Projects
Curated proof of research, founder, and analytical systems work.
This is intentionally selective. The page highlights projects and workstreams that support the advisory positioning without relying on unsupported metrics or private-client claims.
Founder and applied AI/analytics lead
Osiris Systems
Applied analytics and intelligent systems work focused on making data, workflows, and decision support more usable.
Shows the connection between quantitative research, workflow automation, and practical analytical products.
Founder and quantitative finance lead
Osiris Finance
A finance-oriented analytical product direction for chart-first intelligence, market interpretation, and evidence-led signals.
Connects empirical finance, market analytics, and usable information design.
Academic, advisor, and knowledge-exchange contributor
Research and innovation work
Selected public-safe research, teaching, and knowledge-exchange work at the intersection of finance, AI, and analytics.
Provides the evidence base behind the advisory work without overclaiming private project outcomes.
Business cases
Practical advisory cases without unsupported outcome claims.
These cases explain buyer situations, Mikhail's analytical angle, and the intended business effect. They should become full case studies only when real client-approved examples are available.
Business case
AI adoption priority map
A leadership or innovation team has many AI ideas, but no clear way to decide which use cases are worth testing.
Mikhail's angle
Frame AI opportunities around decision value, data readiness, workflow friction, risk, and human oversight.
Intended business effect
A shorter, better-ranked list of AI priorities with clearer next actions and fewer unfocused experiments.
Business case
Forecasting and reporting redesign
A team produces reports or dashboards, but the outputs do not consistently improve planning, forecasting, or action.
Mikhail's angle
Audit the signals, assumptions, review cycle, and decision points that sit around reporting and forecasting work.
Intended business effect
A more disciplined analytical workflow that connects data, interpretation, accountability, and forward-looking decisions.
Business case
Market intelligence briefing
A finance, fintech, or product team needs a sharper view of market signals, digital-asset questions, or event effects.
Mikhail's angle
Use empirical framing, event-study thinking, and structured interpretation rather than loose market commentary.
Intended business effect
Clearer evidence for strategy, product, investment research, or stakeholder briefing decisions.
Business case
Sustainability signal review
An organisation needs to interpret ESG or sustainability-related information without over-relying on headline labels.
Mikhail's angle
Assess signal quality, rating movement, market interpretation, and the link between sustainability data and decisions.
Intended business effect
Better sustainability-related judgement, clearer analytical caveats, and more decision-relevant reporting.
Business case
Evidence quality checkpoint
A team wants to adopt dashboard metrics or AI-generated analysis, but the reliability and decision value are unclear.
Mikhail's angle
Introduce a review point for signal quality, assumptions, validation, and where human judgement must remain visible.
Intended business effect
Fewer false signals, clearer governance, and less risk of adopting metrics or AI outputs that look useful but do not support action.
Case inventory
01
AI adoption priority map
Draws on applied AI, analytics workflow design, and research-led thinking about evidence quality.
02
Forecasting and reporting redesign
Connects quantitative finance, forecasting discipline, event-study logic, and decision-intelligence methods.
03
Market intelligence briefing
Supported by published work on cryptocurrency markets, market effects, behavioural finance, and empirical methods.
04
Sustainability signal review
Grounded in sustainable finance research and ESG information interpretation.
05
Evidence quality checkpoint
Connects empirical research discipline with responsible AI and analytical workflow design.
Start with a focused conversation