Real problems. Production solutions.

Every use case below has been deployed in production.

Use cases: 15
Industries served: 5
Average time to production: 7 wk
Deployed in client cloud: 100%

Browse by industry

Industry

Solution

Legal, RAG Pipeline

6 weeks to production

Contract Review Time Cut from Days to Minutes

A 40-person legal team processing 300+ vendor contracts per month across multiple jurisdictions.

View challenge and solution

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.
Stack: RAG, Vector DB, Claude API, AWS

reduction in review time: 85%
saved per attorney/week: 14 hrs
to production: 6 weeks

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Legal, RAG Pipeline

4 weeks to production

M&A Due Diligence Accelerated by 70%

A boutique M&A advisory firm handling 4–6 deals per year, each involving 2,000–5,000 documents across multiple jurisdictions.

View challenge and solution

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.
Stack: RAG, Pinecone, Claude API, Azure

faster document review: 70%
ROI vs no AI strategy: 3.9x
to full data room searchability: 48 hrs

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Biotech, AI Agents

8 weeks to production

Clinical Trial Data Extraction Automated at 99.5% Accuracy

A 200-person life sciences company running 3 active clinical trials, generating thousands of unstructured PDFs and lab instrument exports per quarter.

View challenge and solution

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.
Stack: AI Agents, Claude API, AWS Bedrock, Python

extraction accuracy: 99.5%
saved per quarter: 15K hrs
reduction in processing cost: 73%

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Biotech, RAG Pipeline

6 weeks to production

Drug Discovery Literature Review From Weeks to Hours

A preclinical research team at a San Diego biotech evaluating 5+ potential targets simultaneously, each requiring continuous literature surveillance.

View challenge and solution

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.
Stack: RAG, Vector DB, PubMed API, AWS

faster literature synthesis: 10x
research time reclaimed: 40%
avg target-to-preclinical with AI: 18 mo

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Biotech, Workflow Automation

8 weeks to production

IND Application Assembly Time Cut by 60%

A clinical-stage biotech preparing its first IND submission, with regulatory affairs team of 4 managing document compilation across 12 functional departments.

View challenge and solution

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.
Stack: Workflow Automation, LLM, SharePoint API, AWS

reduction in assembly time: 60%
submission prep cycle: 6→2 mo
cross-reference accuracy: 100%

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Healthcare, Workflow Automation

8 weeks to production

Prior Authorization Processing Time Reduced 70%

A regional healthcare group with 80 providers processing 800+ prior authorization requests per week across 15 commercial payers.

View challenge and solution

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.
Stack: AI Agents, HL7 FHIR, EHR Integration, AWS

faster processing time: 70%
fewer PA denials: 22%
requests handled per FTE: 3x

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Healthcare, LLM Development

10 weeks to production

SOAP Note Generation Cuts Documentation Time by 50%

An independent oncology practice with 12 physicians, each spending 2–3 hours daily on clinical documentation after patient hours.

View challenge and solution

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.
Stack: LLM Fine-tuning, Azure OpenAI, HIPAA-compliant, FHIR

documentation time saved: 50%
reclaimed per physician/day: 2 hrs
physician satisfaction score: 94%

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Defense, RAG Pipeline

10 weeks to production

Technical Specification Search Deployed On-Premise, Air-Gapped

A defense contractor maintaining a 40,000+ page library of technical specifications, maintenance manuals, and requirements documents for a multi-platform program.

View challenge and solution

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.
Stack: RAG, Llama 3, On-premise GPU, Air-gapped

faster requirement traceability: 4x
on-premise, no external APIs: 100%
documents indexed: 40K+

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Defense, AI Agents

8 weeks to production

SOC Alert Triage Time Reduced 65% With LLM Context Analysis

A defense contractor's internal security operations center handling 10,000+ daily alerts across classified and unclassified networks.

View challenge and solution

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.
Stack: AI Agents, Llama 3, On-premise, SIEM Integration

reduction in triage time: 65%
false positive deflection: 80%
avg analyst response time (was 47): 12 min

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Fintech, LLM Development

12 weeks to production

Fraud Detection Enhanced With Transaction Narrative Analysis

A Series C fintech processing 500K+ transactions per day, with a rule-based fraud system generating 40% false positive rates.

View challenge and solution

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%.
Stack: LLM Fine-tuning, XGBoost, AWS SageMaker, Real-time inference

fewer false positives: 40%
inference latency: <50ms
increase in novel fraud caught: 28%

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Universal, AI Agents

6 weeks to production

Customer Support Agent Resolves 75% of Tickets Without Human Escalation

A B2B SaaS company with 5,000 customers, handling 2,000+ support tickets per month with a 6-person support team.

View challenge and solution

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.
Stack: AI Agents, Claude API, Zendesk API, AWS

tickets resolved without escalation: 75%
avg first response time (was 4 hrs): 4 min
cost per resolution (was $11): $4.10

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Universal, RAG Pipeline

6 weeks to production

Internal Knowledge Search Replaces "Ask a Colleague" for 800-Person Org

A professional services firm with 800 staff, 10 years of institutional knowledge spread across SharePoint, Confluence, email archives, and completed project files.

View challenge and solution

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.
Stack: RAG, SharePoint/Confluence APIs, Slack Integration, AWS

faster new hire ramp-up: 40%
saved per senior staff/week: 6 hrs
return per $1 invested: $3.70

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Universal, AI Agents

5 weeks to production

Sales Qualification Agent Increases Qualified Pipeline 25%

A B2B SaaS company with 15 AEs, receiving 500+ inbound leads per month. Response time averaged 6 hours. Weekend leads went uncontacted until Monday.

View challenge and solution

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.
Stack: AI Agents, HubSpot/Salesforce API, Claude API, Zapier

time-to-first-contact (was 6 hrs): <5 min
increase in qualified pipeline: 25%
reduction in rep prep time: 65%

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Universal, AI Agents

4 weeks to production

HR Helpdesk Agent Deflects 80% of Tier-1 Queries Instantly

A 600-person company with a 4-person HR team fielding 300+ routine employee questions per month alongside strategic HR work.

View challenge and solution

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.
Stack: AI Agents, Slack API, Claude API, HRIS Integration

of queries resolved instantly: 80%
HR team time freed per month: 200 hrs
better 90-day retention: 30%

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Universal, Workflow Automation

6 weeks to production

AP Invoice Processing Automated — 3-Way Match in Seconds

A distribution company processing 1,200 vendor invoices per month. 4-person AP team spending 80% of time on manual data entry and exception handling.

View challenge and solution

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.
Stack: Workflow Automation, OCR/LLM, ERP Integration, AWS

reduction in manual processing: 85%
avg processing time (was 6 days): 1 day
ROI vs traditional automation: 8:1

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