Embedded in your team. Built for production.

1. 1
   PHASE 01

   Readiness Assessment

   Data audit, use-case ROI sizing, architecture and governance gaps, prioritised roadmap. Fixed scope.

   4–6 weeks
2. 2
   PHASE 02

   Pilot

   One use case, built with your team in your environment, with an eval suite and pre-agreed success metrics.

   3–6 weeks
3. 3
   PHASE 03

   Production build

   Hardened system: pipelines, retrieval, guardrails, observability, hand-over documentation.

   4–12 weeks
4. 4
   PHASE 04

   Run

   Managed AI operations, eval regression, model swaps, monthly architecture review.

   Ongoing

Questions, answered.

What does "built in India, shipped to the world" mean for an AI project?

The engineering happens in India; the system runs wherever your business does. Anveeksha Technologies is a product and engineering company whose senior engineers embed in your team, work in your tools and your environment, and deliver in phases that end in a running system. We are vendor-neutral across OpenAI, Anthropic and open-weight models, with private deployment when nothing can leave your environment.

What is a forward deployed engineer and when do I need one instead of a system integrator?

A forward deployed engineer is a senior engineer embedded in your team who ships a working system inside your environment rather than handing over a specification. You need one when a pilot has to become a production system quickly and the person who scopes the work must also be the person who ships it. Our Applied AI and Embedded Pods practices work this way: a named lead, a weekly demo, and a monthly architecture review with a principal engineer.

What does an AI readiness assessment include and what does it cost?

The AI Readiness Assessment runs 4–6 weeks and is led by a principal engineer. It delivers a data audit, use-case ROI sizing, architecture and security gaps, DPDP/GDPR/EU AI Act exposure, and a prioritised 6-month roadmap. Price on request — you receive a scope and investment range within one business day.

RAG vs fine-tuning vs prompting — how do you decide?

Start with prompting and context engineering, because it is the cheapest to change. Use retrieval (RAG) when answers must be grounded in your own documents, cited, and permission-aware. Fine-tune or move to a small model when you need a consistent format, lower latency or cost at volume, or behaviour that prompting cannot hold. In every case the decision is made against an evaluation suite, not by preference.

How do you evaluate an LLM application before launch?

Every AI deliverable ships with its evaluation harness and golden set. Success metrics are agreed before the pilot starts, the suite is run against the golden set before launch, and you keep it when we leave so your team can catch regressions before your users do.

Can AI be deployed inside our VPC without data leaving?

Yes. Work happens in your VPC or on-premises by default, and nothing leaves without written scope. We are vendor-neutral, use private endpoints with model providers, and deploy open-weight models on request, including private or air-gapped deployment.

What does DPDP compliance mean for an AI project in India?

India's Digital Personal Data Protection Act governs how personal data is collected, used and retained, so an AI project has to account for consent, purpose limitation, retention and grievance handling from the design stage. Our Digital Transformation practice maps DPDP, GDPR and EU AI Act exposure as part of the Readiness Assessment, and we design for deployment inside your VPC.