ENTERPRISE SOLUTIONS

Representative Challenges

Real problems we've tackled—examples of the complexity, constraints, and engineering depth in our work.

Real Engineering Problems

These aren't case studies or marketing narratives. They're examples of the technical challenges we encounter in enterprise AI system development—anonymized but authentic.

Each represents the kind of engineering work we do: systems that must operate reliably under real-world constraints, integrate with existing infrastructure, and meet regulatory requirements.

Multi‑Agent Document Processing with Full Audit Trail

Context

Financial services firm needed to process thousands of regulatory filings daily. Documents arrived in inconsistent formats (PDF, scanned images, structured data). Extracted data fed downstream compliance systems. Every extraction had to be auditable: who extracted what, when, and with what confidence.

Technical Challenges

  • •State had to be versioned and immutable once written (no retroactive changes)
  • •Every extraction decision required confidence scoring and justification
  • •Low-confidence extractions escalated to human reviewers with full context
  • •System had to handle 10,000+ documents/day with sub-5-minute SLAs
  • •Integration with legacy systems (mainframe batch jobs, SOAP APIs)

Solution Approach

Built multi-agent pipeline using Agiorcx for orchestration. Document classifier routed to specialist extraction agents based on filing type. Validation agents cross-checked extractions against business rules. Quality agent assessed confidence and triggered human review when needed.

Every state transition logged to append-only audit store. Integration layer adapted to legacy systems without requiring changes to downstream consumers.

Outcome

System processed production volumes with 94% automated extraction rate (6% escalated to humans). Full audit trail for regulatory review. Team extended system to additional document types after handoff.

Automated Security Code Review for CI/CD Pipeline

Context

Security-conscious organization wanted automated code review integrated into pull request workflow. Needed to catch vulnerabilities, check dependency versions, and enforce secure coding patterns—without slowing down developer velocity.

Technical Challenges

  • •Reviews had to complete in under 3 minutes to fit developer workflow
  • •False positive rate had to stay below 10% or developers would ignore findings
  • •System needed to understand context (not just pattern matching on code)
  • •Integration with GitHub Enterprise, Jira, and Slack for notifications
  • •Support for 8 programming languages with language-specific security rules

Solution Approach

Multi-agent system with parallel review streams. Security agent checked for known vulnerability patterns, dependency agent verified library versions against CVE databases, architecture agent assessed API design and data handling.

Aggregator agent combined findings, deduplicated issues, and prioritized by severity. Low-confidence findings tagged for human security team review rather than blocking PR.

Outcome

Average review time under 2 minutes. False positive rate measured at 8% after tuning. Caught 14 security issues in first month that had passed manual review. Development velocity unchanged—team gained confidence without slowdown.

Permission‑Aware Enterprise Knowledge Search

Context

Healthcare organization had 15+ years of clinical documentation, research notes, and operational procedures scattered across wikis, SharePoint, and file servers. Needed semantic search that respected complex permission hierarchies (role-based, department-based, and patient-record-level access).

Technical Challenges

  • •Search results had to filter based on user's access rights before returning
  • •No accidental exposure of restricted documents in embeddings or context
  • •Sub-second response time for searches across millions of documents
  • •Audit log of who searched for what and which documents were accessed
  • •Support for natural language queries ("What's our protocol for pediatric asthma?")

Solution Approach

Vector search with permission tags baked into index. Query agent interpreted natural language, retrieval agent fetched candidates with user's permission context, reranking agent prioritized by relevance and recency.

Citation agent extracted specific passages and generated source links. All searches logged with user ID, query, and accessed documents for compliance auditing.

Outcome

System handled 10,000+ queries/day with average response time under 800ms. Zero permission violations in 6 months of production use. Reduced time-to-answer for clinical staff by 60% compared to manual search.

Common Themes Across Challenges

While each engagement is unique, certain patterns recur:

  • •Observability is non-negotiable. Teams need to see what agents are doing and why.
  • •Guardrails matter more than accuracy. Systems must fail safely and escalate gracefully.
  • •Integration complexity dominates. The AI part is often simpler than connecting to legacy systems.
  • •Latency constraints are real. If the system is too slow, users won't adopt it.
  • •State management is underestimated. Multi-agent workflows generate complex state that must be tracked, versioned, and debuggable.