Real problems.
Production solutions.

Challenge

Attorneys spent 6–8 hours per engagement manually reviewing contracts for risk clauses, indemnification language, and compliance issues. Senior attorney time was consumed by work that required pattern recognition, not legal judgment.

Approach

RAG pipeline over 10,000+ historical contracts and playbooks. Attorneys query in plain English — system retrieves relevant clauses, flags deviations from standard terms, and cites exact source documents with page references.

Challenge

Every deal required weeks of paralegal time manually reviewing NDAs, employment agreements, IP assignments, and regulatory filings. Deal timelines were stretched by document review bottlenecks.

Approach

Deal-specific RAG deployment per transaction. Document ingestion on day one, enabling natural language queries across the full data room. Risk flags surfaced automatically; attorneys review exceptions only.

Challenge

Data management teams spent 15,000+ staff hours per quarter manually extracting structured data fields from clinical trial reports. Human error rates required costly QC cycles before regulatory submission.

Approach

Multi-step agent pipeline: ingest PDFs → extract structured fields → validate against clinical schema → flag anomalies → route exceptions for human review. Deployed on AWS within the client's existing GxP-compliant environment.

Challenge

Scientists spent 40%+ of their time reading papers and synthesizing evidence across PubMed, internal assay data, and patent filings. Target prioritization decisions were delayed by weeks waiting for literature reviews.

Approach

RAG over internal research corpus + live PubMed integration. Scientists query across thousands of papers, patents, and internal experimental data simultaneously. Structured evidence summaries generated on demand per target.

Challenge

Regulatory affairs spent 3–4 months collecting, formatting, and cross-referencing documents from CMC, clinical, pharmacology, and toxicology teams. Version control and traceability were managed manually in SharePoint.

Approach

AI workflow that pulls source documents from departmental repositories, applies CTD formatting rules, checks cross-references, flags missing sections, and generates submission-ready templates. Human team reviews and certifies final output.

Challenge

Staff manually cross-referenced patient records, payer-specific criteria, and clinical guidelines for each request. Denial rates were high due to documentation gaps. Physicians were pulled into admin work to provide clinical justification.

Approach

AI automation layer ingests incoming PA requests, matches against payer rule database, auto-populates clinical documentation from EHR, and routes only edge cases to staff with pre-drafted clinical rationale.

Challenge

Physicians spent evenings completing notes, leading to burnout and reduced patient capacity. Standard ambient AI tools didn't understand oncology-specific terminology, staging criteria, and treatment protocols.

Approach

Fine-tuned LLM on oncology clinical notes corpus. Ambient audio capture during patient visits → structured SOAP notes in specialty-specific format → physician reviews and signs. Deployed on Azure with BAA in place. No PHI leaves the client environment.

Challenge

Engineers spent hours searching for requirements buried in spec documents. Traceability matrices were maintained manually. New program staff took months to become productive on the document ecosystem.

Approach

Fully on-premise RAG using open-weight LLMs (Llama 3) on client GPU infrastructure. Zero external API calls. Full audit logging per DFARS requirements. Engineers query across the full document corpus in plain English.

Challenge

Analysts spent 70%+ of their time triaging false positives. Alert fatigue caused real threats to be deprioritized. Rule-based SIEM couldn't understand narrative context of incidents — only pattern matching.

Approach

LLM-based triage layer reads alert context, enriches with threat intel feeds, classifies severity, generates analyst-ready summaries, and prioritizes queue. Deployed entirely on-premise with no data leaving the secure environment.

Challenge

Rule-based system flagged legitimate transactions while missing novel fraud patterns. Analyst team overwhelmed with manual review queues. International expansion increased fraud surface area faster than rules could be updated.

Approach

LLM layer reads transaction narrative context (merchant category, description, timing, behavioral pattern) to catch inconsistencies that numeric models miss. Sub-50ms inference via two-stage pipeline: fast XGBoost for 95% of transactions, LLM deep-scan for flagged 5%.

Challenge

Tier-1 tickets consumed 70% of team capacity, leaving complex issues underserved. Response times averaged 4 hours. After-hours coverage was non-existent, frustrating international customers.

Approach

AI support agent trained on product documentation, historical ticket resolutions, and escalation patterns. Integrated with Zendesk. Handles tier-1 autonomously, routes tier-2 with full context summary, escalates tier-3 with draft response.

Challenge

New staff took 6+ months to become productive. Senior staff spent hours per week answering knowledge questions. Critical decisions were made without awareness of relevant past work or existing processes.

Approach

RAG over the full knowledge corpus — policies, past projects, templates, guidelines. Staff ask questions in plain English and get answers with citations. Updated nightly. Slack integration for in-workflow access.

Challenge

Reps spent 65% of time on research, CRM updates, and email drafting before their first conversation. Lead quality varied wildly — reps often discovered disqualifying factors after 2–3 meetings.

Approach

AI qualification agent contacts inbound leads within 5 minutes, asks qualifying questions via email/chat, researches company context, scores lead against ICP criteria, and books only qualified meetings. CRM updated automatically.

Challenge

HR team spent 40% of time answering repetitive questions about PTO policies, benefits, onboarding steps, and payroll. New hires felt unsupported waiting hours for basic answers.

Approach

HR knowledge agent trained on employee handbook, benefits documentation, and HR policies. Deployed in Slack. Instant answers with citations. Escalates complex or sensitive queries to HR staff with full context.

Challenge

Invoice processing took 5–7 days average. 15% exception rate required manual vendor follow-up. Month-end close was delayed by unprocessed invoices. Early payment discounts were consistently missed.

Approach

AI ingests invoices (PDF, email, EDI), extracts fields, performs 3-way PO match, routes exceptions with context to approvers, and posts matched invoices directly to ERP. Vendor communication automated for standard exceptions.