Agents reach production
- Governed agents replace pilots
- AI run-cost becomes a CFO topic
- Regulation moves from guidance to enforcement
I'm Vamsi Talasila. For more than two decades I've built the part of a system nobody sees in the demo: the part that decides whether a platform holds at a hundred million users, and whether AI is safe to put in front of a regulator. Today I help boards, CEOs and CTOs get that part right, and carry the systems and people they already have into AI, quantum-safe and next-generation computing.
Technology behind India's first gaming-company IPO, 2021
Where the work has run
From my field notes on enterprise AI that survives production, scale and audit.
Everyone will soon have strong models. The companies that win will have the best architecture around them. Security isn't a firewall you add. Compliance isn't paperwork after launch. Cost control isn't just a cheaper model. These are design decisions. Get them right and cost, accuracy, scale and control improve together. Anyone can pick a model. Architecture decides when, where and how it's allowed to think.Vamsi Talasila
Whatever the industry, the same six decisions separate AI that pays from AI that stalls. The top is what a CEO buys; the middle is what I design; the base is what most advisers skip.
Model cascades, caching and FinOps, so AI spend grows slower than usage.
65–75% lower AI run-costLatency and quality designed in, with evaluations that gate every release.
Evals before every launchEU AI Act, ISO/IEC 42001 and DPDP by design, human review where it matters, full audit.
Governance in the kernelOne governed gateway for every model call. Swap models by configuration, not code.
257 agents · 23 providersEvent-driven platforms built to hold the next tenfold jump without heroics.
100M+ users · ~6B events a dayPost-quantum cryptography now, and an architecture ready for quantum and bio-computing.
Quantum-safe by designEvery engagement is judged on business (cost, growth, risk), product (what people will actually adopt) and technology (architecture, scale, governance). Most advisers bring one lens. I've owned all three as a CTO, a product builder and a founder.
An AI roadmap your board can approve and your CFO can measure.
How it works →Agents that work in production, not just in the pilot.
How it works →Built in, so you can answer the regulator's questions.
How it works →Faster answers at a fraction of the bill.
How it works →Connect the systems you have to the AI you need, ready for the next 10x.
How it works →Quantum-safe today, ready for quantum and bio-computing tomorrow.
How it works →Zero to one, with a CTO who has done it four times.
How it works →What the technology is really worth, before you pay for it.
How it works →When data can't leave the country and answers can't be wrong.
How it works →Your goals, your constraints, and the P&L line the board will judge.
Architecture, data, cost and compliance posture, measured rather than assumed.
The target design, the trade-offs, and what not to build.
Ship one high-value workflow, with evals and a cost baseline.
Roll out, train the team, and leave them owning it.
Three of the six case studies. Each one covers the hard part, what I did personally, the result, and what I'd do differently.
Group CTO across 11 companies; the technology story behind India's first gaming-company IPO.
257governed agents · 23 providersOne gateway for every model call. Run-cost down 65–75%, governance in the kernel.
6jurisdictions · data never leaves164 specialist agents per jurisdiction, live in India. Every answer checked for citation and faithfulness.
I'm not predicting this future from the outside. The bottom of each column is something I've already built or am building now.
Separating the two is what makes "any industry" believable.
I take on a small number of board, advisory and executive mandates each year.