Case Studies

Production systems in operation

Anonymized enterprise projects showing how we engineer AI systems for production—business challenge, architecture, solution, and operational outcome.

Enterprise IT operations

Production AI service operations agent

Challenge

A service organization handled high request volume against operational knowledge dispersed across ticketing history, documentation, and the experience of senior staff. Triage quality depended on who picked up the ticket.

Approach

Architected a production agent integrated with the ticketing, documentation, and identity platforms. The agent triages and drafts resolutions grounded in approved knowledge, with human approval gates on actions and full audit logging of every step.

Outcome

Consistent, knowledge-grounded triage in production. Every agent action is attributable, reviewable, and constrained to least-privilege access.

AI AgentsProductionHuman Oversight
Regulated enterprise

Enterprise knowledge platform

Challenge

Years of operational history—tickets, documentation, procedures, decisions—were fragmented across collaboration and documentation systems. Answers depended on knowing whom to ask, and access controls made naive search approaches unacceptable.

Approach

Built a permission-aware retrieval platform spanning the organization's collaboration, documentation, and workflow systems. Answers carry citations to approved sources, honor per-document access controls, and every query is logged for governance review.

Outcome

Institutional knowledge is answerable in seconds without weakening access boundaries. The audit trail gives governance teams visibility into how knowledge is accessed.

Knowledge PlatformPermission-Aware RetrievalGovernance
AI platform team

Automated AI evaluation platform

Challenge

An AI system in production had no systematic way to verify behavior before changes shipped. Prompt and model updates were assessed by spot-checking, and regressions surfaced in front of users.

Approach

Engineered an evaluation framework running automated suites of hundreds of realistic enterprise scenarios against every candidate change, with graded scoring, regression comparison, and integration into the release process.

Outcome

AI behavior became measurable. Regressions are caught before deployment, and quality trends are tracked release over release.

AI EvaluationRegression DetectionRelease Engineering
Security organization

Security reporting platform

Challenge

Security posture reporting was assembled by hand from scanning tools, identity systems, and infrastructure inventories. Evidence collection consumed engineering time and reviews worked from stale snapshots.

Approach

Built a reporting platform that continuously ingests findings from security and infrastructure tooling, tracks vulnerabilities and exceptions through their lifecycle, and produces dashboards designed for security reviews and leadership.

Outcome

Continuous, queryable security posture in place of manual evidence assembly. Reviews work from current data, and exception handling has a durable record.

Security AutomationReportingEvidence
Enterprise engineering

Knowledge continuity architecture

Challenge

Critical operational knowledge lived with individual senior staff and in unstructured records. Departures and reorganizations created real continuity risk, and onboarding depended on tribal knowledge transfer.

Approach

Designed an architecture that captures operational knowledge from workflow and documentation systems into a governed, retrievable store—structured for permission-aware access and grounded citation rather than one-off exports.

Outcome

Operational knowledge persists through team changes. New staff work from the same governed knowledge base as veterans, and continuity no longer depends on specific individuals.

ArchitectureKnowledge ContinuityGovernance

Facing a similar engineering problem?

Tell us about your workflows, systems, and operational constraints, and we will discuss how we would approach the architecture.

Book a consult