AI‑accelerated system discovery
We use causal discovery and retrieval‑augmented synthesis to build a traceable hypothesis of how your system behaves—grounded in literature, data, and operational context—in days, not weeks.
We synthesize research, data, and tacit knowledge to reveal where to intervene, how to measure progress, and how to de‑risk delivery. You get a traceable system map, clear leverage points, and a decision brief your oversight bodies will accept.
Request a decision briefWe use causal discovery and retrieval‑augmented synthesis to build a traceable hypothesis of how your system behaves—grounded in literature, data, and operational context—in days, not weeks.
Structured workshops bring experts and rightsholders together to stress‑test assumptions, surface constraints and risks, and establish acceptance criteria—so recommendations are implementable and defensible.
We co‑design an M&E framework with indicators, data flows, and feedback routines so teams can track effects, adapt quickly, and report clearly to executives, funders, and auditors.
Mapped 200+ factors shaping knowledge uptake, isolated three high‑leverage interventions, and delivered an integrated monitoring framework that connects management actions to measurable outcomes.
Built cross‑department system models and a shared theory of change; identified nine leverage points with associated indicators to guide policy implementation and ongoing evaluation.
We combine AI-accelerated discovery with rigorous human expertise to identify strategic leverage points, then design integrated measurement, evaluation, and learning frameworks. AI speeds initial hypothesis generation while thorough stakeholder validation ensures trusted insights.
AI‑assisted review unifies literature, program data, and operational insight into a source‑linked system hypothesis with a clear audit trail.
We facilitate structured engagement with domain experts and rightsholders to confirm mechanisms, expose blind spots, and document dissent.
Graph analytics and policy criteria surface the highest‑leverage interventions, dependencies, and risks—prioritized against cost, feasibility, and equity.
We translate the model into indicators, data flows, and review cadences, enabling real‑time learning and transparent reporting.
Founded by Chris Callaghan (M.Sc.), PolicySpark brings more than 25 years of public‑sector experience (NSERC, Environment Canada, DFO, Treasury Board, OAG). We pair that depth with modern AI tooling so leaders can move faster without sacrificing rigor, fairness, or accountability.
M.Sc. Biology, University of Ottawa
B.Sc. Renewable Resources, McGill University
Senior leadership roles at NSERC, Environment Canada, Fisheries & Oceans Canada, Treasury Board, and Office of the Auditor General
Systems thinking, measurement design, program evaluation, environmental policy, causal analysis, AI-enabled strategy
Deploy AI-powered causal discovery to accelerate initial hypothesis generation, then apply rigorous human validation—delivering comprehensive insights efficiently without sacrificing quality.
Thorough stakeholder engagement ensures AI discoveries reflect real complexity and expert knowledge—creating trusted, actionable models that drive implementation.
Modern tools enable sophisticated analysis at accessible scale—bringing enterprise-grade systems mapping to organizations of all sizes.
From discovery to measurement framework to learning systems—comprehensive support across the entire evidence-informed policy lifecycle, delivered cohesively.
We collaborate with leading government departments and agencies to strengthen evidence-informed decision-making and accelerate policy implementation.
Tell us the policy question you’re wrestling with—we’ll outline the fastest path to a decision‑ready brief and a right‑sized engagement.