Selected work

Systems we have had to make work end to end.

These examples are anonymized. Each one is written around the operating problem, the constraint that shaped the design, the engineering work, and what changed afterward.

XR developer and input systems

Problem

Developers needed input capabilities to behave consistently across hardware, runtime, APIs, SDKs, documentation, diagnostics, and staged releases.

Constraint

A feature could be technically complete and still fail in practice when compatibility, entitlement, privacy, diagnostics, documentation, or release state drifted apart.

Engineering work

Tracked and virtual keyboard product execution

Controller and hand-input API launch coordination

Developer diagnostics across SDK/API documentation, telemetry, compatibility, entitlement, defects, and releases

Acceptance criteria and instrumentation

Cross-functional readiness reviews

Experimental, partner-preview, and public release paths

Result

Developer need, capability state, compatibility evidence, diagnostics, and release readiness were brought into one operating path. That made release decisions easier to make and problems easier to isolate.

Enterprise AI and governed workflow

Problem

Models could answer questions. The useful job was getting those answers into identity, records, documents, APIs, events, workflows, and approvals already used by the business.

Constraint

The path crossed hybrid infrastructure and business systems with existing access controls, source authority, and human approval requirements.

Engineering work

Serverless integration services

REST APIs and event-driven integration

Enterprise identity and access-control patterns

Agentic RAG workflows with tool schemas and approval gates

Retrieval across documents, records, APIs, and events

Hybrid deployment across cloud and enterprise infrastructure

Result

Retrieval, workflow, identity, events, and human approval were connected into a governed task path that people could use inside the existing environment.

Wireless, edge compute, and operating software

Problem

Network infrastructure, edge compute, connected devices, and operator software had to behave as one service across physical sites.

Constraint

The path crossed network layers, device lifecycles, compute placement, field conditions, and different operating owners. A locally healthy component could still leave the service unusable end to end.

Engineering work

4G LTE-Pro / 5G core and RAN components

CU/DU architecture and edge-compute placement

Private wireless and Industrial IoT integration

Device and base-station management

Central operator software

Workflow automation around field operations

Result

Network, compute, devices, and field workflows were tied into one operating path with clearer ownership and fewer integration gaps for the customer team to bridge manually.

LiDAR + AI perception and field response

Problem

Fixed sensing covered isolated zones. Operators needed continuous object position, movement history, classification, and a usable response path across a much wider area.

Constraint

The design had to preserve movement continuity across a 900 m perimeter and turn perception into information that could drive incident response.

Engineering work

Multi-object position and trail tracking

Speed, direction, distance, and classification

Video and UAV correlation

Incident creation and operator notification

Command-center and field-response workflow

Edge-assisted near-real-time event processing

Result

In the 900 m design model, continuous coverage increased from 449 m² to 87,000 m² — a 193× modeled increase — with movement, classification, incident, and response context carried into the operator path.

Operational intelligence from distributed signals

Problem

Sensors, video, access systems, security logs, workforce activity, enterprise systems, and external feeds were producing useful signals without a shared operating context.

Constraint

The sources differed in timing, structure, quality, and meaning. Adding more alerts would have increased workload for the operator without improving the response.

Engineering work

Contextual analysis across heterogeneous feeds

Threat and event severity assessment

Situational awareness for operators

Workflow management

Integrated communications and notification

Preventive and mitigating task flows

Result

Detection, context, severity, communication, workflow, and operator action were connected so the response team could work from a common operating picture.

Large-scale data and production AI

Problem

Useful decisions depended on live streams, historical data, enterprise records, documents, APIs, and model outputs arriving together in a dependable production path.

Constraint

The environment included 50+ PB/day data scale, mixed batch and streaming paths, changing model behavior, governance requirements, and actions that sometimes required explicit human authority.

Engineering work

Streaming and batch data environments

Lakehouse, graph, and knowledge layers

Enterprise RAG with grounded retrieval and citations

Recommendation and next-best-action systems

Risk, anomaly, and intent models

Agent workflows with permissions, approvals, retries, audit, and handoff

Result

Data and model outputs were carried into task and operating workflows with permissions, approvals, evidence, retries, and outcome measurement built into the execution path.

What we carry forward

Leave the customer with a system they understand and can keep improving.

We document the state model, interfaces, measurements, failure behavior, deployment path, and operating decisions behind the implementation. That makes the next change easier for the team that owns the system after us.

Experience & organizations Start a conversation