AI products & services · Dublin, Bengaluru
Intelligence, built and put to work.
Medhayan Tech is an AI company. We implement AI inside the businesses that hire us, and we also build our own AI products. Both held to one standard.
The common thread
One capability. Two ways it reaches the world.
Everything here is AI. Sometimes we point it at yours; sometimes we point it at our own products. Same team, same methods, same bar. You either hire what we know, or you use what we’ve built.
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01 · Products
Products · Studio
AI products we own end to end. Opinionated, shipped, and held to a real quality bar rather than a demo bar.
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02 · Services
Services · Practice
We embed AI into how a business already works, from the first useful use case through to production and handover.
Services · How we work
We don’t wrap an API. We build systems.
Orchestrated, measured, and safe by design. The same pattern whether it’s our product or your problem.
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Custom AI applications
Products built around a real user need, not a demo.
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Agents & multi-agent pipelines
Planners, specialists, and judges that work together and stay in their lane.
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AI inside your product
Embed intelligence into the software your business already runs on.
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Evals & measurement
If you can’t measure it, you can’t trust it. We build the harness first.
Engagement
- 01 Diagnose Find the use case worth doing, and the one to skip.
- 02 Prototype A working system in front of real users, fast.
- 03 Ship To production, with evals and a safety gate.
- 04 Hand over Your team owns it, with the docs to prove it.
Proof · Inside a build
Niyati, a multi-agent Vedic-astrology system.
Niyati is our own AI product: a conversational Vedic-astrology guide. It runs as a multi-agent system on LangGraph, routing across Claude Opus 4.8 and Sonnet 5, GPT-5.6 and GPT-5.0, and Grok 4.5 and 4.6, with its own output judge on every response. Memory extraction is tuned to surface only what a turn actually needs, and a daily pipeline turns real conversations into new eval sets and feeds them back to optimize the system. It is live on AWS today, serving real customers and generating revenue. This is what we mean by building AI properly.
- Orchestration
- A multi-agent system built on LangGraph. A planner decides how each message should be handled and spawns the sub-agents it calls for, and those agents load the skills they need as they go.
- Models
- Claude Opus 4.8 and Sonnet 5, GPT-5.6 and GPT-5.0, Grok 4.5 and 4.6, routed by what the task actually calls for.
- Shared context
- One context layer every agent reads from, so no two parts of the system work from a different version of the user.
- Safety
- An output judge reviews every response before it reaches a user. It blocks forbidden topics, refuses confident claims on serious matters, and holds tone.
- Memory
- Extraction tuned for precision. A turn gets the handful of things it needs rather than the whole history, which keeps answers sharp and cost predictable.
- Evaluation
- Eval sets grown from real conversations, not written once and forgotten.
- Optimization
- A fully autonomous loop. Every day it reads the previous day’s conversations, writes new skills and new eval sets, and folds them back into the system on its own.
- Runtime
- Live on AWS, serving real customers and generating revenue.
The same pattern is what we bring to a client’s problem: orchestration, a shared context layer, a safety gate on every output, and a system that improves itself.
Team · Track record
Built by people who’ve shipped AI in production.
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Vaibhav Sharma
Dublin
- Asana
- RTU
- MDI
12+ years in professional services and client solutioning at Google and Asana, across some of the most complex products either company ships. Has spent that time making sure customers throughout Europe and APAC get the smoothest possible implementation, and the maximum value out of what they have bought.
Expertise
- Enterprise rollout
- Adoption and change
- EMEA and APAC delivery
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Avishek Nayak
Bengaluru
- Barq
- NIT
- MDI
12+ years in tech product management. Most recently Director of Product Management at Barq, the largest payment wallet in KSA, leading the customer support and growth charter. Took conversational AI all the way into production there: a chat and voice support agent in front of millions of customers, and Ops AI, a digital twin of the payment operations team.
Expertise
- Conversational AI in production
- Agent design and evals
- AI product strategy
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Abhishek Sachdeva
Bengaluru
- Barq
- IIT
IIT graduate with 8+ years building tech products, and engineering manager at Barq across the customer support and international payments charter. Built the international payments system from the ground up, and the AI chat and voice bots now serving 12mn+ customers in KSA.
Expertise
- AI engineering leadership
- Voice and chat agents at scale
- Regulated payments
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Dikshant Jain
Bengaluru
- Barq
- VIT
VIT graduate with 6+ years building products at scale. Built Barq’s transaction communications service, which carries 240mn messages a month, then built the martech platform from the ground up, with AI agents that let marketers define segments and journeys without ever touching event schemas or profile attributes.
Expertise
- High-throughput systems
- Agents for non-technical teams
- Data and martech platforms
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Aniket Singh
Bengaluru
- Barq
- IIT
IIT graduate with 6+ years building scalable products from scratch. Built the self-onboarding flow that brings on 12mn+ consumer customers, and the equivalent flow for hundreds of businesses, with AI carrying document verification end to end.
Expertise
- Workflow automation
- AI document processing
- Scale to millions of users
Principles
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First principles
We break problems down to what’s actually true, then rebuild. Status quo isn’t a reason.
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Craft is the point
The details are where trust is won. The higher the obsession, the more invisible it looks.
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One standard
Client work and our own products meet the same bar. No demo-grade shipping.
Contact
Start a project.
Tell us the problem, not the spec. We’ll tell you whether AI is the right tool and what the first step looks like.