LLM Integration
Model selection and integration across Azure OpenAI, AWS Bedrock, Google Vertex AI, or self-hosted — matched to latency, cost, and data-residency constraints.
Home | Consulting | Engineering | GENAI APPLICATION DEVELOPMENT
From LLM prototype to production-grade application.
Photon builds enterprise generative AI applications end to end — LLM integration, prompt and context engineering, retrieval architectures, and the production hardening that separates a demo from a system a regulated enterprise can run.
Core Capabilities
Retrieval & Context Architecture
Grounding GenAI applications in enterprise knowledge and data without leaking what shouldn't leave the institution.
Prototype-to-Production Hardening
Evaluation, guardrails, and observability that turn a working demo into a governed production system.
Delivered by Lean Lab Pods
Small, co-located squads spanning Product, Architecture, Dev, and QA — the same delivery model used across all of Engineering.
Three Adoption Patterns
Every GenAI engagement maps to one (or more) of three patterns.
Pattern 1
Run a More Efficient Engineering Org
AI-based automated testing, auto-generated code, legacy modernization — the SDLC Agent is Photon's own proof point here.
Pattern 2
Run a Better Business
Gen-AI driven automated customer service, AI Nerve Centers for agent and customer experience, AI-driven contract management.
Pattern 3
Increase Revenue & Engagement
Gen-AI based valuation models, improved buyer experiences through AI and ML, personalized recommendation engines.
The GenAI Logical Structure
Four layers, from raw enterprise data to the interface a person actually uses.
Level 4
User Experience Layer
- Integrated digital experience
- IVR/contact center
- Chatbots/copilots
- Custom UI
- Knowledge base
Level 3
Distribution Layer
- Integrations & microservices
- Connecting models to existing enterprise systems (Salesforce, SAP, GitHub)
- Retrieval-augmented generation
Level 2
LLM / Platform Layer
- Google Gemini
- OpenAI GPT/Azure
- Anthropic
- Cohere
- Domain-specific startups (legal, contract, enterprise search)
Level 1
Data Source Layer
- Code repositories
- Document repositories
- Data lakes/warehouses
- Vector data stores
The AI Jump Start — A 4-Week Accelerator
Two workstreams, four weeks, three outcomes — how Photon de-risks a first GenAI investment.
- WEEK1
- WEEK2
- WEEK3
- WEEK4
Workstream 1
Use Case, Value Case & Roadmap
External perspective (peer and competitive benchmarking) plus internal ground-truth, synthesized into a No Regrets matrix plotting value against complexity.
Workstream 2
Activating the Sandbox
Connecting to Azure OpenAI (or GCP/AWS equivalent), building out the service, and standing up a branded, protected UI for safe experimentation.
Outputs
GenAI Strategy Blueprint
AI Sandbox Activation
Execution Roadmap
Proof points
A global wealth-management fintech platform
Two-track GenAI accelerator
Architecture assessment for a scalable AI-ready data foundation, plus GenAI adoption for engineering-workforce productivity.
sdlc agent (internal)
80–90% reduction in prep time
One super-agent orchestrating nine specialist sub-agents, turning meeting notes and transcripts into structured PRDs, epics, and test cases with one-click Jira sync.
a major u.s airline
AI Nerve Center, Airborne.
Pioneered an AI Nerve Center on Azure OpenAI for agent- and customer-facing virtual assist use cases.
50%
Productivity increase across the use case
15 min
Average processing time per task, down from hours
