AI Engineering Services for
Automation, Agents, Models, Voice
& Deployment
We build production-ready AI systems—workflow automation, agentic copilots, custom models, voice AI, ADP extraction, and reliable deployment—integrated into your stack with measurable outcomes.
Our AI service lines
Outcomes we target
How we work
From discovery to production.
Discovery & Assessment
Define use-cases, data readiness, security constraints, success metrics.
Build & Validate
Implement MVP in a real workflow, integrate, iterate with evaluation loops.
Deploy & Harden
Production hardening: access controls, monitoring, logging, guardrails.
Operate & Optimize
OptionalOngoing support, model/workflow tuning, new use-cases, improvements.
What you get in each phase
- Use-case prioritization
- Working prototype
- Production deployment
- Performance tuning
- Full documentation & handover
- Monitoring & runbooks
- Optional ongoing support
How you can work with us
Choose what fits your procurement.
Security & Trust
ISO-backed delivery
Certified processes & security
Security-first
Enterprise-grade controls
Full documentation
Runbooks + handover
Security questions?
Our security team is available to answer your questions and provide documentation for your compliance needs.
Contact Security TeamFrequently asked questions
What's the difference between your services and your products?
Our products (TattvIQ, VyasaIQ) are ready-to-deploy SaaS solutions. Our services help you build custom AI systems or implement our products with customization and integration support.
Do you deliver pilots and production deployments?
Yes. We start with discovery, build a pilot workflow to validate the approach, then proceed to production deployment with hardening and handover.
Can you work with our existing stack?
Absolutely. Integration is core to our service—CRM, ERP, ticketing, VoIP, inbox, databases, and custom internal systems.
Do you support on-prem or private cloud?
Yes. We support cloud, private cloud, and on-premises deployments based on your security and compliance requirements.
How do you measure success for AI systems?
We define metrics during discovery—accuracy, latency, cost, business outcomes. We build eval frameworks and dashboards for continuous monitoring.
Tell us your use-case. We'll map the
fastest path to production.
Get a personalized recommendation based on your specific needs.