Two industries, by deliberate choice. Both share the same operating reality: AI must coexist with model risk frameworks, audit obligations, and compliance functions whose tolerance for hallucinated outputs is zero. The depth of that constraint is the gap most consultancies cannot work inside. We focus narrowly because the alternative — claiming general expertise across every industry — is the position that makes the senior buyer in this kind of firm immediately suspicious.
Banking, insurance, asset management, capital markets. Where AI must coexist with model risk management, audit trail requirements, and the tolerances of CRO and CCO functions.
Your AI deployment lives inside a model risk management framework, on systems older than the cloud, with change windows measured in quarterly releases. The work that actually lands here is data engineering, governance, and integration with risk and compliance functions — not the headline projects on every consultancy's brochure.
Short list per industry. For the full set organised by offering (overlay, data engineering, co-pilot products), see what we do →
A first engagement with a new client is usually scoped to a single workflow within a single business unit. Discovery maps the workflow, agrees the architecture, and surfaces anything that should change the scope. Build follows in agile two-week iterations against the discovery output. Support is included from production deployment. Multi-year retainers follow when the work justifies them — and we have walked away from extensions where it did not.
Discuss a financial services engagementPharmaceutical manufacturing, process industrials, and other process-driven sectors where systems are deeply integrated, change is expensive, and uptime is the first KPI.
Your validated systems are not optional. Every change runs through validation, change control, and audit evidence. Change windows are measured in hours, not weekends. The consultancies that work in process manufacturing are mostly Six Sigma firms; the consultancies that work in AI are mostly software firms. We sit between them.
Short list per industry. For the full set organised by offering (overlay, data engineering, co-pilot products), see what we do →
A first engagement with a new client is usually anchored to a single line or a single submission. Discovery establishes scope and validation envelope. Build delivers a working integration with the existing manufacturing-execution and quality systems in agile two-week iterations, with releases timed around mandated change-control windows. Complex integration topologies extend the build. Multi-year integrations follow when the depth justifies them — and we have walked away from extensions where it did not.
Discuss a process-driven enterprises engagement