructure based on your requirements — speed, cost, accuracy, and privacy all factor in.
LLM & Inference
ML & Vision Frameworks
Orchestration & Vector DBs
Ask TechPuls AI anything
Powered by Groq + LLaMA 3.3, trained on everything about TechPuls . Ask about our services, pricing, or how we work.
Will the chatbot hallucinate?
When built with RAG, the chatbot is grounded in your actual documents and can only answer from what you’ve provided. It won’t invent pricing, policies, or features. For anything outside its knowledge base, it says so — rather than guessing.
What’s the difference between Groq and GPT-4?
GPT-4o offers the highest reasoning quality for complex tasks. Groq uses a specialised LPU that runs LLaMA models up to 10x faster at a fraction of the cost — ideal for high-volume customer-facing chatbots. We recommend the right model based on your use case.
How long does it take to build a custom chatbot?
Basic chatbot with knowledge base and website embed: 3–5 days. Full RAG system with CRM integration: 2–3 weeks. Working prototype always delivered within 48 hours of receiving your knowledge base.
Is my business data safe?
Yes. We sign NDAs before handling any data. For sensitive industries, we can deploy fully local models using Ollama — meaning your data never leaves your server and no third-party API ever sees it.
Do I need a large dataset to train an ML model?
For LLM-based systems you need structured knowledge, not a large dataset. For custom ML models you typically need 1–3 years of clean historical data. We audit your data first and give an honest assessment before any commitment.
Ready to build AI that works
for your business?
Tell us the problem — we will deliver a working proof of concept within 48 hours. No long proposals. Just AI you can see in action before you commit.