Decisionline.ai
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AI + Engineering for Decisions that Optimize Safety and Mobility

Engineering intelligence for complex systems.

We apply machine learning, AI, and optimization to produce clear, defensible decisions—optimizing operations and safety while delivering measurable savings for government entities, insurers, legal teams, and engineering firms.

Explore solutions See examples

Solutions

Data → AI Models → Learning → Testing → Deployment → Results

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Data Optimization

Connect and harmonize data from signals, ATMS logs, probes, counts, geometry, and safety sources. Automated QA/QC, entity matching, temporal/spatial alignment, and feature stores that make the data ready for AI at network scale.

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Machine Learning

Model discovery and training on curated feature stores—supervised, unsupervised, and graph methods with expert‑in‑the‑loop review, constraint handling, and uncertainty quantification.

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Testing & Evaluation

Engineer‑grade validation grounded in transportation practice: back‑testing to ground truth, scenario and edge‑case tests, sensitivity and explainability, and expert review before release.

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Deployment & Interface

User‑friendly deployment with dashboards and APIs, SSO and audit trails, and optional LLM assistants. Integrates with your existing systems for seamless decision workflows.

Who we helped

Government & MPOs

Prioritize investments, quantify benefits, and align decisions with safety & mobility outcomes.

Insurers & Law

Litigation AI analysis & deployment: claim triage, scenario reconstruction, exposure modeling, and expert‑ready reports—rigorous methods without positioning the firm as a forensic brand.

Engineering Firms

Partner to engineering firms on complex problems—integrated AI applications for data fusion, modeling, and optimization that unblock designs, de‑risk delivery, and accelerate decisions.

Accomplished Examples

Data

AADT Estimation using AI

Using trained AI models, we estimate traffic counts—vehicular, bicycle, and pedestrian—at any point in your network. Models blend probes, limited counts, and contextual features (land use, geometry, controls) to produce auditable estimates with confidence bounds.

Safety

Data‑Driven Safety Analysis

Data import, optimization, and correction using learned AI models; interactive spatial visualization with user‑custom models; advanced predictive analysis of risk; and improvement recommendations prioritized by impact and cost.

Risk

Legal / Risk Management AI Evaluation

AI‑based models to evaluate engineering litigation exposure for government entities, law firms, insurers, and risk managers—multi‑layer data acquisition, scenario reconstruction, causation analysis, and consistent, expert‑ready outputs.

Focus Areas

Safety Analysis

Data‑Driven Safety Analysis

Deep data review and optimization of raw datasets; development & calibration of Safety Performance Functions (SPFs); evaluation of proposed improvements using AI models and optimization of the alternatives.

Exposure & Risk

Litigation

Analyze litigation exposure from an engineering perspective for government entities, law firms, insurers, and risk managers—scenario reconstruction, causation analysis, and expert‑ready documentation.

Transportation

Transportation Engineering

AI‑assisted traffic engineering across signals, corridors, and networks—demand inference, control strategies, and simulation‑backed what‑ifs to improve flow and safety.

Applied AI

Other Engineering Problems

Deploy advanced, integrated AI applications to difficult engineering problems—data fusion, modeling, testing, and decision support, from feasibility through deployment.

Team Leader

Andrew Kwasniak, PhD, PE, PTOE, ACTAR

Andrew Kwasniak PhD, PE, PTOE, ACTAR

Founder & Principal. Andrew is an engineering problem-solver who applies AI applications, machine learning, and data-driven analysis to complex transportation and infrastructure challenges—turning messy, multi-source data into auditable models and defensible decisions. He leads a multidisciplinary team—software engineers, civil/transportation experts, human-factors specialists, and accident-reconstruction practitioners—building deployable model pipelines with QA/QC, governance, and explainability so clients can operate on the decision line across agency programs and engineering-litigation evaluations.

A national leader in data‑driven safety analysis (including development of Safety Performance Function tools) and advanced data collection/visualization, Andrew brings litigation‑grade rigor without leaning on forensic branding—focused on learning → testing → deployment that agencies and partners can trust.

PhD, PE, PTOE, ACTAR

Innovative engineering begins with Decisionline: AI‑driven solutions to enduring infrastructure problems—effectively and efficiently addressing complex engineering issues.

Contact

Decision, not noise. Tell us what you’re trying to improve; we’ll suggest the quickest, defensible way to get there.

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