Our Work · Reference Architectures on GitHub
Open-source architectures built to prove specific engineering patterns: deterministic arithmetic, evidence trails, human-in-the-loop review. This is how the approach was tested, not a list of things currently available to book. That's ComplyAI and the enablement workshop.
Proof of capability
Five open-source reference architectures. Each build proves the engineering capability that sits underneath ComplyAI, and each is a workflow its governance layer can wrap. Every one targets a specific operational cost or regulatory risk in UK banking and lending. This is the proof, not the offer.
Reduces document extraction from hours to minutes. Reads scanned PDFs and balance sheets directly, routes all financial ratio calculations to a verified Python server, and holds every decision for human review before it is logged.
View codebase & ADRsClears 90%+ of overnight AML alerts automatically, leaving compliance analysts to focus on the cases that genuinely need them. Every cleared alert includes a documented reason a team can review and a regulator can inspect.
View codebase & ADRsCuts regulatory gap analysis from months to minutes. Reads new FCA or PRA policy updates alongside internal policies and produces a structured report with exact citations from both documents. No paraphrasing. No invented references.
View codebase & ADRsReduces commercial loan covenant review cycles from days to minutes. Two AI agents review each clause against a lending policy, challenge each other's reasoning, and only output a final draft when every clause meets the standard. Lawyers review the output, not the raw documents.
View codebase & ADRsReduces KYC beneficial ownership investigations from hours to seconds. The system traces corporate ownership chains through Companies House automatically, layer by layer, until it finds a human. It never invents an entity it cannot verify. Every step is logged for the audit trail.
Architecture Teardown
One of the reference architectures ComplyAI's governance layer wraps, examined in technical depth.
Large Language Models are excellent at semantic extraction, but they cannot do math reliably. In this build, Claude was locked out of arithmetic entirely. Claude extracts the raw figures from scanned balance sheets into strict JSON, then passes those variables to a deterministic Python Model Context Protocol (MCP) server. The Python server calculates DSCR and LTV, returning mathematically perfect results. The result: an auditable process with zero hallucination risk in the arithmetic.
The Pipeline Flow
Illustrative outcomes
Estimates and proof-of-concept measurements from ShiftAi's own synthetic test data and architectural design choices, not live client results.