AI that runs on your data — not on a demo loop.
Computer vision, document intelligence, forecasting, LLM-powered workflows. We build AI that lives in production, integrates with your existing systems, and pays for itself in months, not years.
Five categories where AI actually earns its keep.
Most "AI projects" never ship. They die in PoC limbo because someone built a model but nobody integrated it into the workflow that needed it. We start from the workflow and work backward to the model.
Computer vision
Object detection on shop floors, OCR on documents, defect detection in production, AI on CCTV for security and operations. The category that pays back fastest.
Document intelligence
Extract structured data from invoices, POs, contracts, lab reports, KYC docs. Classify and route. We've replaced entire data-entry teams.
Forecasting & prediction
Demand forecasting, churn prediction, predictive maintenance, dynamic pricing. Models that watch your data and make calls before humans would.
LLM-powered workflows
Internal copilots, knowledge-base chatbots, agent systems that draft, summarize, classify. Built on OpenAI, Claude, or open models depending on what fits.
AI on existing CCTV
People counting, attendance from face, intrusion alerts, PPE detection, workforce intelligence. No new hardware in most cases — we run on your existing IP cameras.
Custom model training
When frontier APIs don't fit — data privacy, unit economics, latency — we fine-tune open models (LLaVA, YOLO, Qwen, Llama) or train from scratch on your data.
A blunt question we ask every client.
"What decision does this AI make easier?"
If you can't answer that in one sentence, the AI project will struggle. Every successful AI deployment we've shipped maps to a specific human decision: which invoice to flag, which customer to call, which machine to inspect, which camera frame to alert on.
We start every engagement by mapping that decision. Then we work backward: what data informs it, what accuracy is good enough, what the cost of a wrong answer is, where the model output goes.
The model is the easy part. The decision wrapper around it is where projects live or die.
Frontier API vs. open model — how we pick
- Language, reasoning, document tasks → frontier APIs (Claude, GPT, Gemini) usually win on accuracy and shipping speed
- High-volume, latency-sensitive, or unit-economics-sensitive → fine-tune open models
- Computer vision on edge devices or CCTV streams → almost always open (YOLO, RT-DETR, custom)
- Data privacy / on-prem / regulated → open models, self-hosted
- Multi-modal vision + reasoning → frontier API if quality matters, open model if scale matters
Models, frameworks, infrastructure we work with daily.
Straight answers, no AI hype.
What kinds of AI projects do you take on?
Computer vision (object detection, OCR, CCTV analytics), document intelligence (extraction, classification, summarization), forecasting and prediction (demand, churn, maintenance), and LLM-powered workflows (chatbots, copilots, agent systems). We focus on production AI that lives on real data — not lab demos.
Do you build with OpenAI/Claude or train custom models?
Both, picked by fit. For language, document, and reasoning tasks, frontier APIs (OpenAI, Anthropic, Gemini) usually win on quality-per-rupee. For computer vision and domain-specific tasks where data privacy or unit economics demand it, we fine-tune open models or train from scratch.
We have data but no AI team — can you start from zero?
Yes. Most clients come to us with a problem and a database, not a model. We handle everything — data audit, labeling, model selection, training, deployment, monitoring. You don't need an in-house ML engineer.
How long until we see results?
A working proof of concept on your data takes 2 to 4 weeks. Production-grade deployment with monitoring and feedback loops adds 4 to 12 weeks depending on integration depth. We don't believe in six-month research projects with nothing to show.
Can AI run on our existing CCTV system?
Almost always yes. We build Praxate — our own CCTV workforce intelligence product — so this is daily work for us. We plug into existing IP cameras over RTSP or pull from your DVR/NVR. No new hardware in most cases.
What about data privacy?
We default to self-hosted open models when client data can't leave your infrastructure. We've shipped on-prem and air-gapped AI deployments. We also sign NDAs and DPAs before kickoff for any project touching sensitive data.
How do you price AI projects?
PoCs are typically fixed-price (₹3L–₹8L depending on scope). Production deployment is either fixed-price after PoC validation, or T&M with weekly caps. Ongoing model maintenance and retraining is a small monthly retainer.
Got an AI idea? Let's pressure-test it.
30-minute call. We'll tell you honestly whether AI is the right answer, what the data needs to look like, and what a realistic PoC would cost.