Modernizing critical systems without disrupting the business

To protect confidentiality, names, locations, figures, committed metrics, equipment specifications, operating parameters and contractual details are omitted. These are designs in progress; stated objectives are not presented as achieved results.

• Automated vehicle access and operational traceability

  In progress · solution design

  Designing a platform for operations with different vehicle, visitor and goods flows, where access authorization must coordinate with operational processes and existing systems.

  Business and operational challenge

  Replace fragmented checks and decisions that depend on manual intervention with a coherent flow that reconciles identity, authorization and operational context. The design must account for exceptions, service continuity and retrievable evidence without turning every edge case into a blocked operation.

  Discovery and design decisions

  • Mapped actors, entry and exit journeys, service types and decision points with operational stakeholders.
  • Separated functional requirements, quality attributes, operating rules and responsibilities across participating systems.
  • Defined boundaries, master data, manual exceptions, inspection criteria and audit needs before finalizing interfaces.
  • Prioritized integrations and physical/operational risks to distinguish critical dependencies from later improvements.

  Functional reference architecture

  Identification and credentials

  Automated vehicle identification and visitor credential validation are kept separate from the final authorization decision.

  Access orchestration

  A domain service combines context, document authorization and configurable rules, coordinating control devices through defined integration contracts.

  Validation and integration

  Adapters connect document sources and external platforms with clear contracts, explicit handling of unavailable dependencies and defined provider responsibilities.

  Exceptions and continuity

  Explicit paths for review, inspection and manual authorization record permissions and reasons rather than relying on implicit decisions.

  Operational traceability

  Correlated decision and event records, linked evidence, alerts and later retrieval support discrepancy resolution and operational audits.

  Proposed rollout

  1. 01Validate journeys, operating conditions, responsibilities and interface availability on site.
  2. 02Agree contracts across the platform, local controllers and external systems; prepare catalogs and acceptance criteria.
  3. 03Test normal flows, exceptions, connectivity loss and recovery in a controlled environment.
  4. 04Pilot a bounded flow, review incidents and refine rules before progressively expanding to other flows.
  5. 05Define monitoring, support, safe manual operation and rollback criteria for each stage.

  Efficiency and investment control

  Phased scope addresses the highest-risk dependencies first and validates interfaces before scaling procurement and installation. Automation is intended to reduce repetitive checks, rework and discrepancies; any savings must be measured during the pilot, not assumed in advance.

  Design objective (not a measured outcome)

  More consistent and auditable access decisions, with controlled exceptions and less reliance on isolated manual checks.

• Digital load auditing with computer vision and AI

  In progress · operational analytics module

  Designing a complementary module that reconciles a digital load declaration with visual evidence from the process and returns a traceable recommendation to the operational flow.

  Business and operational challenge

  Manual counts can be inconsistent, while post-event reviews often lack structured evidence. The analysis must distinguish pre-existing load from items added during the operation, interpret different loading cycles and send cases with insufficient evidence to human review.

  Discovery and design decisions

  • Decomposed the physical process into observable states: initial condition, loading activity, completion and reconciliation.
  • Identified variation by cycle type, occlusion, pre-existing items and conditions where the system should abstain from an automated decision.
  • Defined pilot acceptance criteria using representative data, reviewed jointly by operational and technical stakeholders.
  • Explicitly bounded responsibilities across capture and analytics, the existing control system, physical infrastructure and exception handling.

  Functional reference architecture

  Capture at the point of operation

  Complementary visual perspectives provide synchronized event sequences without delegating authorization decisions to the model.

  Edge perception

  Processing close to the operation detects and tracks units through the loading cycle, reducing dependence on continuous connectivity for visual analysis.

  Cycle context and initial state

  Process context classification and an initial baseline distinguish what was already present from what was added during the event.

  Reconciliation and rules

  Configurable rules compare observed evidence with the declaration; discrepancies, low confidence or incomplete data trigger review rather than silent authorization.

  Integration and evidence

  A documented service interface connects to the operational platform, with an auditable event record, linked evidence, alerts and a supervisor view.

  Degraded operation

  Network or component failures produce explicit pending states and an alternate operating procedure; recovery reconciles events instead of discarding them.

  Proposed rollout

  1. 01Agree on what constitutes a valid event, which evidence is captured and which decisions must remain under human control.
  2. 02Build a proof of concept with representative samples and operating variability; evaluate quality in real conditions before setting thresholds.
  3. 03Apply a go/no-go gate based on agreed quality, integration and operational criteria.
  4. 04Integrate with the existing system in supervised mode and compare automated recommendations with human review.
  5. 05Calibrate, document exceptions and expand gradually only when pilot evidence justifies the investment.

  Efficiency and investment control

  A proof of concept reduces the risk of purchasing or scaling deployment before real-world performance is understood. Edge processing and automated counting may limit video transfer and repetitive work, but their effect on total cost must be verified through pilot measurements.

  Design objective (not a measured outcome)

  Provide a second evidence source to identify discrepancies and enable timely audits, retaining human review for ambiguous cases and making no unvalidated accuracy claims.

• Consumption reconciliation with computer vision and AI

  In progress · applied AI proposal

  Designing a solution for a high-volume food-service operation that reconciles recorded transactions with visual evidence of consumption at service points. The aim is to turn a hard-to-scale manual review into AI-assisted reconciliation with verifiable rules and auditable exceptions.

  Business and operational challenge

  There is no single point at which consumption can be confirmed: the decision depends on observing a tray and its items across different stations, then relating that event to a transaction record. The solution must classify relevant situations, identify discrepancies and avoid unreliable automated decisions when faced with occlusion, environmental variation or low confidence.

  Discovery and design decisions

  • Mapped the service journey and events that constitute valid consumption, identifying stations, actors and exceptions with operational stakeholders.
  • Defined visual classes and business rules, including tray-without-consumption cases, special categories and events that must be escalated for review.
  • Assessed video capture conditions and availability, data quality, access to transaction integration and privacy requirements early with the responsible teams.
  • Separated responsibilities for model perception, transaction correlation, reconciliation rules, exception management and operational decisions.

  Functional reference architecture

  Governed visual capture

  Video sources at service points selected with operations are synchronized for event analysis and governed by access, retention and privacy controls.

  Edge perception and tracking

  Computer-vision models detect trays and items; anonymized temporal tracking preserves event continuity without custom facial recognition.

  AI data and evaluation lifecycle

  Representative samples are labeled and organized for training, validation and testing. Iterative cycles introduce variability and hard cases, evaluating precision, coverage and errors by class before enabling assisted decisions.

  Consumption correlation and rules

  A backend relates visual events to transaction records using temporal context and applies explicit rules, keeping the model separate from business policy.

  Integration resilient to dependencies

  A connector to the existing transaction system uses the interface made available by its provider. If that is not available in time, report-based reconciliation can serve as an interim step, with its limitations documented.

  Audit and operations

  A reconciliation dashboard, alerts and evidence support exceptions; ambiguous cases go to human review, and operations have an alternate procedure for connectivity or component failures.

  Proposed rollout

  1. 01Validate the service flow, consumption rules, observable stations, responsibilities and privacy requirements on site before capturing data.
  2. 02Agree on access, governance and visual-data retention with responsible teams; capture a representative sample as a preparatory phase, subject to those approvals.
  3. 03Build the backend, integration and dashboard in parallel with labeling; define separate training, validation and test sets from the outset.
  4. 04Train and evaluate iteratively across real-world variation and edge cases; set acceptance criteria from evaluation and operational review, not an assumed figure.
  5. 05Validate integration and reports in staging, compare recommendations in supervised mode and move gradually to production only after an acceptance gate.
  6. 06Establish monitoring, exception handling, model-quality review and an operational response and rollback plan.

  Efficiency and investment control

  Automated reconciliation and exception prioritization may reduce repetitive audit work. Preparatory capture and parallel development can avoid repeating stages, while validating integration and the model before scaling limits investment risk. Savings and actual quality must be measured in a pilot; proposal figures are not published.

  Design objective (not a measured outcome)

  Determine whether computer vision can complement existing records with traceable evidence and more timely reconciliation, while preserving privacy, human review of exceptions and verified AI metrics before any operational automation.

• Banking and capital markets

  Integration and coexistence between platforms while critical systems evolve.

• Retail

  Microservices modernization and cancellation, returns and customer experience flows.

• Telecommunications

  Solution delivery and innovation project management.

• Higher education

  Technical leadership on software projects for educational institutions.

• Food services

  Enterprise software for high-volume operations.

• Health and well-being

  Digital platforms for therapeutic support and patient management.