Your AI works in the demo. The real question is whether it works in production - under security review, at real volumes, and at a cost per transaction your CFO will sign. We build the architecture foundation that gets AI initiatives there, grounded in TOGAF discipline and the Well-Architected Frameworks of OCI, AWS, Azure and Google Cloud.
Most AI initiatives start well. A proof of concept impresses the leadership team, a budget is approved, and a go-live date is set. Then production arrives. The security team asks where the data goes. The integration team finds the AI component was never designed to talk to the ERP, the CRM or the core platform. Finance asks what each AI transaction will cost at ten times today's volume. Nobody has the answers, because nobody designed for those questions.
This is an architecture gap, not a technology gap. It shows up as pilots that never graduate, three teams building three incompatible AI stacks, cloud bills that grow faster than the benefit, and business cases that report hours saved but never move the EBITDA line.
Discuss this serviceEvery engagement begins with one question worth answering well: is this AI initiative ready for production, and if not, what stands in the way? From there, you decide how far to take it. Each step is scoped, time-bound and follows our one-month-minimum, no lock-in model.
Our diagnostic is independent. It can conclude that an initiative should pause, be redesigned, or go to a different delivery partner. Where we recommend implementation partners, any commercial relationship is disclosed to you in writing. An architecture review that always says "build" is not a review.
Enterprise architecture and cloud architecture are usually practised by different people who rarely meet. The EA team writes principles that engineers never read. The cloud team builds landing zones that business owners never understand. AI initiatives fall into the gap between them.
We work both layers as one. Sethunath is a TOGAF 10 certified Enterprise Architecture Practitioner, and every engagement follows the Architecture Development Method: architecture vision and business case first (Phase A), then business, data, application and technology architecture (Phases B to D), then migration planning, implementation governance and change management (Phases E to H). We keep it lightweight. The ceremony is optional. The discipline is not.
At the platform layer, every design is reviewed against the provider's own framework, so your architecture holds up to the questions your cloud provider, your auditor and your enterprise customers will ask. Oracle Cloud Certified Architect Professional and multicloud credentials sit behind that review, along with years of running OCI and AWS workloads for airline, pharma and logistics customers.
Most AI business cases stop at effort: hours saved, FTEs freed, tickets deflected. Operations heads like those numbers. CFOs discount them, because freed capacity only reaches EBITDA when it becomes a cost no longer paid, a hire no longer needed, or revenue now captured. EBITDA moves in two ways only: revenue grows without headcount growing with it, or operating cost falls in COGS or SG&A.
We build the case the way a Deal P&L is built, from the unit up. What does one AI-assisted transaction cost today, including inference, tokens, orchestration, guardrails and the human review that stays in the loop? What does the manual version cost? How do both curves move at three times and ten times the volume? The answer is an EBITDA bridge: baseline, AI run-cost, realisable savings, revenue effects and payback, in a form finance can audit.
We show every case three ways: EBITDA, EBIT and cash. EBITDA shows operating efficiency. EBIT shows what the investment really costs once amortisation is counted. Cash shows when the money comes back. A case that only looks good on one of the three is a case to question.
The same baseline becomes the anchor for outcome-based commercial models. When the number is agreed before the build starts, you can pay for what changes rather than for who is staffed. This work draws directly on our ROI & TCO practice.
An AI SaaS product is quick to start and easy to stop. Every seat and every surcharge is operating expense, from the first month to the last. A custom agentic build asks for engineering investment up front, then runs on tokens, infrastructure and maintenance. The per-unit cost falls away sharply as volume grows. Which one wins depends on how much volume you have, how fast it grows, and how proprietary the workflow is.
We model both options over three years and find the crossover: the Year 1 volume above which the build beats the licence. One trap needs naming. Capitalising development cost keeps it out of EBITDA, so a build can look better on EBITDA than it really is. We calculate the crossover twice, once on EBITDA and once including the capital outlay on EBIT and cash, and we make the recommendation on the stricter test. Whether the cost can be capitalised at all is for your auditor to confirm under IAS 38 or Ind AS 38.
On a time-and-materials contract, every hour AI saves is an hour you no longer bill. Take an illustrative engagement of 1,000 hours billed at $100 and delivered at $60: revenue of $100,000 and a gross margin of 40%. Halve the hours with agentic delivery and revenue halves too. The margin percentage stays where it was, and the absolute profit falls by half.
Now price the same scope as a fixed-price or outcome-based contract at $100,000. Delivery becomes 500 hours of human effort at $30,000 plus $3,000 of agent compute. Revenue holds. Gross margin rises to 67%. The efficiency stays with you instead of flowing to the client. Getting there takes more than a pricing change. Delivery has to be engineered, measured and contracted differently, and that is architecture work.
This service fits mid-market ISVs embedding AI into products sold to enterprise customers, IT services firms moving from time-and-materials to fixed-price agentic delivery, and enterprises in banking, healthcare, aviation, logistics, pharma and hospitality running AI on mission-critical processes. It is most valuable at one of three moments: before a pilot is funded for production, when several AI initiatives are running without a common foundation, or when an enterprise customer's security and architecture review is holding up a deal.
It also connects the rest of our work. Reliability links to Operational Maturity. Security links to Security Compliance. Profitability links to ROI & TCO. The value story links to SaaS Value Selling. Enterprise architecture is the thread through all four, which is why many clients start here.
Designed for failure from day one: fallbacks when a model is slow or wrong, resilience patterns, and operations your team can actually run.
Unit economics understood before the volume arrives, so growth improves margin instead of eroding it.
Data residency, access control and AI-specific risks such as prompt injection handled in the architecture, not in a remediation plan.
An EBITDA bridge finance can audit, and kill criteria that stop low-value initiatives early.
Every new AI initiative reviewed against the same principles before it is funded, not after it fails.
Model and platform choices kept open, so a price change or a better model does not force a rebuild.
Start with a 30-minute discovery call. Sethunath will tell you where the architecture risk sits, and whether a diagnostic is worth doing.