A showcase of what we build for regulated manufacturers. Governed workflows, connected systems, decision-grade reporting, and private on-premises AI running on top of them. Everything below is delivered work, with the screens and the engineering behind it.
The first problem is establishing where the operational decision lives, how it is governed, and what system owns the record. Almost every engagement in this file started the same way: something happened to a controlled artifact, and the record of it was thinner than the change was.
How we sequence every engagement
Challenge. A commercially critical process ran on spreadsheets and email. There was no state, no approval record, and no way to see where a request was sitting or who was holding it.
What we built. Three layers, built in that order. The first is a governed platform with a full state machine covering request, review, and approval or rejection. It integrates live with the ERP for cost and material data, and generates an output carrying an executive summary of total cost, delivery time and the specific elements driving cost. Every state transition is owned, logged and notified. The system reads from the ERP and never writes back to it.
Underneath it, a deterministic optimizer. Quoting a thermoformed part means deciding how many cavities to run per shot. Too few wastes sheet and machine time on every cycle for the life of the tool. Too many will not fit the platen or will violate a spacing constraint. The platform computes that layout from the part geometry and the machine's physical limits. The answer is reproducible, and so is the cost that follows from it.
On top of it, an on-premises assistant. It carries context of both the application and the business rules encoded in it, guides users through how to quote, surfaces insights across historical quotes and prepares reports on request. Everything runs on the client's own hardware. No cost structure, margin logic or customer data leaves their environment, and there is no cloud exposure to review.
A spreadsheet process replaced by a governed system with a full audit trail, running in production. The approval chain is visible by step and by owner. The assistant answers against structured data, the workflow was governed first.
Live ERP integration, read-only · state machine workflow · STEP geometry ingest and 3D preview · deterministic cavity-layout optimizer under machine constraints · automated notification · structured document generation · KPI instrumentation · on-premises AI assistant, local GPU inference · no data leaves the client environment
Challenge. Program leadership operated on manually assembled status decks. Risks surfaced after deviation had already occurred, and resource conflicts were discovered too late to act on. Replacing the scheduling tool the planners already used was not acceptable.
What we built. A governance layer that reads existing schedules into a structured data platform and drives live dashboards, a risk register, milestone tracking against baseline, a resource heatmap with capacity versus utilization, and automated alerts on milestones at risk, overloaded resources, and items aging past defined thresholds. It deploys inside the client's own tenant. No planner changed tools.
Configured, not templated. The panels, gates and thresholds are set against how the client actually runs programs, they are not a fixed layout. In a validated environment that means qualification phases carry as first-class schedule items alongside engineering work, and milestone variance is reported against the approved baseline, not the most recent replan.
Microsoft-native, not Microsoft-only. The governance layer is built on the Microsoft stack and runs inside the client's own M365 tenant. No new vendor to procure, no data leaving the tenant boundary. The ingestion side is deliberately source-agnostic. Schedules are one input; elsewhere in this file the same approach reads a live ERP, an accounting system, a plant production system over ODBC, and machine controllers directly. Where a planning tool is not Microsoft, it becomes another source into the same governed model.
Running in production across a multi-phase medical device program. Risk register, milestone tracking against baseline, and portfolio dashboards in daily use. Overloaded resources and at-risk milestones raise themselves.
Microsoft 365 tenant deployment · Dataverse · Power BI · Power Automate · schedule ingestion, unidirectional · baseline-versus-current milestone variance · qualification phases as schedule items · resource capacity analytics · configured per client environment
Challenge. The plant knew when machines broke. It did not know, in real time, how much each one was actually producing, or where capacity was disappearing between shifts.
What we built. Continuous state monitoring across nine machines, including legacy equipment with no protocol and no controller access, with availability timelines, micro-stoppage detection where cycles run slower than expected, and automated shift reporting.
Capacity the plant already owned, recovered without buying a machine or adding a shift. The chart below carries the numbers.
Sensorless, native protocol, and PLC-tap acquisition · legacy machine coverage · micro-stoppage classification · automated shift reporting · machine-level data only, no individual worker data collected
| Machine | Change |
|---|---|
| Best performing machine | +34.1 pts |
| Second best | +26.5 pts |
| Workshop-wide, all 9 machines | +11.5 pts |
| Machine with reduced work assigned | −10.2 pts |
Challenge. Invoicing for a high-volume customs operation depended on spreadsheets, loose email, and manual re-entry, with no way to reconstruct how a figure was derived. The data was commercially sensitive and could not leave the client's environment.
What we built. An ingestion and reconciliation platform that consolidates operational reports, system exports, and email attachments, applies business rules, detects differences and missing records against historical baselines, and requires human review and authorization before anything is committed downstream. Every action, change, and movement is indexed automatically, permanently, and immutably. No operational data leaves the client environment.
The assistant comes next, not first. An on-premises operational intelligence layer is designed on top of this platform, to answer in natural language over the governed data once that data layer is complete. It is specified to answer only from authorized sources with permission checks applied before any query, and to hand business rules and calculations to deterministic components rather than to the model. It is not deployed yet, and that order is deliberate.
Multi-source ingestion · exception and difference detection · immutable audit index · no data leaves the client environment · assistant layer designed, not yet deployed: local language model, permission-scoped retrieval, deterministic calculation tools
Challenge. An existing control application froze under load and could not run unattended for days. Overtravel alarms were absent, interlock functions were not working, status lights reported the wrong machine state, recipe steps could not be edited in place, and the motion code had diverged into an unmaintainable duplicate set. Tests run for days, a validation pass that consumed more than two cycles was unusable.
What we built. A full rebuild of the control and data acquisition software on a real-time controller with FPGA and scan engine: deterministic motion sequencing across three integrated servos, force and torque monitoring over Modbus TCP, configurable force-reversal thresholds, safety supervision harmonized to the certified safety controller without ever overriding or emulating it, and a rebuilt 15-inch touchscreen operator interface conformed to the client's visual and interaction standard across seven screens and a 91-entry alarm catalog. Configuration lives in a single source. Systematic version comparison across interface revisions isolated visual, state transition, and functional changes without taking the rig out of service.
Scope, against the clock. This is not a patch on an existing application. It is a full rebuild of the machine's control and data acquisition brain, from requirements baseline through validated release, in nine weeks. Motion, safety supervision, force control, recipe handling, data capture and the operator interface were all replaced, on a machine that has to run unattended for days at a time.
Every requirement verified by test, demonstration, inspection, or analysis, with a traceability matrix mapping each requirement to its verification. A continuous 48-hour endurance run with the interface responsive throughout, no freeze, no data or cycle-count loss, and a complete export carrying full traceability fields including part number, lot number, operator, recipe name and version, equipment IDs and calibration dates. Logging operator-selectable to 500 Hz.
LabVIEW · NI CompactRIO cRIO-9040 · FPGA + Scan Engine · Modbus TCP · 6-axis force/torque sensing · integrated servo motion · certified safety controller supervision · ISO 12100, ISO 13849-1, ANSI B11 · optional documentation package harmonized to 21 CFR 820
One account, four systems, built over successive programs in a release environment governed by ASPICE. They are grouped here because they answer the same question in four places: how do you make verification produce its own evidence, instead of producing a result that someone then has to document.
Challenge. Evidence collection, reporting, and traceability maintenance for audits in a heavily regulated environment consumed up to a third of the technical team's productive capacity. Traceability was reconstructed before audits.
What we built. An automated documentation platform that captures operational results in real time and synchronizes evidence back to the original requirements, adaptable to client-specific requirements management platforms, compliance traceability is maintained as a by-product of running the tests.
Administrative documentation effort reduced by at least 80%. Audit readiness maintained continuously, with bidirectional traceability between software, test evidence and stakeholder requirements.
Basis: EmbedMotion internal delivery data. Documentation effort on the program before and after the platform, reported by the engineering lead.Automated evidence capture · requirements management platform synchronization · ASPICE-aligned lifecycle · bidirectional traceability
Challenge. Test design and test execution were separate manual phases. A designed test still had to be hand-programmed before it could run, which put a programming queue between an approved design and any actual verification.
What we built. A generator that reads design specifications and automatically produces complete, executable test sequences, eliminating the intermediate programming phase entirely.
Sequence development time reduced by more than 95%. Execution code is ready to operate the moment the design is approved.
Basis: EmbedMotion internal delivery data. Hand-coding a sequence from an approved specification, against generating it, reported by the engineering lead.Specification parsing · automated sequence generation · NI TestStand · Python
Challenge. Verifying security behavior across a closed protocol required manual analysis that no general purpose tool could perform, and structural risks were surfacing late, after manufacturing commitments had already been made.
What we built. KeyForge, a purpose-built validator that automates fault injection, verifies end-to-end message protection including polynomial CRC and sequence counters, and evaluates seed randomness and uniqueness against international standards, detecting repeated seeds, absent randomness, and parametrization errors. Supports both predefined and user-defined test sequences.
Structural risk surfaces while the design can still absorb the change. The analysis is repeatable, a finding is re-tested against the next build.
Proprietary protocol parsing · automated fault injection · CRC and sequence counter verification · NIST-referenced randomness evaluation
Challenge. Validation cycles gated releases, and time-zone gaps between engineering sites meant hardware sat idle between manual test runs.
What we built. A continuous integration and continuous test framework on specialized instrumentation hardware. It detects repository changes, updates the physical equipment automatically, and executes validation sequences autonomously against the standard governing the program, with automated evidence capture.
Total execution time decreased by at least 74%. Releases shipped with no defects leaking to production.
Basis: EmbedMotion internal delivery data. Manual validation runs against the automated pipeline across comparable test campaigns.Repository change detection · automated bench provisioning · NI PXI instrumentation · TestStand and Python · CI/CD pipeline integration
From requirements and architecture through deployment and long-term support, one engineering team owns the complete solution. Every capability below has delivered work behind it, tagged on each project in the next section.
Transform manual execution into governed operational workflows with standardized processes, accountability, and traceability.
Workflow automation · Process standardization · Digital traceability
Connect enterprise and operational platforms into a unified information ecosystem, without replacing what already works.
System integration · Data synchronization · Legacy connectivity
Turn operational data into intelligence people trust enough to act on, delivered where the decision actually gets made.
Operational dashboards · Automated executive reporting · Performance analytics
Automate engineering verification while generating quality evidence that holds up under audit.
Test automation · Unit testing · Product verification
Engineer software using disciplined practices built for regulated industries, where controlled release is not optional.
Software development · Controlled deployment · Requirements traceability
Apply AI to governed systems and trusted data, then extend it into engineering formats other tools cannot reach.
Private on-premises deployment · Context-aware assistants · Agentic engineering for proprietary formats
Every automation and AI capability rests on one assumption: that source code is text. Text can be read, compared, patched, and generated. That holds for C, Python, and XML. It fails completely for a binary engineering format, which cannot be opened in an editor, searched, or compared. On those formats the tooling does not exist.
So we built a scripting channel against the vendor’s own automation API, and the binary artifact became something that can be inspected, read back, and authored programmatically. Type definitions, cluster order, connector panes, and wire nets read back as data, meaning contracts are audited mechanically instead of by opening files and looking. Read and write are both covered, meaning generated output can be verified.
Three gates, and each one produces a deliverable before the next one starts. No engagement moves forward without a gate review.
Understand the operation before writing software. Requirements, architecture, and integration surfaces are baselined against your systems, not assumed.
You sign off the requirements baseline. Nothing gets built against an assumption we made on your behalf.
Engineer, integrate, and validate on target. Development and unit testing against the baseline, deployed into a test environment.
The end-to-end path is validated with your real files and real data, not test fixtures.
Deploy with evidence and transition into production. System integration, verification, validation, and handoff.
Every requirement maps to a test case with recorded evidence, and you have accepted the release candidate in writing.
What we commit to in writing
Read-only integration is a contractual commitment across our engagements, not a technical limitation. Your validated systems stay untouched. Where we have installed controls alongside existing equipment, we have committed in writing that no change is made to the existing controller program or any existing control logic.
Requirements traceability matrix, executed test cases with objective evidence, and a validation summary report, delivered at the release gate. Stated plainly: this is a documentation package, not hardware IQ, OQ, and PQ protocols.
Custom systems are delivered with source code, configuration, and release documentation. After final payment, you own the delivered application. A thirty-day hypercare period is included to stabilize the release and capture actual support needs. Ongoing maintenance is then proposed based on observed issues, required response times, and any SLA coverage the operation requires.
