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y: systems built · x: systems understoodhuman → software → intelligent systems

Don't read my resume.
Talk to it.

Fig. 1 · Priyanshu Yogi, Jaipur, India. Notes.

Economics and political science, then self-taught software engineering, enterprise AI products, a lot of time with customers, and now agents, RAG and voice. Ask my AI about any of it.

Seven turning points, not seven job titles. Hover or click any point on the curve and the note updates. The ringed points are the first and the last: the dip between them is real, building fell while he learned the commercial side, and the last point is the correction.

Why am I relevant to your role?

Table 1Two educations, one method.

Formal training in how human systems behave; self-directed training in how to build technical ones. The first is why the second was learnable.

Human systems → software systems → intelligent systems
FormalSelf-directed
Programme
B.A. Humanities (Economics & Political Science)
University of Delhi, 2019 – 2022
Full Stack Web Development, Job Bootcamp
Coding Ninjas, 2022 – 2023
Ongoing
Self-directed, through projects and professional work, 2023 – present
What it trainedAnalytical thinking · Incentives · Institutions · Human systems · Why people and organizations behave the way they doJavaScript / TypeScript · Frontend · Backend · Databases · Cloud (AWS, Azure, Terraform) · System design · Computer vision solutioning · Edge deployments · LLMs · Agents · RAG · Voice systems
Carried forwardReading incentives before reading requirements. Treating an organization as a system with its own equilibrium.Learning a stack by shipping in it. Comfort with being the least credentialed person in the room and the most prepared.
  1. Economics + Political Science
  2. Curiosity about technology
  3. Learning to code
  4. Full-stack development
  5. Building real software
  6. Applied AI / Computer Vision
  7. Enterprise AI systems
  8. Agents + RAG + Voice
  1. aDates and programmes from the resume. No CS degree; nothing here is a certification list.
  2. bThe message is not “studied something unrelated and became an engineer.” It is “learned to think about human systems first, then taught himself to build technical ones.”

Fig. 2Experience that overlaps rather than stacks.

Six regions, one person. Click a region for its evidence, or ask the AI and watch the relevant ones hatch.

BuilderAgentsCustomerProductBusinessHumanities

Builder

Software engineering · Frontend · Backend · Integrations · System architecture · AI products

  1. 1Built frontend dashboards, backend APIs and Computer Vision pipeline integrations on HawkVision's core engineering team; used Terraform for deployment pipelines. Software Developer, HawkVision AI
  2. 2Built Instipe, a NestJS/GraphQL payment system for educational institutions processing 50+ lakh INR/month; cut a GraphQL query from 10+ seconds to under 100ms. SDE Intern, Edviron
  3. 3Led a 5-engineer team shipping Hazo (NestJS, React Native, AWS) and WorkOS. Entrepreneur in Residence, Project Dark Horse
  4. 4readTrail: a Manifest V3 Chrome extension with a canvas reading trail, validated service-worker messaging and 128 behavioral tests. readTrail

A non-linear path, in six moves.

  1. 1

    Humanities

    Studied Economics and Political Science at the University of Delhi. Developed an interest in understanding systems, incentives, institutions and why people or organizations behave the way they do.†

  2. 2

    Self-taught engineering

    Moved into technology through self-learning: a full-stack bootcamp, then projects and professional work rather than a traditional CS degree.†

  3. 3

    Building

    Started working on software products: a payment system at Edviron, then leading a small team shipping Hazo and WorkOS at Project Dark Horse.††

  4. 4

    HawkVision

    Joined early on the small product/engineering team. Built parts of the product, worked on deployments, moved closer to customers, and eventually took on solution architecture, solutioning and broader business responsibility.††

  5. 5

    AI-native products

    Now increasingly building around AI agents, RAG, voice interfaces, multimodal systems, intelligent workflows and tool-using AI.††

  6. 6

    What comes next

    Building technically difficult products that solve real user problems. Roles like Forward Deployed Engineer, AI Engineer, AI Solutions Engineer, Product Engineer, Customer Engineer and technical product roles.

  7. Roles he is looking at: Forward Deployed Engineer, AI Engineer, AI Solutions Engineer, Applied AI Engineer, Product Engineer, Customer Engineer, Solutions Architect, Technical Product roles.

Fig. 3Four roles, three companies, one direction.

Backend under real load, then 0→1 product leadership, then inside an enterprise computer-vision company from engineering to global solutions. HawkVision is the deepest chapter, not the only one.

Click any row to open what that role actually involved.

20192020202120222023202420252026
Jun 2025 – Aug 2026 · Jaipur, India

Global Solutions Head & Head of India Sales

HawkVision AI

Enterprise Computer Vision company. Moved from the core engineering team into owning technical solutioning end-to-end, then into India sales and global solutions.

  1. 1Led end-to-end technical solutioning across 50+ enterprise Computer Vision opportunities in 7 countries (Europe, Middle East, APAC), spanning manufacturing, construction, infrastructure, highways, warehousing, logistics and airports.
  2. 2Owned discovery, AI feasibility assessment, camera placement strategy, system architecture and deployment planning for 12 concurrent enterprise PoCs, authoring every Solution Architecture Specification and running customer workshops and site visits to turn field feedback into roadmap decisions.
  3. 3Diagnosed and resolved a failed on-site automation deployment (Raspberry Pi + relay kit) by identifying incorrect circuit diagrams and redrawing the correct wiring on customer premises, restoring functionality without a return visit.
  4. 4Grew one live deployment from a 2-camera demo to 10 cameras in production with a 30-camera expansion proposed, and carried a 10-camera highway-safety PoC across a 165km corridor to sign-off, now scaling toward 100+ camera coverage.
  5. 5Authored 10+ RFI/RFP responses across automotive, fabrication, construction, oil & gas and energy, each scoped at 1,000+ camera enterprise deployments.
  6. 6Built the India sales pipeline from scratch, defined India pricing strategy and commercial proposals (typical deal ₹10–15L; largest active opportunity scaling from 20 to 200+ cameras), and supported the CEO on international business development and partnerships.
  7. 7Designed and built an internal AI-native solutioning operating system (Claude + Obsidian) that automated company, process and operations research, cutting proposal turnaround from a multi-day manual research cycle to a 5-10 minute first draft, and Solution Architecture Spec first drafts to under 5 minutes, with customer and market intelligence auto-routed into a shared knowledge vault.
What it taught him

How enterprise AI actually gets bought and deployed: feasibility before features, site reality over slide decks, and the commercial constraints that decide whether a technically sound solution ships.

Technologies
Computer Vision solutioningEdge deploymentsRaspberry PiSolution Architecture SpecsRFP/RFIClaudeObsidian
  1. aAll figures (camera counts, deal sizes, PoC counts, query timings) are as stated on the resume; none are rounded up here.

Table 2Not all projects are equal, so they aren't shown equally.

Featured products get full case studies. Technical projects are shipped systems from professional work. Experiments are labelled as what they are.

readTrail reading guide highlighting the current passage on an arXiv article

readTrail

Working MVP, built in public

A privacy-first Chrome reading companion that remembers exactly where you stopped reading.

Explore readTrail GitHub
0
ask about any pointsystems understood

Priyanshu OS / AI Voice Portfolio

In progress: you are using it

A portfolio you talk to. An AI agent grounded in structured profile data that can answer, navigate and reason about fit.

Explore Priyanshu
1
Technical projectWhat it didWhereStatus
AI-native solutioning operating system
Claude · Obsidian · Research automation · Knowledge management
Claude + Obsidian system that automated company, process and operations research for enterprise CV proposals.Proposal first drafts in 5-10 minutes; Solution Architecture Spec first drafts in under 5 minutes; customer and market intelligence auto-routed into a shared knowledge vault.HawkVision AIJun 2025 – Aug 2026Shipped internally at HawkVision
WorkOS
Google Calendar API · Jira integration · Role-based access · Agentic workflows
An agentic enterprise operating system unifying role-based access, onboarding and cross-tool integrations.One of two products shipped by a 5-engineer team from an ambiguous early-stage vision.Project Dark HorseJun 2024 – Mar 2025Shipped at Project Dark Horse
Hazo
NestJS · React Native · AWS
Mobile product with a NestJS backend, React Native frontend and AWS infrastructure.Led the team building it; directed roadmap and delivery cadence for 30% faster time-to-market.Project Dark HorseJun 2024 – Mar 2025Shipped at Project Dark Horse
Instipe
NestJS · GraphQL · MongoDB · AWS Lambda
NestJS/GraphQL payment system for educational institutions processing 50+ lakh INR/month.MongoDB pipeline optimisation cut query execution time up to 90%; one GraphQL query went from 10+ seconds to under 100ms with caching and AWS Lambda.EdvironNov 2023 – May 2024Shipped at Edviron
Highway-safety Computer Vision PoC
Computer Vision · Camera placement strategy · Edge deployment · Solution Architecture Spec
10-camera highway-safety PoC across a 165km corridor, carried to sign-off and now scaling toward 100+ cameras.Owned discovery, feasibility, camera placement, architecture and deployment planning.HawkVision AIJun 2025 – Aug 2026PoC signed off, scaling
  1. e1Agentic RAG experiments [exploring] LangGraph, vector databases, memory, tools and structured workflows.
  2. e2Voice and audio experiments [exploring] Transcription, synthesis, conversation and signal analysis. The voice layer of this portfolio is the first concrete piece.
  3. e3EdgeVision Lite [planned] A real-time computer-vision system: video streams, detection, tracking, rules, events, APIs and a local dashboard. Bringing the CV solutioning experience back into hands-on code.
  4. e4ANPR video pipeline [planned] Video processing with timestamped detections and structured exports.

Fig. 4A background that connects, not a list of keywords.

Drag nodes. Click one to see what it links to. Economics sits three hops from a highway camera deployment for a reason.

Experience

HawkVision AI

Open Global Solutions Head & Head of India Sales, HawkVision AI
Connected to

Table 3Why am I relevant to your role?

Paste a job description. The agent reads it against Priyanshu's structured experience and returns strong matches, transferable experience and the gaps. Every match cites its source row. A fit report with no gaps is not trustworthy, so gaps are the point.

0 characters · not stored
The ledger has five rows: strong matches, transferable experience, gaps, relevant projects, and questions worth asking him. Nothing is stored.

Fig. 5Things I keep going down rabbit holes on.

Not a skills list. Solid points are things he works on; hollow points are things he is curious about. Positions are a self-portrait, not data. Drag them if you disagree.

Hover or tab to any point to read the note behind it.

builds withreads about