Applied AI
Perception and prediction in production — voice, vision, language and forecasting, wired into products people actually use.
Applied AI
What is applied AI?
Applied AI covers models solving a specific perception or prediction task — recognising speech, reading documents, classifying images, understanding text or forecasting demand — deployed inside a working product. The value comes from the integration and the accuracy threshold, not the model architecture.
Accuracy is a product decision
Every applied AI system has an accuracy ceiling and a cost of being wrong. A vision model catching defects at 94% is excellent if a human reviews the flagged items and terrible if it silently rejects stock. So the first conversation is not about models — it is about what happens on a wrong answer, and what threshold makes the system worth running.
Once that is settled the engineering is tractable: collect representative data, establish a baseline, measure honestly against held-out examples, and ship behind a fallback.
Speed
MVP development
A working AI product in weeks, built to test the hypothesis rather than to impress.
Read moreVoiceVoice AI agents
Telephony and in-app voice agents that handle calls end to end in multiple languages.
Read moreChatConversational AI & chatbots
Assistants on web, WhatsApp and app that resolve rather than deflect.
Read moreVisionComputer vision
Inspection, counting, document capture, safety monitoring and OCR pipelines.
Read moreLanguageNatural language processing
Classification, extraction, sentiment and search over large volumes of text.
Read moreForecastingPredictive analytics
Demand, churn, credit and maintenance forecasting that feeds a decision, not a dashboard.
Read more
Frequently asked
Questions we get about this
How much data do we need?
Less than the folklore suggests for most tasks. Pre-trained models mean a few hundred well-labelled examples often beats ten thousand messy ones. What matters is that your examples represent the messy reality the system will meet, including the awkward edge cases.
Can these systems work in Indian languages?
Yes — Hindi, Marathi and other Indian languages are well supported for speech and text, though quality varies by language and by domain vocabulary. We test on your actual data before committing to an accuracy target, because published benchmarks rarely reflect regional usage.
What does ongoing maintenance look like?
Models drift as the world changes. We set up monitoring against a held-out set, alert on degradation, and schedule retraining. Budget for maintenance from the start — an unmonitored model quietly gets worse.
Talk it through before you commit
A discovery call is a working session on your constraint, not a sales pitch.
Related
Where this connects
Quick inquiry
Tell us what you're trying to build
A short note is enough. You'll hear back from the team, not a bot — usually within one working day.
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