Delivery patterns

A professional services firm processing high volumes of client financial documents faced a persistent manual bottleneck. Staff were transcribing structured data from incoming documents — invoices, statements, and tax filings — into internal systems by hand. The work was repetitive and rule-bounded, but error-prone and scaling with headcount rather than revenue. An architecture review confirmed process fit and data readiness before any build commitment. The goal was to automate extraction and entry end-to-end, with validation gates before any data reached the system of record.

A B2B SaaS platform serving regulated-industry clients needed internal knowledge retrieval exposed to end users across multiple tenants. The client came with an existing retrieval prototype. An architecture review identified access control at the query layer — not retrieval quality — as the critical production risk before go-live. Documents carried role and tenant-level permissions that a standard RAG implementation would not respect. Post-filtering was insufficient: it couldn't prevent cross-tenant context from influencing generated responses. The system had to enforce boundaries at the index query layer, before any content reached the model.

An operations team managing high-volume, multi-step workflows across several internal tools wanted to automate a class of repetitive decisions — while retaining human review for actions above a defined risk threshold. A workflow discovery scoped the automation boundary before architecture work began — defining what the agent could execute autonomously, what required approval, and what had to fail deterministically rather than degrade silently.

A B2B SaaS sales team was spending significant SDR time on manual prospect research — ICP qualification, company enrichment, contact data entry, and CRM hygiene. An architecture review scoped the research and enrichment loop before build began, defining what the agent could automate fully versus what required human approval before any CRM write or outreach trigger. The research loop was repetitive but judgment-dependent: some enrichment could be fully automated, but outreach decisions required human review.

A team had shipped an AI-powered feature to production but had no alerting, no per-workflow cost visibility, and a deployment process that required manual steps with no rollback capability. The first time quality degraded, it was reported by a customer — not surfaced by the system. An architecture review confirmed the gap: the AI system was live but the operational infrastructure that should have surrounded it from day one was absent.

A team had built an AI assistant handling customer and internal data across multiple tenants. The system had never been tested adversarially — no prompt injection testing, no cross-tenant access validation, no review of how data sensitivity was handled at the retrieval layer. A Workflow Discovery assessment surfaced critical and high-severity security findings. The engagement scope was to test, document, and close the gaps before the system was exposed to a broader user base.