AI that works as hard as you do.
We help businesses move from AI experiments to AI in production: agents, assistants and models that are accurate, secure and measurably valuable.
AI Agents
Autonomous agents that plan multi-step tasks and act on your tools: update CRM, raise POs, reconcile accounts, reply to customers.
Customer Support AI
Omnichannel assistants on web, WhatsApp and voice that resolve queries, track orders and hand off to humans with full context.
Document Intelligence
Extract, validate and route data from invoices, contracts, KYC and forms with accuracy that beats manual entry.
Enterprise Search & RAG
Ask questions in plain language across SharePoint, Drive, Confluence, databases and tickets. Answers come with citations.
Computer Vision
Quality inspection, safety compliance, people counting, number-plate recognition and medical imaging.
Predictive Analytics
Forecast demand, predict churn, score leads and credit risk, optimise prices and detect anomalies before they cost you.
AI Copilots for Products
Embed generative AI features into your SaaS or app: smart compose, insights, natural-language queries and automation.
Voice AI
Inbound and outbound voice agents for appointment booking, collections, surveys and lead qualification in Indian languages.
A production AI stack, built for trust.
Every layer is observable, secure and swappable, so you are never locked into one model or vendor.
From idea to ROI in weeks, not years.
AI opportunity workshop
Map use cases, rank by value and feasibility, check data readiness.
Proof of concept
Working prototype on your real data with an evaluation scorecard.
Production build
Integrations, guardrails, security review, monitoring and training.
Optimise & expand
Track accuracy, cost and adoption. Roll out to new teams and use cases.
Your data stays yours. Always.
Security and governance are not add-ons. They are designed into every AI system we deliver.
- Enterprise model endpoints that never train on your data
- Private deployments in your AWS, Azure or GCP account
- Role-based access, PII redaction and full audit logs
- Automated evaluations for accuracy, bias and hallucinations
- Human approval for high-impact actions
- Aligned with India’s DPDP Act, GDPR and SOC 2 controls
AI questions, answered
Is our data safe when using large language models?
Yes. We use enterprise endpoints that do not train on your data, can deploy open-source models fully inside your cloud, and apply access controls, redaction and audit logs.
How long does an AI proof of concept take?
A focused proof of concept typically takes 3 to 6 weeks, using your real data and evaluated against agreed success metrics before you commit to a full build.
Do we need a lot of data to start?
Not always. Modern LLM-based solutions can deliver value with your existing documents and systems. For custom predictive models we assess data readiness first.
Put AI to work. Let's build something extraordinary.
Tell us about your idea. An engineer will get back to you within one business day with next steps.