Engineering is the technical delivery practice that turns AI strategy into running systems. With 8,500+ engineers embedded directly in client environments, Photon builds and deploys agentic AI programs using the same Lean Lab delivery model that has cut time-to-market by up to 3x on traditional software — small, co-located, fully-empowered pods, now building and operating agentic systems on PHTN.ai.
We engineer intelligent agents, generative AI applications, and machine learning solutions that automate complex workflows and bring enterprise AI into production.
Multi-agent orchestration built on the Universal Agent Harness™ — the same architecture behind PHTN.ai.
LLM integration and enterprise generative AI products, prototype to production.
Model development, MLOps, and production deployment — AI as the core engineering discipline.
We bring AI into enterprise operations through intelligent agents, generative applications, and machine learning—built to automate workflows, improve decisions, and scale.
Cloud architecture and migration across Azure, AWS, and Google Cloud — the foundation every deployment runs on.
The omnichannel commerce backbone behind Photon's retail and CPG engagements.
The data foundation every agent and analytics program depends on.
How we Deliver
Photon's engineering delivery model puts small, fully-empowered, co-located pods against a single feature or component — Product, PM, CX, Architecture, Dev, and QA in one room, avoiding the handoffs that slow larger programs down. Squads scale from a 4-person "Mini" pod to a 14-person "Extra Large" pod depending on scope, with built-in quality and continuous-delivery infrastructure that lets teams regress and release code multiple times a day.
Photon's own internal SDLC Agent is a working example of the agentic systems this practice builds: one super-agent orchestrating nine sub-agents that turn raw inputs — meeting notes, transcripts, emails, decks — directly into structured PRDs, epics, user stories, acceptance criteria, and test cases, with a one-click sync to Jira and auto-generated UI prompts for rapid prototyping.
80-90%
Reduction in requirements-to-delivery prep time verses manual process
Weeks > Minutes
PRD, story, and test-case creation time, before vs. after
9
Specialist sub-agents ⎯ structuring, decomposition, Gherkin, QA governance, and more
1-Click
Jira sync for epics, user stories, and test cases
How We Decide
Time to market
Meaningful reduction versus current release schedules for a new feature
Defects in production
Escape defect rate versus historical production releases
Productivity / Throughput
Story points and estimation models measured against a pre-engagement baseline
Employee engagement
Before-and-after survey capturing team morale and satisfaction
Business objectives
Defined, projected business impact — measured whether or not it is met
A delivery model built around small, co-located, fully-empowered squads (4 to 14 people) spanning Product, Architecture, Dev, CX, and QA — designed to eliminate the handoffs and dependencies that slow larger programs, with built-in quality and continuous-delivery infrastructure.
