How I work: from first conversation to a team that runs it
The problems I'm usually brought in to solve, how I approach them, the ways we can work together, and what happens after you get in touch.
The problem you have, and how I'd solve it.
Each engagement follows the same disciplined method. It adapts to the problem, but you always know what happens next, what it costs and what you get.
Discover
Stakeholder interviews, system and data audit, constraints.
Model
Map the domain into an ontology everyone agrees on.
Architect
Target architecture, decision records, phased roadmap.
Build
A thin production slice first, then expand with evidence.
Scale
Load, cost, reliability and security hardened to target.
Transfer
Runbooks, hiring and mentoring until the team owns it.
Our AI pilot won't make it to production
Demos impress, but answers are unreliable, costs are unknown and nobody will sign off.
Usually the model isn't the problem. Retrieval is ungrounded, there's no evaluation set, and no one owns quality.
- Define the decisions the AI supports and the cost of a wrong answer
- Ground retrieval in a domain ontology and knowledge graph (GraphRAG)
- Build an evaluation harness and tracing before adding features
- Orchestrate agents with LangGraph: explicit state, tools via MCP, human checkpoints
- Ship one workflow to production behind guardrails, then expand
Our data is everywhere and means different things
Every team has its own definition of a customer, a patient or revenue. Reports disagree. AI makes it worse.
It's a semantic problem, not a storage problem. Without a shared model, more pipelines just copy the confusion.
- Run domain workshops to capture core concepts and relationships
- Formalise an ontology and controlled vocabularies, versioned like code
- Map sources onto it: entity resolution and master data
- Expose a semantic layer to BI, APIs and AI agents
- Put data contracts and quality checks at the boundaries
Our platform buckles as we grow
Latency spikes at peak, batch jobs overrun, incidents are frequent and every release feels risky.
Synchronous coupling and shared databases turn growth into contention. The architecture is fighting the load.
- Instrument first: find the real bottlenecks with tracing and load tests
- Set SLOs and latency budgets for the critical paths
- Move hot paths to an event-driven core with idempotent consumers
- Redesign data access: indexes, aggregations, read models, caching
- Automate deploys and rollbacks on Kubernetes with GitOps
Our healthcare data won't interoperate
EHRs, labs and claims arrive in different formats and codes. Every integration is a bespoke project.
Point-to-point integration doesn't scale. You need a standard exchange format and a standard analytical model.
- Inventory sources, code systems and PHI flows
- Standardise exchange on HL7 FHIR; normalise to SNOMED, LOINC and RxNorm
- Land analytics in OMOP CDM for reproducible research with OHDSI tools
- Build in de-identification, consent, encryption and audit trails
- Layer clinical AI only on governed, provenance-tracked data
Cloud costs and complexity are out of control
Environments were hand-built, nobody knows what's running, and the bill grows faster than revenue.
Without infrastructure as code and cost visibility, every environment drifts and waste is invisible.
- Map every resource to an owner, a service and a cost
- Codify infrastructure in Terraform modules across AWS, Azure and GCP
- Consolidate workloads onto a shared Kubernetes platform
- Right-size, autoscale and route AI calls to the cheapest capable model
- Report unit economics per transaction, tenant and token
We don't have senior technical leadership
The founders or executives are making technical bets without a trusted, experienced voice in the room.
You don't need a full-time CTO yet. You need CTO-grade judgement on the decisions that are expensive to reverse.
- Assess the team, architecture, roadmap and risk in the first month
- Set a technical strategy tied to business milestones
- Establish architecture governance: ADRs, reviews, standards
- Hire and mentor senior engineers; design team topology
- Represent technology to the board, investors and partners
How I work with leadership teams.
Senior technical judgement is mostly about what not to build. These principles shape every engagement.
Model the domain first
A clear ontology of the business outlives any framework. Get the nouns and relationships right and the systems, analytics and AI fall into place.
Architecture is a business decision
Every technical choice is tied to cost, risk, speed or capability, and explained in those terms to the people who fund it.
Prove it in production
Thin vertical slices, shipped early, measured honestly. Demos persuade; production systems with evaluations and telemetry decide.
Own your stack
Avoid lock-in where it matters. Portable infrastructure, open standards and data you control keep your options open.
Leave the team stronger
Documentation, decision records and mentorship are deliverables. Success is the team running it confidently without me.
Discretion, always
Your roadmap, data and challenges stay yours. Confidentiality is the default.
Shaped around where you are.
From a focused two-week review to an ongoing seat at the leadership table. Every engagement starts with a no-obligation conversation.
Architecture & AI readiness review
An independent assessment of your systems, data and AI plans.
- Current-state audit
- Risk & opportunity map
- Prioritised roadmap
Fractional CTO
Executive technical leadership, part-time, for as long as you need it.
- Strategy & board reporting
- Hiring & team building
- Architecture ownership
Build & hand-off
Hands-on delivery of a critical platform, AI system or knowledge graph.
- Design through production
- Embedded with your team
- Documented transition
Advisory
A senior sounding board for founders, CTOs and investors.
- Regular office hours
- Technical due diligence
- Decision reviews
From first note to kick-off, without the runaround.
No sales team, no gatekeepers. You talk to me directly from the first message, and you'll know within a week whether and how we should work together.
Book or write
Pick a slot on my calendar, or analyse your challenge first and bring the result.
Intro call
30 minutes, free. Your goals, constraints and whether I'm the right fit. Pick a time →
Proposal
A short written plan: scope, approach, timeline and cost. No 40-page decks.
Kick-off
Discovery starts right away, with a clear first milestone in the calendar.
Looking for an advisor? I'm up for it.
If you're building in AI, data, fintech, SaaS or healthcare and want an experienced technical voice on your side, as a board or startup advisor, a technical advisor to investors, or a sounding board for your CTO, let's talk.
Common questions.
If yours isn't here, ask me directly. I'm happy to answer before any engagement.
What does a fractional CTO do?
A fractional CTO provides executive-level technology leadership part-time. I own technical strategy, architecture decisions, hiring and board-level communication, without the cost of a full-time hire, and hand over cleanly when you're ready for one.
Can you help us get our AI or LLM pilot into production?
Yes. I ground retrieval in a domain ontology and knowledge graph (GraphRAG), orchestrate agents with LangChain and LangGraph, and add evaluation, tracing and guardrails so quality and cost are measurable before you scale.
Do you work with healthcare data and HIPAA?
Yes. I design HIPAA-aligned architectures and work with HL7 FHIR, OMOP CDM and OHDSI, and clinical vocabularies like SNOMED CT, LOINC and RxNorm, with de-identification, consent and audit trails built in.
What is ontology engineering and why does it matter for AI?
An ontology is a formal model of the concepts in your business and how they relate. It gives every system, report and AI agent the same meaning, which makes data reconcile and makes AI answers accurate, explainable and permission-aware.
How do engagements start?
With a free 30-minute introductory call, which you can book directly from my calendar. Most clients then begin with a 2 to 4 week architecture and AI readiness review that produces a prioritised roadmap, then continue as fractional CTO, a build-and-hand-off project or advisory.
Do you work remotely with international teams?
Yes. I'm based in India, the United States and the UAE and work with teams worldwide, remotely and with on-site sessions when they add value.
Let's talk about what you're building.
The fastest route is a free 30-minute call. Prefer to write? Send a message and I'll reply personally within two business days.