Patterns we deliver to production.

Ingestion pipeline across 8 to 15 source systems, semantic and lexical hybrid retrieval, permission-aware access (ACLs preserved from source), agentic interfaces per role (engineering copilot, sales copilot, support copilot), evaluation harness with golden questions per department.

Answer precision on golden set, query latency, deflection rate from human channels, weekly active users per department, hallucination rate against ground truth.

An internal endpoint queried by Slack, IDE plugins, CRM sidebars, and a web UI; usage dashboards per business unit; a content team owning the golden set.

Normalized event schema across heterogeneous sources, detection-as-code repository, AI-assisted detection authoring and tuning, alert triage agent with explainability, feedback loop from analyst verdicts back into detection quality.

Mean time to detect, mean time to triage, false positive rate, analyst minutes per alert, coverage against MITRE ATT&CK or equivalent framework.

Detections versioned in Git, CI/CD pipeline running test traffic, agent triaging tier-1 alerts with human override, weekly tuning loop owned by your detection engineering team.

Ingestion across canonical and edge sources, schema mapping with versioning, deterministic and probabilistic matching (record linkage), LLM-assisted reconciliation for ambiguous cases, human-in-the-loop review queue, lineage tracking, golden record service.

Duplication rate before and after, match precision and recall against a labeled set, reconciliation throughput, downstream system error reduction, reviewer minutes per ambiguous case.

A golden record API consumed by downstream systems, a reviewer console for edge cases, lineage queryable from any record, weekly data quality scorecard.

Fleet orchestration plane, task graph definition and versioning, simulation-to-production promotion pipeline, telemetry and behavior recording, safety envelope and intervention protocols, operator console, incident replay, model and policy versioning per unit.

Units in production, autonomy rate (% of tasks completed without human intervention), mean time between interventions, simulation-to-production accuracy, rollback time, safety incident rate.

A control plane with operator UI, a sim environment that mirrors production, a per-unit deployment pipeline, an on-call rotation that includes humans who can override any agent.

AST-level code understanding across the legacy codebase, behavioral test suite generated from production traffic, the slice itself in the target stack, strangler-pattern routing, parity verification, rollback path.

Parity rate against legacy on production traffic, latency and error rate against legacy baseline, build minutes saved, lines of legacy retired.

New slice running in parallel with old, traffic shifted incrementally, dashboards showing parity per endpoint, a documented playbook your team uses for the next five slices.

Layout-aware extraction with confidence scoring, schema validation, cross-document reconciliation, exception queue with reviewer UI, decision rules layer, full audit trail per document.

Straight-through processing rate, exception rate by document type, reviewer time per exception, decision accuracy on a labeled set, time from receipt to decision.

An intake endpoint, a reviewer console, a decisions API consumed by your core system, an audit log per document, monthly model and rules review.

Agent runtime with tool permissions, system-of-record connectors (read and write with full audit), policy layer, approval workflows for sensitive actions, evaluation harness with replayable runs, observability per agent step.

Operator minutes saved per case, agent action success rate, escalation rate, error rate by action type, audit completeness.

Agents running inside ServiceNow / Salesforce / SAP / Epic / equivalent, every action logged and reversible, a policy file under version control, weekly review of escalations.

Tool selection and pilot (Cursor / Copilot / Claude Code / Windsurf / Cline matrix per role), security posture (data residency, code exfiltration, prompt logging), evaluation harness, internal training and pairing program, productivity telemetry, governance policy.

PR throughput per engineer, cycle time, review time, defect rate post-adoption, tool spend per engineer, adoption rate by team.

A written AI usage policy, an enabled tool stack with SSO and audit, a dashboard showing impact by team, a quarterly review owned by your engineering leadership.