Turning aerial LiDAR into auditable emissions data

What we'd propose

• Enterprise AI

  Autonomous validation engine for plume analysis

  A convolutional-neural-network pipeline that takes the first pass on every GML scan, classifies plume presence and quantifies emission rate, and surfaces exceptions for human review only when the model confidence or environmental conditions warrant it.

  • Training data assembly from fifteen years of plume history

    Labels drawn from the existing record

    Curate Bridger's labelled plume-history archive into training, validation and test splits with explicit handling of seasonal variation, surface conditions and wind regimes, so the model is exposed to the conditions it will run on.

  • CNN-based plume identification and quantification

    First-pass classification on every scan

    Run a convolutional-neural-network (a deep-learning image-recognition architecture) over each scan to identify plume presence, classify intensity and produce a quantified emission rate with a confidence score attached to every output.

  • Analyst exception-handling interface

    Human review where it counts

    Build the user interface so analysts see the model's output, the inputs behind it, and the cases the model flagged for review, which keeps the human role intact at the points where it adds the most value rather than at the routine first pass.

  • Report delivery tracks scan volume instead of analyst headcount, which is what near-real-time global detection requires.
  • Analyst time concentrates on the cases where the model is uncertain, rather than on every scan.
  • Each output carries the inputs that produced it, so an audit can replay the model's reasoning rather than reconstruct it.

  We've done this before

  • Transforming Raw Bioprocess Data into Actionable Insights
  • Bioreactor Control: Computer Vision for Non-Invasive Foam Management

  See how we do Enterprise AI
• Enterprise AI

  Ontology-based data platform for multi-modal emissions

  A shared data architecture that defines the entities each measurement technology reports once, then loads aerial LiDAR, satellite, drone and ground-camera data against that single model, so reconciliation becomes a query rather than a project.

  • Shared emissions ontology

    One model across modalities

    Define plume, source, site, instrument, emission rate and uncertainty as explicit entities with agreed relationships, so aerial LiDAR, satellite passes, drone surveys and ground-camera reads describe the same things in the same way.

  • Ingestion from each measurement modality

    Loading each source against the model

    Build the data pipelines from GML, satellite, drone and ground-camera sources with schema validation at the boundary so out-of-spec records fail loudly instead of being silently dropped.

  • Reconciliation and retrieval layer

    Level 4 and Level 5 in one query

    Expose the combined dataset through a retrieval layer that answers source-level and site-level questions together, supporting OGMP 2.0 Level 5 reconciliation and Responsibly Sourced Gas audit cycles without a new extract for each one.

  • Reconciliation work is done once against the shared model, not once per audit cycle.
  • Each new modality joins an existing architecture rather than triggering another migration.
  • The platform becomes the substrate for regulators, operators and internal analytics to query the same data in the same terms.

  We've done this before

  • Ontology-Driven Ecosystem for Biomanufacturing Data
  • Creating a Digital Lab for a Global Biotech Player

  See how we do Enterprise AI
• Digital CDMO

  VR-enabled technician onboarding for SCOWA assembly

  An immersive training programme that captures the assembly know-how of Bridger's photonics engineering team in virtual-reality environments, so the institutional knowledge of the people leaving under BlinkForward is preserved as a training asset.

  • Virtual-reality SCOWA assembly environments

    Training without tying up physical hardware

    Build VR simulations of the 1.65-micron SCOWA assembly and maintenance procedures, including the precision steps where surface-defect dynamics matter, so trainees can practise without consuming production equipment time.

  • Structured digital onboarding journeys

    Role-based learning paths

    Create learning journeys tailored for laser engineers and manufacturing technicians, with progression tracked against the competencies each role requires, including assessment of the safety-critical steps in laser handling.

  • IoT gateway integration on the Bozeman line

    Live equipment data for the training programme

    Connect the Bozeman manufacturing equipment through OPC UA (Open Platform Communications Unified Architecture) gateways so live process data feeds the training simulations and the trainers see the actual conditions a new technician will encounter.

  • The photonics knowledge that leaves with the workforce reduction is captured as an interactive training asset rather than lost.
  • New technicians reach competence faster, which matters in a tight Montana labour market.
  • The same OPC UA connectivity that supports training becomes the data layer for future line-side improvements.

  We've done this before

  • Transforming Biotech Manufacturing with VR/AR
  • Support Framework & Assisted Reality for Global Biotech Manufacturer

  See how we do Digital CDMO
• Digital Lab

  Analyst-first user experience for the validation pipeline

  A redesign of the data validation interface that puts the analyst's mental model at the centre, surfaces the AI's reasoning in the same view as the data, and builds the trust required for autonomous decisions to be accepted as the first pass on every scan.

  • User-experience redesign of the validation interface

    An interface built around how analysts actually work

    Revamp the analyst's working interface based on direct observation of expert workflows, with the AI's classification, the inputs it saw and the confidence it carries shown alongside the plume data so the reasoning is legible.

  • Shadowing and feedback loops

    Software teams inside the analyst workflow

    Embed digital-team scientists with the expert analysts for structured shadowing periods, so the friction analysts experience is translated into specific interface improvements rather than guesses from outside the workflow.

  • Sandbox environments for automated decisions

    Trust built by testing

    Provide isolated environments where analysts can test the AI's decisions against historical scans they already know the answer to, with disagreement flagged for review, building confidence in the automation incrementally rather than asking for it all at once.

  • Analysts transition from manual verifiers to supervisors of automated decisions, which is the role the BlinkForward workforce model depends on.
  • The interface itself becomes a record of how expert judgement is encoded into the platform.
  • Adoption follows trust, which is built incrementally rather than by mandate.

  We've done this before

  • Bridging the Gap Between Scientists and Algorithms
  • Transforming Raw Bioprocess Data into Actionable Insights

  See how we do Digital Lab
• Agents

  AI agents for audit-ready calibration and reporting

  Narrow, reviewable agents that draft the recurring parts of audit-ready documentation — calibration logs, OGMP 2.0 reconciliation narratives, EPA Subpart W report sections — from the underlying system records, with a named reviewer approving every output.

  • Calibration log drafting from instrument records

    First drafts from the actual data

    Generate the first draft of a calibration log directly from the underlying sensor and instrument records, including the deviation history and the corrective action timeline, so the human reviewer edits and judges rather than assembles the document.

  • Reconciliation narrative drafts for OGMP 2.0

    Level 4 and Level 5 reconciliation in plain language

    Produce a draft reconciliation narrative for each reporting period that combines source-level and site-level measurements into the structure OGMP 2.0 expects, with the underlying figures and the cross-modal comparison included for review.

  • Standards-change impact search

    Which reports a regulatory change touches

    When EPA or OGMP publishes a change, retrieve every controlled report template, calibration record and reconciliation narrative that references the affected standard and rank them by exposure, so the update scope is established by search rather than by recollection.

  • Audit cycles move faster because the first draft is produced from records rather than compiled by hand.
  • The scope of a regulatory change is established on day one rather than discovered during a review.
  • Every output carries a named reviewer, so the human accountability in the audit chain is unchanged.

  We've done this before

  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES

  See how we do Agents