AI Expertise.
Deep Engineering.
Technology That Works.
Photon Comptech helps organizations design, build, modernize and optimize intelligent technology systems — from AI applications and FinTech platforms to cloud infrastructure and intelligent automation.
- AI ApplicationsProducts and workflows people actually use
- LLMs · RAG · Agents · AutomationRetrieval, orchestration, tool use, evaluation
- Model Serving & InferenceThroughput, latency, batching, quantisation
- AI PlatformsPipelines, registries, experiment and cost tracking
- Kubernetes & ContainersScheduling, isolation, autoscaling, multi-tenancy
- GPU · Compute · StorageAccelerators, throughput, capacity planning
- Cloud · Private Cloud · DatacenterPublic, hybrid, on-premise, colocation
- Networking · Security · MonitoringSegmentation, identity, observability
From application to infrastructure.
Most AI work stops at the application layer. Production AI needs the layer underneath it too — serving, scheduling, GPUs, storage, networking — and the two are rarely designed by the same people.
We work across the whole stack: the application a business uses, the retrieval and orchestration behind it, the serving layer that makes it fast enough, and the infrastructure that makes it affordable. Generative AI, RAG, agents and automation at the top; model serving, GPU capacity, Kubernetes platforms and observability underneath.
From AI idea, to production application, to the infrastructure underneath it.The layers are rarely designed by the same peopleAI capabilities in detail →
Deep FinTech expertise. Broad engineering capability.
Money movement is a correctness problem before it is a throughput problem. Authorization, clearing, settlement and reconciliation each fail in their own way, and the failures are expensive.
- Payments
- Authorization
- Clearing
- Settlement
- Ledger
- Architecture
- Development
- Integration
- Automation
- QA
From datacenter to cloud to AI infrastructure.
The expensive part of cloud is rarely the migration. It is the second year — the estate nobody owns, the environments that drifted apart, the bill that grew faster than the business.
Cloud & Infrastructure in detail →Quality engineering with AI.
Most test suites do not fail because they were badly written. They fail because maintaining them costs more than the bugs they catch.
QA Automation in detail →When the problem doesn’t fit into one technology.
The hard problems sit between disciplines — an AI feature that is really a data problem, a cost problem that is really a scheduling problem, a reliability problem that is really an architecture problem. Diagnosing those requires depth in more than one layer.
- 01Understand
- 02Architect
- 03Build
- 04Optimize
- 05Evolve
Bring us your technology challenge.
Complex problems rarely fit inside one technology. Tell us what you are trying to solve and we will tell you how we would approach it.
Discuss a Technology Challenge