Model Development
Custom model development and fine-tuning where an off-the-shelf LLM isn't the right fit.
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Most enterprises are stuck at L2–L3. The value is at L7.
Model development, MLOps, and production deployment — AI as the core engineering discipline, feeding directly into the agentic systems built on PHTN.ai. Part of Photon's broader AI & Machine Learning pillar: embedding intelligence to accomplish more with less across every operational workstream.
Core Capabilities
MLOps & Production Deployment
Training pipelines, versioning, and monitoring that keep models reliable once they're in production.
Model Evaluation & Governance
Evaluation frameworks that feed the same governance layer PHTN.ai enforces at runtime.
Legacy ML Modernization
Bringing existing predictive and classification models into an agentic, governed architecture.
The Enterprise AI Maturity Model
Seven levels, from hardcoded rules to full orchestration — most organizations plateau well before the value actually lives.
L1
Conditional Logic — predefined if/then, no AI
Human writes every rule
L2
Prompt-based Tasks — RAG, summarize, generate on demand
Human writes every rule
L3
Context-Defined Tasks — multi-step via tools & APIs
Human initiates every interaction
L4
Plan & Execute — agent devises plan, executes
Human triggers, agent executes
L5
Self-Triggered — agent monitors, initiates workflows
Human approves exceptions only
L6
Multiple Isolated Agents — function-specific objectives
Human sets strategy
L7
Super Agent Orchestration — agents coordinate across functions
Human sets strategy, agents orchestrate
Most enterprise ML investment today sits at L2–L3 (prompt-based and context-defined tasks).
Photon’s Engineering practice takes you from isolated use cases to L7 Orchestration.
Choosing the right model isn’t one-size-fits-all
Seven levels, from hardcoded rules to full orchestration — most organizations plateau well before the value actually lives.
DeepSeek R1
- Reasoning depth
High — thinks through complex logic
- Speed
Slower, in-depth
- Best for
- Complex projects with missing or scattered references
Microsoft Phi-4
- Reasoning depth
High — balanced with speed
- Speed
Fast and efficient
- Best for
- General-purpose tasks balancing quality and speed
Google Gemma 2
- Reasoning depth
Lower — less reconstruction logic
- Speed
Very Fast
- Best for
- Simple, self-contained tasks where speed matters
Gemini 2 Pro
(Flash Thinking)
- Reasoning depth
Very High — builds full logic
- Speed
Extremely fast despite complexity
- Best for
- High-quality, production-level output when cloud usage is allowed
Evidence
A global athletic footwear and apparel brand
Smarter Search, Sharper Assortment.
Replaced a proprietary recommendation engine with a trained ML stack, integrated into search to influence results and power merchandiser analytics on product assortment.
A major U.S. consumer and commercial bank
Real-Time Data at Banking Scale.
Lambda Architecture pattern for real-time big data — batch layer for the immutable master dataset, speed layer for real-time access, serving layer unifying both for downstream consumption.
