Opulion Systems builds production systems that hold in the field, not demos that pass in staging. We work the full stack, from model to metal, and across the whole lifecycle, and clients enter at any stage: diagnose why a live system fails, architect what will hold, build it end to end, harden it for the field, and operate it in production.
Most of our work is AI-powered, but little of it is only AI, and some has no model at all: server-fleet platforms over Redfish, IPMI, and SNMP, industrial protocol integration across OPC-UA and Modbus, telemetry at scale, and orchestration. The differentiator is range. Most vendors work a single layer and stop where it gets hard. We work every layer, from models and pipelines down to protocols, drivers, firmware, and silicon, which is why we can follow a failure to where it actually lives instead of papering over the symptom.
We are founder-led and deliberately small, taking a few select clients each quarter so the senior engineer who scopes your problem is the one who solves it, with no handoff to juniors. Selected outcomes: a document model recovered from 44 to 95 percent accuracy in five days, false alarms cut 41 percent across an 87-unit fleet, a Korean OCR pipeline at 96.8 percent across 50,000 documents a month, and on-device voice command at 95.7 percent and 22 ms on a Jetson Orin Nano.
We work across industrial automation, defense and unmanned systems, medical devices, infrastructure and server fleets, fintech and risk, and robotics.
Opulion Systems builds production systems that hold in the field, not demos that pass in staging. We work the full stack, from model to metal, and across the whole lifecycle, and clients enter at any stage: diagnose why a live system fails, architect what will hold, build it end to end, harden it for the field, and operate it in production.
Most of our work is AI-powered, but little of it is only AI, and some has no model at all: server-fleet platforms over Redfish, IPMI, and SNMP, industrial protocol integration across OPC-UA and Modbus, telemetry at scale, and orchestration. The differentiator is range. Most vendors work a single layer and stop where it gets hard. We work every layer, from models and pipelines down to protocols, drivers, firmware, and silicon, which is why we can follow a failure to where it actually lives instead of papering over the symptom.
We are founder-led and deliberately small, taking a few select clients each quarter so the senior engineer who scopes your problem is the one who solves it, with no handoff to juniors. Selected outcomes: a document model recovered from 44 to 95 percent accuracy in five days, false alarms cut 41 percent across an 87-unit fleet, a Korean OCR pipeline at 96.8 percent across 50,000 documents a month, and on-device voice command at 95.7 percent and 22 ms on a Jetson Orin Nano.
We work across industrial automation, defense and unmanned systems, medical devices, infrastructure and server fleets, fintech and risk, and robotics.
Location and contacts
Major clients
Processes and approach
How do you gather and validate client requirements?
We start by diagnosing the real problem, not the stated one. That means reading the actual system, its telemetry, its failure cases, and its constraints, then restating the requirement as a measurable outcome both sides agree on before any code is written. For a production system the requirement is rarely a feature list, it is a behavior under real load and real consequence. We validate by defining the success metric and the failure modes up front, so the target is a number, not an opinion.
How do you ensure alignment with client goals and business strategy?
We tie every engagement to the cost of the system being wrong. Before building, we agree on what failure actually costs the business, downtime, a lost contract, a safety or compliance risk, and set the operating point to that rather than to a generic benchmark. Because the founder leads the work directly, there is no translation layer between what the business needs and what gets built. If we think the work will not serve the goal, we say so.
Which software development methodologies do you use (e.g., Agile, Waterfall, Scrum)?
We work in short, measured iterations, closer to lean and Agile than to Waterfall, without ceremony for its own sake. The lifecycle is the method: diagnose, architect, build, harden, operate, with a working, measured increment at each step. Because engagements are senior-led and small, we favor tight feedback loops and direct communication over heavy process. For regulated work we add the documentation and traceability the domain requires.
How do you keep clients and stakeholders updated on project progress?
Directly and in writing. You work with the engineer doing the work, not an account manager, so updates carry real technical detail, not a status color. We report progress against the agreed metric and failure modes, flag risks early, and keep a written trail of decisions and their rationale. You always know what is done, what is next, and what is at risk, in terms you can act on.
How frequently do you hold check-in meetings or status updates?
We set the cadence to the engagement, typically a weekly working session plus asynchronous written updates as milestones land, with more frequent contact during a live diagnosis or a critical push. We keep meetings short and technical, and default to async for anything that does not need a live conversation, so your time goes to decisions, not status theater.
What quality assurance practices do you follow?
We measure, we do not assume. Every system is judged against a defined metric and its failure modes, evaluated on real held-out data or real load, not a happy-path demo. We build in per-stage evaluation so a failure is visible and locatable rather than hidden in an end-to-end average, add automated tests and monitoring, and harden explicitly for the field before calling anything done. For AI systems that includes faithfulness checks, abstention, and drift monitoring.
How do you identify and manage project risks?
Risk-first is how we start. Diagnosis surfaces the real failure modes early, and we design against the ones that cost the most, not the ones that are easiest to demo. We prefer the fewest moving parts that solve the problem, add guardrails, timeouts, and graceful degradation so failures shed instead of cascade, and gate anything irreversible behind human sign-off. Risks are named and tracked in writing, not discovered at the end.
What kind of support or maintenance do you offer after delivery?
Operate is a stage of the work, not an afterthought. We can hand off with documentation and runbooks, or stay on to run the system, watching the metric, drift, and the tail, and improving it as the real distribution shifts. Because we keep few clients, we can hold long-term operate-and-harden relationships instead of building and disappearing. Support terms are set to what the system's consequence warrants.