AI ADOPTION ADVISOR & CORPORATE TRAINER · CLAUDE SPECIALIST
Most teams I work with have already been told to use AI. The mandate came down, a few people are pasting prompts into chat tools, and few can point to what changed. I close that gap through hands-on training, a clear adoption roadmap, and when it’s the right call, the systems behind it.
AHMEDABAD · IST / EU / US OVERLAP
WHAT I HELP WITH
Five ways teams work with me. Custom programs available on request. These five are the core.
A structured assessment of where you are, where AI pays off, and where it does not. You get a prioritized plan tied to business outcomes. It covers what to adopt, in what order, and what to skip. This is the work that stops teams from buying the wrong thing expensively.
Hands-on sessions built around the tools and tasks your people actually use. Delivered as focused executive briefings, half- or full-day team workshops, or multi-session programs. Anchored by five signature programs, with the full 19-session catalog organized by audience below.
AI for Executives
EXECSWhat to bet on, what to ignore, and how to evaluate AI vendors before signing.
Claude for Power Users
POWER USERSProjects, Skills, Memory, and MCP for teams that have the tools but get little out of them.
AI Workflow Mastery
MULTI-SESSIONA multi-session program taking professionals from ad-hoc AI use to systematic workflows across AI systems, prompt architecture, and AI-assisted development. Run previously as a paid cohort, now delivered to organizations.
Claude Mastery (Private Small-Group)
PRIVATEHands-on Claude training for small professional groups. Premium-format corporate enablement: small room, real workflows, direct application.
Building Reusable Prompt Frameworks for Your Team
FRAMEWORKSMove from one-off prompts to durable, shareable frameworks the whole team can reuse.
Applied training for a specific function on running and automating its own work with Claude Cowork and connected tools. The session is built around that domain’s real workflows: a finance team automating reconciliation and reporting prep, a real estate team handling listings, documents, and client follow-up, a pharma team managing regulated documentation and review trails. People leave able to run and maintain these workflows on their own.
Ongoing guidance for teams that want a steady hand but cannot justify a full-time AI hire. It covers tool vetting, use-case review, team Q&A, and course correction as models change, structured as a monthly relationship.
A time-boxed pilot that turns one AI idea into a working proof and a clear recommendation. You pick the use case with the highest payoff or the highest uncertainty, and the engagement produces a functioning prototype, an honest read on whether it is worth taking further, and what production would actually require. The deliverable is a decision, backed by something real your team can see and test. Deeper build work, when warranted, runs as a follow-on rather than an assumed next step.
PROOF
Both halves are real and current. The evidence below.
01
Designed and ran structured technical programs at a services company, including AI enablement for delivery teams. This is organizational training: defined curricula, mixed-seniority audiences, and responsibility for changing how teams work over time.
02
Designed an end-to-end AI training curriculum spanning AI systems and research tooling, prompt architecture, and AI-assisted development, then ran it as a paid cohort. People paid for the curriculum and it stood on its own.
03
Hands-on Claude training delivered to small professional groups. This is the format premium corporate enablement usually takes: small room, real workflows, direct application.
04
A knowledge hub of prompt frameworks, original research, worked examples, and a structured prompt-engineering course that I build and run myself. The frameworks teams learn in my workshops are documented in the open there. Much of the AI training market recycles other people’s material. This is original work a buyer can read before hiring me, which shows the training rests on a live practice.
01
A multi-layer retrieval architecture over messy enterprise documents: mixed formats, scanned pages, complex tables, and domain vocabulary that off-the-shelf embeddings could not handle. Ingestion, retrieval, neural re-ranking, and generation were decoupled so each tunes independently, with a concept memory graph for multi-hop questions that single-shot RAG cannot answer. Retrieval relevance improved on internal evaluations, and the architecture held as document volume scaled into enterprise workloads.
02
Led a small team building distinct AI personas with consistent voice, memory, and behavior across thousands of concurrent conversations. The system was built for horizontal scale from the start, with a stateless API layer, vector-backed memory, and queue-based generation. It handled 10,000+ concurrent users in load testing and shipped to production with stable behavior, and the persona-prompt structure became a reusable template.
03
Production n8n and Zapier flows routing work through Claude for classification, structured-output validation, and downstream routing. MCP servers that expose internal APIs to Claude so it reads and acts inside existing systems without leaking credentials or rebuilding them. Internal Streamlit dashboards for side-by-side prompt comparison, cross-version output diffing, and structured failure logging, so prompt iteration is grounded in real data.
I have also led large-scale production delivery, including a high-traffic, compliance-critical platform built to a fixed external deadline (60% load-time improvement, launched on schedule, passed a third-party compliance review), micro-frontend storefront architecture, and order processing at 10,000+ orders per hour. It is here only because owning systems at this scale shapes how I think about reliability and operations for AI in production.
HOW I WORK
01
Before architecture or curriculum, I want to understand what “good” looks like for your team and what bad output actually costs. That conversation separates a useful engagement from an expensive wrong turn.
02
Nobody’s success is measured by which model they used. Training and strategy are framed around what your team can do after the engagement that it could not do before.
03
When building, I keep retrieval, prompting, and post-processing as independent layers with clear interfaces. When a model gets deprecated next quarter, you swap one component instead of rebuilding the system.
04
Part of adoption strategy is saying where AI is not the right answer yet. I would rather tell you that than sell you a project.
05
Logging, monitoring, cost tracking, fallback logic. The work that makes AI survivable in production rather than impressive in a demo.
THE STACK
TRACK RECORD
11+ years building software, the last several focused on AI systems with Claude at the center.
AI Tech Lead
I run AI engineering day to day for a SaaS product company.
Engineering Manager
Led 15+ engineers across AI-driven projects, and designed and ran technical and AI training programs.
Tech Lead
Product serving roughly 100,000 users.
The guidance comes from someone still building and teaching production AI now. The question I care about is whether the system and the people around it can operate, scale, and debug it at 2am, well after the demo.
HOW WE WORK TOGETHER
A predictable rhythm. Fixed-fee where it makes sense, retainer where it doesn’t. You always know what week you’re in and what comes next.
A focused conversation to understand your team’s current state, AI ambitions, and what “good” looks like. No deck. No sales pressure.
Written proposal with prioritized objectives, weekly milestones, success measures, and a flat-fee or retainer quote. Approve or push back; we iterate.
Weekly check-ins, async-first updates, working artifacts shared throughout. You see progress in real terms, not in a slide deck at the end.
Documentation, internal training session, and a 30-day implementation Q&A window. Optional fractional advisory continues monthly if you want a steady hand.
GET IN TOUCH
If your team is trying to actually use Claude, or to work out what is worth building before spending on it, let’s talk. I take on a limited number of engagements at a time.