Sairam KrishnanI lead engineering teams that build AI and data platforms at Apple. There I led an eight-engineer team across Seattle, London, and partner sites in Europe and Latin America, and led architecture of the media knowledge graph for over 100 million users. At Amazon, I designed the Alexa Engine data platform that saved $40M a year. I keynoted the Philadelphia Eagles’ 12th annual Eagles Care Summit for more than 300 community and business leaders and published, as first author, the AI ROI framework behind the AI ROI Review.
One hour on the design, scaling problem, data model, or AI build your team is stuck on. Bring the doc or the diagram; leave with a recommendation.
A written review of one design doc before your team commits to building it: data models, service boundaries, failure modes, and scale. Every risk is ranked with a proposed fix, and the week closes with a call to walk your engineers through it.
Standing access for your engineering leaders: design reviews before work starts, office hours for hard distributed-systems and AI problems, and mentoring for senior engineers growing into staff roles.
One session from the catalog below, fitted to your firm, for your executives or engineers. I taught AI strategy to C-suite leaders at HP, Morgan Lewis, PBS, and the Philadelphia Eagles.
A keynote on any topic in the catalog below, for a conference, summit, or company offsite.
A review of a whole system, not one doc: every service boundary, data contract, and dependency, and the bottlenecks, failure loops, and batch jobs that should be streams. Ends with a ranked remediation plan and a working session with your senior engineers.
I run every AI initiative on your list through the four-layer framework from my SSRN paper: a SCALE strategic readiness assessment of your firm, a value driver tree that traces each initiative to return on invested capital, a discount rate adjusted for AI-specific risks such as vendor and platform dependency and organizational friction, and stage gates that release each tranche of funding only on evidence. You get a decision scorecard that tells your executives which initiatives to fund.
Two sessions from the catalog below, built as a sequence, like the two-part course I designed and taught at HP.
A test suite that shows whether a prompt, model, or pipeline change made your AI better or worse: deterministic checks first, a golden set of real queries with expected answers, and model-graded judges where code cannot check. It runs on every code change, so no change ships without passing.
“Sairam has a deep commitment to his work; if there is something that needs to be completed, he acts on it immediately. As such, over time, I and others in our team rely on him for this response.”
“That rapid response to challenges is combined with a relatively rare combination of both solid technical ability and a sense of ownership over the issues he addresses. He learns the context surrounding a problem rapidly, he can accelerate into new domains quickly and collaborate well with other teams when a solution requires input from various areas of expertise.”
“In team settings, Sai naturally takes on complex, high-stakes challenges that require a sophisticated blend of analytics, operations, decision-making, and executive communication. He not only develops sharp, well-structured solutions, but also brings them to life as a compelling storyteller, equally effective with colleagues, senior stakeholders, and board-level audiences.”
How large language models work and where they fail, from bias to hallucination; choosing among ChatGPT, Gemini, Claude, Copilot, Perplexity, and NotebookLM; the H4W and COSTAR prompt frameworks; and AI across the software development lifecycle.
Retrieval-augmented generation, vector databases, and knowledge graphs; custom GPTs; the difference between AI workflows and agents, and a checklist for when an agent is worth building; and AI governance.
Why adoption fails, diagnosed with the innovation adoption curve, Deloitte Business Chemistry, and the MAPPR model, then a live roleplay turning resisters into adopters with the CLEAR framework and a champion’s toolkit for the firm.
What generative AI is and isn’t; privacy and governance frameworks as the first step; and a path to AI ROI that starts from the pain points taking people away from the mission.
The keynote I delivered to more than 300 community and business leaders at the 12th annual Eagles Care Summit.
How a knowledge graph enriches questions before they reach a model and checks the answer afterward, then when to go small with language models, with a live setup of a local model in five minutes.
More than 80 percent of AI projects fail to deliver measurable returns. A four-layer method for deciding which initiatives to fund, validated against eight deployments including Salesforce Agentforce, Spotify, and JPMorgan COiN.
Managing software engineering when AI makes code cheap: shrink the unit of review, tier scrutiny by consequence, measure verified outcomes rather than raw output, and rebuild the apprenticeship on purpose.