Case Studies
Work that made it past the demo.
Representative engagements across banking, manufacturing, SaaS and retail — each one measured against the numbers that mattered to the client, not to us.
Representative engagements across banking, manufacturing, SaaS and retail — each one measured against the numbers that mattered to the client, not to us.
From grounded copilots to edge vision systems — how disciplined engineering turns enterprise AI into an operating asset.
Branches and contact centers now get grounded, cited answers instead of manual lookups — the full story opens the list below.
Staff spent hours searching policies and product documentation, with generic chatbots giving unreliable, uncited answers — unacceptable in a regulated environment.
Built a RAG-grounded copilot with GraphRAG for cross-document reasoning, strict guardrails, PII redaction and an automated evaluation harness run against real branch queries.
A trusted, cited assistant adopted across branches and contact centers, with every answer traceable to source documents.
Purchase orders and compliance documents were processed manually across regional teams, creating delays and inconsistent data quality across the business.
Deployed a document intelligence pipeline combining OCR and layout-aware LLM extraction with agentic validation rules and human-in-the-loop review for exceptions.
Consistent, auditable, straight-through processing at a fraction of the manual effort — over 80% of manual processing tasks automated.
A SaaS provider needed grounded, cited answers over a fast-growing corpus of customer knowledge — at a scale where naive vector search returned noise and costs ballooned.
Engineered hybrid retrieval — vector, keyword and re-ranking — with incremental ingestion, tenant isolation and continuous evaluation against a curated question set.
A multi-tenant retrieval platform serving cited answers in production, with predictable latency and per-query cost as the corpus grows.
The client's questions spanned many documents at once — plain RAG retrieved fragments but could not connect entities, relationships and policies across the corpus.
Advised and prototyped a GraphRAG architecture on Neo4j: entity extraction into a knowledge graph, graph-augmented retrieval layered over the existing vector index, and an evaluation suite comparing both.
Cross-document questions that previously failed now resolve with cited, multi-hop answers — and the client's team owns the architecture and the evidence for it.
Support volume outgrew the team. Scripted bots deflected customers without resolving anything, and complex cases still landed on the same overloaded queue.
Built a multi-agent system with tool-calling into order, billing and CRM systems, strict action guardrails, and confidence-based escalation that hands full context to a human.
Routine tickets resolved end-to-end without human touch; the support team now spends its time on the cases that actually need judgment.
Policy prohibited sending internal data to hosted model APIs — but teams still needed a capable copilot over confidential documents and code.
Deployed quantized open-weight models on the client's own GPUs with vLLM, retrieval over internal stores, role-based access control and full audit logging — all inside their network boundary.
A secure, low-latency copilot that cleared security review on the first pass, with per-token costs a fraction of hosted alternatives.
Visual inspection and shelf audits were manual, slow and sampled — defects and out-of-stocks were found after the fact, when they were expensive to fix.
Trained detection models and vision-language pipelines, optimized for edge hardware, with drift monitoring and a retraining loop fed by production imagery.
Continuous, real-time inspection on the line and on the shelf — issues surfaced in seconds instead of days, without shipping video to the cloud.
Deeper write-ups of representative engagements — the challenge, the architecture and the outcome.
A retrieval-grounded assistant that gives employees accurate, cited HR answers instead of digging through portals.
Public Sector · RAGCited, traceable answers over a large public-sector document archive, where every answer is accountable.
MLOps · ForecastingML forecasting taken from notebooks to a monitored pipeline with automated retraining and drift detection.
Modernization · PlatformAn engine that maps a large monolith and proposes a phased, service-by-service decomposition.
Platform Initiative · GovernanceOur forward view: a context-aware governance runtime for enterprise AI, built on an AI knowledge graph. In active development.
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