Chemical Data & Chemical Informatics
Laboratory results, compound and specification data, process-quality analysis, and governed retrieval for safety and regulatory documents.
Domain Depth Meets Build Capability
Deterministic AI & Data Architecture for Chemistry, Energy, Commodities, and Capital Markets.
We help organisations turn chemical, laboratory, process, and market data into reliable data platforms, valuation tools, and AI-augmented decision-support. From chemical informatics and analytical workflows to energy economics and capital markets, we combine domain expertise with validated data contracts and deterministic calculations to reduce AI-related risk and support traceable, expert-reviewed decisions.
Chemical DataEnergy EconomicsCommoditiesCapital MarketsData PlatformsValuationCompliance-aware AI
What the practice is built to do
Technical systems for high-stakes decisions
The focus is practical delivery: designing how data is ingested, cleaned, validated, structured, analysed, and extended into AI-augmented workflows where control, auditability, and domain accuracy matter.
Data Platforms & Pipeline Engineering
Multi-source ingestion, transformation workflows, streaming logic, and platform-aware engineering that produces analytics-ready data for energy, commodities, and capital markets.
Validation & Data Quality Systems
Rule-based checks, anomaly awareness, and structured validation logic for data that must be trusted before it is used in valuation, trading, or regulatory contexts.
Valuation & Market Intelligence
Deterministic fiscal models, commodity curves, market-data normalisation, and decision-support systems that keep humans accountable to auditable logic.
AI-Augmented Workflows
Governed automation, compliance-aware retrieval, summarisation, and explainable AI-oriented architecture where control, traceability, and auditability matter.
Energy & Commodities
Domain physics, commercial logic, and software execution
From PSC fiscal waterfalls and LNG netbacks to commodity curves and market-intelligence networks, we design systems where energy domain expertise, quantitative rigour, and type-safe engineering meet.
Upstream & Energy Infrastructure
PSC fiscal waterfalls, LNG netbacks, cost-recovery modelling, and ARO/decommissioning forecasting grounded in domain physics.
Commodity & Capital Markets
Real-time data contracts, trade-record normalisation, market-intelligence multi-agent networks, and algorithmic risk guardrails.
Deterministic Valuation Engines
Type-safe fiscal and commercial models that enforce data contracts at every boundary, eliminating hallucination risk from high-stakes capital decisions.
Compliance-aware Retrieval
Regulatory retrieval systems built on authoritative frameworks, with adversarial self-correction and explainable provenance for audit-ready output.
Selected implementation platforms
From domain problem to working system
These repositories demonstrate how the practice moves from domain understanding to production-grade code: deterministic models, validated pipelines, and decision-support interfaces.
ChemData AI Platform
Energy, commodities, and capital-markets tooling: deterministic valuation engines, market-intelligence workflows, compliance-aware retrieval, and Streamlit-based decision-support for scenario testing.
View code on GitHubChemData Asset Integrity Engine
Industrial and engineering data systems for asset integrity, inspection, reliability, maintenance, and risk-aware technical tooling in infrastructure and processing environments.
View code on GitHubTechnical Analytical Project
Scientific, analytical, and data-engineering breadth that demonstrates structured implementation capability and technical depth across multiple applied domains.
View code on GitHubScientific Data Accelerator
Validation-first readiness triage, compute-lane routing, and governed lineage for computational chemistry workflows.
Explore the acceleratorInsights
Thinking drawn from actual project work
Energy Economics & Valuation
Fiscal modelling, PSC waterfalls, and deterministic valuation systems that keep capital decisions accountable to auditable data contracts.
Explore insightsMarket Data & Trading Systems
Trade-record normalisation, market-intelligence workflows, and AI-augmented trading architecture built for explainability and control.
Read moreAI & Architecture Perspectives
Governance, explainability, compliance-aware retrieval, and architecture modernisation for regulated and high-stakes environments.
Explore perspectives Live Production Tooling
Interactive Systems & Analytical Engines
Explore our live, production-grade cloud applications engineered for commodities valuation, regulatory compliance triage, and molecular analytics.
Live Streamlit Suite 100% Operational
ChemData AI Platform — Energy & Capital Markets Suite
Capital Markets & Compliance
Forward curve cashflow discounting (MTM PV), sensitivity price bump modeling, and deterministic scenario compliance triage for REMIT, MAR, MiFID II, and Dodd-Frank.
Valuation Engine: Forward curve discount scheduling & MTM PV
Compliance Checker: Deterministic free-text scenario triage (REMIT/MAR)
Market Intel Pack: Structural energy & power scenario generation
Python 3.12+Forward CurvesREMIT / MARMiFID IIDodd-Frank100% Pytest
Scientific Data & AI Live on Streamlit
ChemData Scientific Control Center — Molecular & Nanomaterials Analytics
Cheminformatics & Nanomaterials
Multiscale cheminformatics platform featuring automated SMILES validation, 2D vector diagrams, WebGL 3D conformers, reaction kinetics, and Nanoparticle DLS size distribution with DLVO Zeta Potential stability modeling.
Validation Engine: SMILES validation, pH/temp bounds, & anomaly scoring
Molecular Visualizer: RDKit 2D diagrams & WebGL 3D conformers
Nanomaterials Analytics: DLS size distribution & DLVO Zeta Potential stability
RDKit3D WebGLDLS Size DistributionZeta PotentialLipid Nanoparticles (LNPs)
QA Test Pack & Compliance Audit Memo
Download our 5-page Institutional QA & Compliance Verification Memo (PDF) with 6 regulatory test scenarios.
Have a valuation, trading-data, or AI architecture challenge worth doing properly?
Start with a conversation. We will look at the problem, the data reality behind it, and whether this practice is the right fit for the work.