We design, build, and deploy production-grade LLM and agentic AI systems for enterprise teams.
We help enterprise teams move from AI experiments to production-grade LLM systems that deliver measurable business value.
Multi-agent orchestration systems using LangGraph, tool-using agents, and human-in-the-loop patterns. We design agent architectures that are reliable, observable, and ready for production.
Enterprise retrieval-augmented generation with vector databases, hybrid search, chunking strategies, and grounding pipelines that give your LLMs accurate, source-cited answers.
Systematic benchmarking, guardrails, prompt engineering audits, and team enablement. We help you measure what matters and build internal AI capability that lasts.
We build LLM and agentic AI systems for teams operating in regulated, data-rich, and operations-heavy environments.
LLM agents for compliance, fraud detection, and customer intelligence.
Process intelligence, quality systems, and operational AI.
Clinical workflow automation, document AI, and knowledge retrieval.
Internal AI platforms, developer tooling, and agentic productivity systems.
Real-world AI systems we've designed and built for enterprise clients.
Real estate analysts spent 3-5 hours per property manually extracting data from appraisal PDFs. Standard text chunkers destroyed table formatting, valuation grids, and comparable sales data — making automated Q&A unreliable.
Built a layout-aware RAG pipeline with custom PDF table extraction, semantic chunking that preserves document structure, ChromaDB vector store with metadata filtering, and Gemini 1.5 Pro for grounded multi-turn Q&A — all deployed on GCP with FastAPI.
70% reduction in appraisal review time. Instant comparable sales lookup across 500+ documents. Multi-hour analyst workflows compressed to sub-minute AI queries. Zero table extraction errors vs. 15% manual error rate.
High-volume semiconductor fab losing $2M+ annually to late defect detection. Yield loss discovered post-process, scrap rates climbing, and engineering teams spending hours triaging root causes manually across multiple tool sets.
Built a real-time ML pipeline integrating inline sensor data, SPC signals, and process monitoring into an anomaly detection + yield prediction system. Models ran directly within MES workflows, feeding early warnings into engineering dashboards and capacity planning tools.
~40% scrap rate reduction. Engineering triage time dropped from 4+ hours to under 15 minutes. Predictive models adopted into weekly capacity planning, preventing 3 major excursion events in the first quarter.
Compliance teams spent 600+ hours per quarter manually reviewing policy documents, regulatory updates, and cross-jurisdictional requirements. Error-prone, unscalable, and consistently behind on new regulations.
Built a multi-agent LLM system using LangGraph that ingests regulatory documents, extracts specific obligations, cross-references internal policy gaps, and generates audit-ready compliance summaries with inline citations and confidence scores.
80% reduction in manual compliance review. Real-time policy gap detection across 12 jurisdictions. Audit preparation compressed from 3 weeks to 2 days. Flagged 23 previously undetected compliance gaps in first month.
EdTech platform with 50,000+ learners struggling with 40% dropout rates. Static course content couldn't adapt to different skill levels, and instructors were overwhelmed answering repetitive questions.
Built a RAG-powered learning assistant that detects learner proficiency in real-time, adapts explanations accordingly, answers curriculum-grounded questions with source citations, and maintains a knowledge gap tracker across sessions.
60% reduction in repetitive instructor Q&A. Course completion rates increased 28%. Scalable personalized learning serving 10,000+ concurrent users with sub-2-second response times.
Research teams manually reviewed 300+ clinical trial documents, patient records, and journal papers per study. Extraction was slow, summarization inconsistent, and critical findings were routinely missed.
Designed a document AI pipeline combining LLM-powered entity extraction, semantic chunking for medical terminology, retrieval-augmented summarization, and multi-layer hallucination guardrails with mandatory source citation.
75% faster document review cycles. Researchers query across 8,000+ documents in seconds. Extraction accuracy exceeded manual review by 12%. Zero hallucination incidents post-deployment with citation verification.
2,000-person enterprise losing 45 minutes per employee per day searching across Confluence, Notion, Slack, Google Drive, and internal wikis. Institutional knowledge was siloed, undiscoverable, and decaying.
Built an agentic knowledge retrieval system with LangGraph that connects to 5+ internal sources, performs multi-hop reasoning across documents, resolves conflicting information, and returns grounded answers with direct source links and freshness scores.
65% less internal search time. New employee onboarding reduced from 6 weeks to 3. Knowledge retrieval accuracy 4x better than keyword search. Adopted by 87% of engineering org within first month.
National distributor losing $4.5M annually from inventory mismatches. Static demand models couldn't capture seasonal shifts, regional trends, or SKU-level volatility — leading to chronic overstock in some regions and stockouts in others.
Built an ensemble forecasting engine (XGBoost + LSTM) trained on 2.4M historical order records with 90-day rolling horizon. SKU-level granularity with regional segmentation, promotional event detection, and automated re-order point recommendations.
55% improvement in forecast accuracy (MAE: 3.1%). Overstock carrying costs reduced by $1.8M. SKU-level demand signals integrated into weekly supply planning cycles. Identified 3 previously invisible demand patterns.
Mid-size insurer processing 15,000+ claims/month with 45-minute average manual triage time. High error rates (8%), growing customer complaints about wait times, and 6 FTEs dedicated solely to initial claim routing.
Built an LLM-powered claims pipeline: Document AI extracts claim details from photos/PDFs, damage assessment model scores severity, risk scoring engine routes low-risk claims to auto-approval, and flags complex cases for human review with pre-filled summaries.
85% faster processing (45 min → 6.5 min avg). 73% of claims auto-approved with 99.2% accuracy. Customer satisfaction up 32%. Freed 4 FTEs for complex case handling. $2.1M annual operational savings.
Legal department reviewing 200+ vendor contracts per quarter. Each review took 4.5 hours: manual clause identification, cross-referencing exhibits, and risk flagging. Missed conflicts led to $800K in avoidable exposure in one fiscal year.
Built an LLM-powered contract analysis platform that extracts all clause types, detects inter-clause conflicts automatically, scores risk on a 5-tier scale, cross-references exhibits and addenda, and generates a structured review memo with flagged items and suggested language.
90% faster contract review (4.5 hrs → 27 min). Automatic risk flagging caught 100% of inter-clause conflicts vs. 71% manual detection rate. Standardized review output across 8-person legal team. Zero missed high-risk clauses post-deployment.
E-commerce platform with 500K+ SKUs where keyword search returned irrelevant results for 35% of queries. Customers abandoned searches 2.3x more often than industry average, directly costing $3M+ in annual lost revenue.
Built a semantic search pipeline: query embedding with fine-tuned sentence transformers, vector retrieval from Pinecone, cross-encoder reranking for precision, and personalized result boosting based on user behavior signals and purchase history.
45% increase in search-to-purchase conversion. Average order value up 18%. Search abandonment cut by 52%. Natural language queries ("warm jacket for hiking in rain") now return relevant results. Revenue uplift: $1.4M in first quarter.
Wind farm operator averaging 4 unplanned turbine failures per quarter at $200K+ per incident. Purely reactive maintenance schedule with no predictive capability. Crew dispatch took 6+ hours after failure detection.
Built a predictive maintenance system ingesting vibration, thermal, and SCADA sensor data. Anomaly detection model identifies degradation signatures 48-72 hours before failure. Automated severity scoring triggers crew alerts with specific maintenance recommendations.
60% reduction in unplanned downtime. 48-hour average advance warning window. Prevented 11 major failures in first year. $1.2M annual savings. Crew response time cut from 6 hours to 45 minutes with pre-diagnosed work orders.
A structured engagement model that takes you from idea to production deployment, with clarity at every stage.
We audit your current workflows, data sources, and AI readiness. We identify the highest-impact use cases and define success criteria before writing any code.
We architect and implement your LLM system — agents, retrieval pipelines, evaluations, and integrations — with production-grade engineering from day one.
We ship to production with monitoring, guardrails, and observability built in. Then we help your team own and extend the system independently.
Founder, DigiFab AI
Former Staff AI Software Engineer at Intel with a PhD in Electrical Engineering. Dr. Berkun has built production machine learning and LLM systems for enterprise environments and teaches AI, deep learning, and generative AI to over 380,000 learners through LinkedIn Learning and universities including Columbia, Portland State, and Michigan State.
DigiFab AI is her consultancy focused on helping enterprise teams design, build, and deploy production-grade LLM and agentic AI systems that deliver real business outcomes.
Dr. Berkun teaches AI, deep learning, and generative AI to a global audience through LinkedIn Learning and top universities.
If your team is exploring LLM adoption or struggling to move AI projects into production, we should talk.
Technical leaders and engineering teams building internal AI products who need expert guidance on architecture, evaluation, and production readiness.
CTOs, VPs of Engineering, and AI leads evaluating LLM strategies who need a trusted partner to cut through hype and build what actually works.
Organizations in logistics, insurance, healthcare, and supply chain that need AI agents to automate complex, document-heavy workflows at scale.
The DigiFab team didn't just build us an AI system. They helped us rethink how our entire team works with data. The RAG pipeline they designed cut our document review time by 70% and is now core to our daily operations.
We went from months of manual compliance review to days. The multi-agent system DigiFab built understands regulatory nuance better than most of our junior analysts. It's become indispensable.
Our engineering team had been stuck for months trying to get our AI prototype to production. The DigiFab team came in, restructured the architecture, and we shipped in six weeks. Night and day difference.
The yield prediction models DigiFab built are now part of our weekly capacity planning. Early defect signals reduced our scrap rate significantly. Their manufacturing AI expertise is rare and invaluable.
They built a learning assistant that adapts to each student's level. Course completion rates jumped, support tickets dropped 60%, and our learners love it. Exactly what we needed.
Our clinicians were drowning in document review. The AI pipeline DigiFab designed reduced review time by 75% and gave our researchers instant access across thousands of papers. Transformative.
The DigiFab team didn't just build us an AI system. They helped us rethink how our entire team works with data. The RAG pipeline they designed cut our document review time by 70% and is now core to our daily operations.
We went from months of manual compliance review to days. The multi-agent system DigiFab built understands regulatory nuance better than most of our junior analysts. It's become indispensable.
Our engineering team had been stuck for months trying to get our AI prototype to production. The DigiFab team came in, restructured the architecture, and we shipped in six weeks. Night and day difference.
The yield prediction models DigiFab built are now part of our weekly capacity planning. Early defect signals reduced our scrap rate significantly. Their manufacturing AI expertise is rare and invaluable.
They built a learning assistant that adapts to each student's level. Course completion rates jumped, support tickets dropped 60%, and our learners love it. Exactly what we needed.
Our clinicians were drowning in document review. The AI pipeline DigiFab designed reduced review time by 75% and gave our researchers instant access across thousands of papers. Transformative.
We work with a select number of enterprise clients at a time to ensure deep, focused engagement. Let's explore how LLM and agentic AI systems can transform your operations.
