Team
Built by people who've already gotten regulated infrastructure through security review.
Mayank Tiwari
Product & Technology
Mayank joined Fisdom as its first engineer and became Head of Engineering, building the org from zero to roughly sixty people. He shipped and scaled regulated fintech products — stock broking and wealth management — and built that infrastructure inside 16 banks as part of Fisdom's distribution efforts. He stayed on in senior engineering leadership through Fisdom's acquisition by Groww. Earlier in his career: Motorola Mobility and Borqs.
Depth in the environment
Controls designed by people who built the systems they had to govern.
Fisdom ran broking, depository, and wealth products alongside bank distribution — so one platform answered to RBI, SEBI, NSE, BSE, NSDL, and CDSL at the same time.
Six regulators and market-infrastructure bodies, one platform · Distributed inside 22 PSU & private-sector banks
Satish Chandra Rabha
Infrastructure
Satish spent 10 years on Fisdom's backend and platform, running high-volume transaction and auto-debit workloads in production. He led the Kubernetes migration and set up CI/CD, design review, and release governance, owning infrastructure end to end — from deployment automation to databases. Earlier, he did platform work for Qualcomm, LG, and Samsung.
Discipline in the deployment
Enterprises buy confidence, and confidence is built through delivery.
Repeated deployments into regulated environments at Fisdom — security reviews, integration constraints, production cut-overs — codified into a rollout playbook that Ordinis reuses.
Built deployment playbooks at Fisdom
Ramakanth Beta
GTM & Enterprise Sales
Ramakanth has 20+ years in enterprise sales, at Citibank, Oracle, and VMware, building enterprise revenue through banks, consulting partners, and GSIs. He co-founded Moneybloom in 2015, signing six banks and building a wealth-management PaaS for NSE. He holds a PGDM from IIM Ahmedabad and a B.Tech from IIT Madras.
Fluency with the buyer
Selling infrastructure into regulated enterprises is its own discipline.
Enterprise product & engineering, distributed-systems infrastructure, and twenty years of enterprise sales at Citibank, Oracle, and VMware — in one founding team.
One team. ~9 years working together.
Dr. Parashjyoti Borah
Product Advisory
Assistant Professor, Indian Institute of Information Technology (IIIT) Guwahati
A researcher in machine learning and deep learning, focused on building intelligent, efficient AI systems — spanning computer vision, NLP, climate and weather intelligence, and model efficiency. His work combines mathematical formulation, experimental research, and practical implementation, from efficient vision architectures and neural network compression to machine learning for atmospheric data. At Ordinis, he is particularly interested in the intersection of AI research and real-world systems — where ideas move beyond theoretical formulations to measurable, deployable, useful technology.
What we believe
AI is heading toward general capability, and the enterprises that win the next decade will be the ones that adopt it fastest and most completely. But an enterprise can only move as fast as it can trust — and trust, at scale, has to be built into infrastructure. The closer models get to general intelligence, the more the enterprise needs one place where its own intent, not the model's, decides what happens. That place is the control layer. It is what lets an enterprise accelerate with confidence.
Pillars
Freedom to build.
Any model, any agent, any provider — shipped fast, because the guardrails live in the infrastructure, not in a review queue. Innovation should never wait for a committee.
Control that scales with capability.
Every action AI takes is bounded by the enterprise's intent and enforced where the request runs. The more capable the model, the more this matters — and the further it can safely be trusted to go.
Trust you can prove.
Every decision leaves evidence an enterprise can stand behind — to its board, its regulator, and the people it serves. Proof is what lets AI move into the work that matters most.
Values
Never the bottleneck.
We measure ourselves by how much faster our customers ship, not by how much we slow them down.
Enforced, not documented.
A control that isn't enforced isn't a control.
Neutral by design.
No model, no cloud, and no regulator owns us — which is the only way to be trusted with all of them.
Inside your walls.
Your data, your perimeter, your rules. Our business never depends on seeing your prompts.
To make AI something every enterprise in the world can build on without fear — so it can grow as fast as its ambition.