AI architecture advisory
I'm Saurav Bakshi, an enterprise architect. I help technology leaders, CDAIOs and architecture teams design AI systems that hold up inside a regulated enterprise: the data foundations, the integration, and the agentic layer on top.
Advisory
I work with technology leaders, CDAIOs and enterprise architecture teams who have to make AI work inside a large, regulated organisation. The work spans three layers of the stack, from the foundations up.
Your context
The architecture
What you get
Re-architecting the core stack so it can actually run on AI — clean data contracts, a decisioning layer, and the governance to deploy models safely inside a regulated enterprise.
Loosely coupled, cloud-native foundations and clean integration patterns — so the right capabilities plug in without the usual technical debt and bottlenecks.
Designing systems around intelligent, agentic services — where decisioning, automation, and orchestration are model-driven by default.
Ways to engage
You have an AI initiative in flight, or a target architecture on paper.
A focused assessment of what you are building: the risks, the gaps, and a prioritised set of decisions your team can act on.
You are building, and want an architect beside your team.
I work alongside your architects and engineers through the build, shaping the target architecture, the patterns and the decision records.
You own the AI agenda and need a standing sounding board.
Ongoing access for CDAIOs and CTOs: vendor and platform choices, governance, board papers, and the calls that come up between.
Sitting with an AI decision that needs an architect's eye?
Book an intro callProof of practice
I build working AI systems to test the patterns I recommend. Each one proves an architecture idea that carries over to the enterprise: privacy, provenance, auditable AI judgment and contract-first delivery.
How I think
Enterprise AI is short on information written for you — which leaves technology executives to turn it into a decision, alone.
For the whole of last year, while I was exploring enterprise AI, each attempt left me inundated with engineering frameworks, architectures and tools.
Very little of it helped CDAIOs and technology leaders make decisions about the use and governance of AI.
AI engineering matters, but enterprise strategy — and the decisions around it — travels well beyond engineering.
The information sits in silos. It is genuinely useful, but pulling it together takes time, and time is what decision-makers have least of.
I call it deciding in fog. You can move, but you cannot see far enough to be sure.
This is the situation most of us face. Finding clarity in it is what I bring to every engagement: gathering what matters, cutting what doesn't, and turning it into a decision your team can act on.
How I work
I strip a crowded field down to what truly touches your architecture and strategy, in the language CDAIOs and technology leaders actually speak.
Most AI writing explains what something is. The question that matters is what it changes: when a choice is forced, what tradeoff appears, and what breaks if you get it wrong.
I turn a moving, uncertain field into something you can hold, explain to your board, and act on.
Latest writing
Latest essayWho trains your next architect?Work with me
Tell me about the AI decision on your desk. A few lines is plenty: what you are building, where it is stuck, and when you need to decide. I'll reply to set up a 30-minute call.
Email me about a projectNot ready to talk yet?
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