FLOW MASON

Real-World AI Workflows

Not vaporware demos. These are production patterns we've seen work, with honest analysis of what's required to implement them.

A note on honesty

We're not going to claim "90% cost savings in 6 weeks." Real enterprise AI deployments take 3-6 months, require significant integration work, and need human oversight. The value is real, but so is the effort. Here's what actually works.

All metrics on this page are illustrative, based on patterns we've observed and industry research. They are not guarantees or predictions for your specific environment. Your results will depend on your data, systems, team, and implementation approach.


Healthcare

Clinical Trial Patient Matching

The Real Problem

Academic medical centers run 100-300 active trials. Coordinators manually read clinical notes to find eligible patients. Match rate: 2-5%. A single trial coordinator can screen maybe 50 patients/week against 10-15 trials. Most eligible patients are never identified. Trials miss enrollment, timelines slip, drug approvals delay by months.

What FlowMason Enables

  • • Extract structured data from unstructured clinical notes
  • • Evaluate all trials in parallel (not one at a time)
  • • Handle incomplete records gracefully (flag missing data, don't fail)
  • • Generate patient-friendly recruitment materials

Realistic Expectations

Metric Before After Notes
Patient match rate 2-5% 8-15% Still needs coordinator validation
Screening throughput 50/week 500+/week Per coordinator
Trials evaluated 10-15 All active Per patient
Time to first match Days Hours After patient enters system

Implementation Reality

Timeline 6-9 months (HIPAA compliance, EMR integration, IRB approval)
Integrations Epic/Cerner EMR, CTMS (OnCore, Velos), trial registry databases
Compliance HIPAA BAA, IRB protocol amendments, data use agreements
Model On-prem or healthcare-specific LLM for PHI (not public Claude/GPT)

Critical Limitation

AI cannot make enrollment decisions. It surfaces candidates for human review. A coordinator must verify eligibility, and a physician must consent the patient. FlowMason accelerates the funnel, it doesn't replace clinical judgment.

Pipeline Pattern
patient_record
    │
    ├── extract_clinical_data
    │   ├── diagnoses (ICD codes)
    │   ├── medications (RxNorm)
    │   ├── lab_values
    │   └── procedures
    │
    ├── foreach: active_trials (parallel)
    │   ├── check_inclusion_criteria
    │   ├── check_exclusion_criteria
    │   └── calculate_match_score
    │
    └── trycatch: missing_data
        ├── success → rank_matches
        └── error → flag_for_manual_review

The FlowMason Advantage

The trycatch pattern is critical here. Clinical records are messy—missing labs, incomplete histories, inconsistent terminology. Rather than failing on bad data, FlowMason gracefully degrades: flag what's missing, score with available data, route uncertain cases to humans.


Financial Services

Investment Research & Analysis

The Real Problem

Buy-side analysts cover 50-80 companies each. Reading a single 10-K takes 3-4 hours. Earnings transcripts, another hour. Competitor analysis, market data, news—it adds up. Analysts spend 70% of time on data gathering, 30% on actual analysis. Coverage is shallow and opportunities are missed because there aren't enough hours.

What FlowMason Enables

  • • Extract key metrics, guidance, and risk factors from filings
  • • Compare management commentary across quarters (what changed?)
  • • Generate structured research summaries in your firm's format
  • • Route different company types through sector-specific analysis

Realistic Expectations

Metric Before After Notes
10-K review time 3-4 hours 30-45 min AI extracts, analyst validates
Companies covered 50-80 150-200 Shallow + deep coverage mix
Data gathering time 70% 30% More time for actual analysis
Earnings reaction Next day Same day Faster initial read

Implementation Reality

Timeline 2-4 months (lighter compliance burden than healthcare)
Integrations SEC EDGAR, Bloomberg/FactSet, internal research DB
Training Calibrate to firm's research format and terminology
Compliance Information barriers, research independence policies

Important Caveat

AI doesn't generate alpha. It frees analysts to spend more time on differentiated analysis that does. The value is leverage: same team, broader coverage, faster reaction. Don't expect the AI to find insights humans would miss.

Pipeline Pattern
company_filing
    │
    ├── extract_financials
    │   ├── revenue_breakdown
    │   ├── margin_trends
    │   ├── guidance_changes
    │   └── risk_factors
    │
    ├── conditional: sector_routing
    │   ├── tech → r&d_analysis
    │   ├── retail → same_store_sales
    │   ├── healthcare → pipeline_valuation
    │   └── default → standard_analysis
    │
    ├── compare_to_prior_quarter
    │
    └── generate_research_memo
        └── firm_template_format

Cost Comparison

Manual: Analyst at $300K/yr covers 60 companies = $5,000/company/year

FlowMason: Same analyst covers 150 companies = $2,000/company/year

API costs: ~$2-5 per filing processed (Claude for analysis)


E-commerce & Retail

Intelligent Customer Service

The Real Problem

Large retailers handle 20,000-100,000 tickets monthly. Current chatbots are rule-based and handle only 30-40% of inquiries. The rest go to agents at $15-25/hour who average 8-12 tickets/hour. Complex issues (damaged items, billing disputes) take 20+ minutes each. Customer satisfaction hovers at 70-75%.

What FlowMason Enables

  • • Intelligent classification beyond keyword matching
  • • Automated responses for simple inquiries (with order lookups)
  • • Human-in-the-loop for complex issues (AI drafts, agent approves)
  • • Sentiment-aware escalation for angry customers

Realistic Expectations

Metric Before After Notes
Automation rate 30-40% 55-70% Simple inquiries only
Agent tickets/hour 8-12 15-22 AI assists with drafting
First response time 4-24 hours 5 min (auto) / 2 hr (human) Depends on complexity
CSAT 70-75% 78-85% Faster + more consistent

Implementation Reality

Timeline 2-3 months (existing help desk integration is key)
Integrations Zendesk/Freshdesk, order management, returns system, CRM
Training 500-1000 labeled tickets to tune classification
Ramp-up Start with order status only, expand categories over time
Pipeline Pattern
customer_inquiry
    │
    ├── classify_intent
    │   └── [order_status, return, complaint, product_qa, billing]
    │
    ├── analyze_sentiment
    │   └── [positive, neutral, frustrated, angry]
    │
    ├── conditional: routing
    │   ├── simple + neutral → auto_respond
    │   ├── complex + neutral → ai_draft_human_review
    │   └── any + angry → priority_escalation
    │
    └── trycatch: order_lookup
        ├── success → include_order_details
        └── error → ask_for_order_number

Cost Analysis

Before: 50,000 tickets × $2.50/ticket (agent time) = $125K/month

After: 17,500 to agents × $1.80 + 32,500 automated × $0.05 = $33K/month

Savings: ~$92K/month after API costs (~$3K/month)

Breakeven: ~8 weeks including setup costs


Cybersecurity

Alert Triage & Incident Response

The Real Problem

SOC teams receive 5,000-50,000 alerts daily from SIEM, EDR, and network monitoring. 90-95% are false positives. Tier-1 analysts spend all day dismissing noise, developing alert fatigue. Real threats hide in the volume. Mean time to detect (MTTD): 10-20 days. Analyst turnover: 25%+ annually because the job is soul-crushing.

What FlowMason Enables

  • • Enrich alerts with threat intelligence and asset context
  • • Correlate related alerts into single incidents
  • • Auto-dismiss known false positives with audit trail
  • • Prioritize by risk score (asset value × threat severity)

Realistic Expectations

Metric Before After Notes
Alerts requiring human review 10,000/day 500-1,000/day After auto-dismissal
False positive rate 90-95% 40-60% Of remaining alerts
Time to triage 15-30 min 2-5 min Context pre-gathered
MTTD 10-20 days 1-3 days Still depends on attack type

Implementation Reality

Timeline 4-6 months (SIEM integration, tuning false positive rules)
Integrations Splunk/Sentinel, CrowdStrike/Carbon Black, threat intel feeds
Model On-prem preferred (log data sensitivity), or private cloud
Tuning 2-3 months of feedback loop to reduce false positives

Critical Warning

Never auto-dismiss without audit trail. Never auto-remediate without human approval for critical systems. AI reduces noise—it doesn't replace security judgment. Start conservative, tune over months.

Pipeline Pattern
siem_alert
    │
    ├── enrich_context (parallel)
    │   ├── threat_intel_lookup
    │   ├── asset_criticality
    │   ├── user_risk_score
    │   └── historical_alerts
    │
    ├── correlate_related_alerts
    │   └── group_into_incident
    │
    ├── calculate_risk_score
    │
    └── conditional: priority_routing
        ├── critical → immediate_escalation + containment_recommendation
        ├── high → tier2_queue + investigation_guide
        ├── medium → tier1_queue
        └── low → auto_dismiss + log

Technology & Software

Code Review & PR Analysis

The Real Problem

Engineering teams merge 50-200 PRs weekly. Senior engineers spend 5-10 hours/week reviewing code they didn't write. Reviews are inconsistent—same bug patterns slip through depending on who's reviewing. New engineers wait 2-3 days for review. Security issues, performance problems, and style violations get caught late.

What FlowMason Enables

  • • Parallel analysis: security, performance, style, test coverage
  • • Context-aware review (understands your codebase patterns)
  • • Auto-approve trivial PRs (dependency bumps, typo fixes)
  • • Flag high-risk changes for senior review

Realistic Expectations

Metric Before After Notes
Time to first review 8-48 hours 5-30 min AI provides initial pass
Senior review time 20-40 min 10-15 min AI pre-flags issues
Auto-approved PRs 0% 15-25% Trivial changes only
Bugs caught pre-merge Variable +20-30% Consistent patterns

Implementation Reality

Timeline 2-4 weeks (CI/CD integration is straightforward)
Integrations GitHub/GitLab API, CI pipeline, Slack/Teams for notifications
Training Feed it your style guide, past review comments, known patterns
Tuning 2-4 weeks of feedback to reduce false positives
Pipeline Pattern
pull_request
    │
    ├── classify_change_type
    │   └── [trivial, feature, refactor, security-sensitive]
    │
    ├── analyze (parallel)
    │   ├── security_scan
    │   ├── performance_impact
    │   ├── style_compliance
    │   ├── test_coverage_check
    │   └── breaking_change_detection
    │
    ├── conditional: routing
    │   ├── trivial + all_pass → auto_approve
    │   ├── security_issues → security_team_review
    │   └── default → standard_review_queue
    │
    └── generate_review_summary
        └── post_as_pr_comment

Honest Limitation

AI catches pattern-matching issues (security anti-patterns, style violations) well. It's weak at architecture decisions, business logic correctness, and "is this the right approach?" questions. Think of it as a very consistent junior reviewer.

On-Call & Incident Response

The Real Problem

Engineers get paged at 3am for alerts they don't understand. MTTR is 45+ minutes because they're context-switching, searching logs, reading runbooks. Many alerts are noise. Post-incident reviews find the same issues repeatedly.

What FlowMason Enables

  • • Auto-gather context before paging (logs, metrics, recent deploys)
  • • Match against known issues and past incidents
  • • Suggest runbook steps with pre-filled commands
  • • Generate post-incident summary draft

Realistic Expectations

Metric Before After
Time to context 15-30 min 2-5 min
Known issue auto-resolve 0% 10-20%
Post-incident write-up 2-4 hours 30-60 min
Pipeline Pattern
alert_triggered
    │
    ├── gather_context (parallel)
    │   ├── recent_logs (last 30 min)
    │   ├── metric_anomalies
    │   ├── recent_deploys
    │   └── related_alerts
    │
    ├── match_known_issues
    │   └── search_incident_database
    │
    ├── conditional: routing
    │   ├── known_issue + auto_fix → execute_runbook
    │   ├── known_issue → page_with_runbook
    │   └── unknown → page_with_context
    │
    └── trycatch: resolution
        ├── resolved → generate_summary
        └── escalate → add_more_context

Cost Analysis

Incident cost: Avg 2 hours × 2 engineers × $100/hr = $400/incident

With FlowMason: 1 hour × 1.5 engineers = $150/incident

At 50 incidents/month: $12,500 saved monthly

Integration Note

Works well with: PagerDuty, Datadog, Grafana, Slack. The key is connecting to wherever your logs and metrics live. Most teams get this running in 1-2 weeks.


Insurance

Claims Fraud Detection

The Real Problem

Mid-size insurers process 500K-2M claims annually. Fraud rate: 5-10% ($25-50M in losses). Current detection software generates thousands of alerts—90%+ false positives. Investigators can only deep-dive 1-2% of flagged claims. Real fraud hides in the noise.

What FlowMason Enables

  • • Analyze claim narrative, medical codes, provider history in parallel
  • • Cross-reference against known fraud patterns and claimant history
  • • Score risk with explainable reasoning (not black-box ML)
  • • Generate investigation brief with evidence summary

Realistic Expectations

Metric Before After Notes
False positive rate 90%+ 50-65% Still needs human verification
Claims investigated 1-2% 5-8% Same team, better targeting
Investigation prep time 2-4 hours 30-60 min AI pre-assembles evidence
Fraud recovery Baseline +30-50% More cases, better targeting

Implementation Reality

Timeline 4-6 months (claims system integration, compliance review)
Integrations Claims management system, provider databases, external fraud databases
Training Historical fraud cases to calibrate detection thresholds
Compliance State insurance regulations, fair claims handling requirements
Pipeline Pattern
incoming_claim
    │
    ├── analyze (parallel)
    │   ├── narrative_analysis
    │   ├── medical_code_patterns
    │   ├── provider_history
    │   ├── claimant_history
    │   └── geographic_patterns
    │
    ├── calculate_fraud_score
    │   └── weighted_indicators
    │
    ├── conditional: routing
    │   ├── low_risk → auto_process
    │   ├── medium_risk → enhanced_review
    │   └── high_risk → SIU_queue + investigation_brief
    │
    └── feedback_loop
        └── investigator_outcome → retrain_weights

ROI Example

Current fraud loss: $40M/year at 8% fraud rate

Current recovery: $8M (20% of fraud caught)

With FlowMason: $14M recovered (35% caught)

Additional recovery: $6M/year vs ~$200K platform cost


Manufacturing

Supply Chain Risk Intelligence

The Real Problem

Global manufacturers source from 500-5,000 suppliers across 20+ countries. When a Tier-2 supplier has financial trouble or a Tier-3 facility has a fire, you find out from a production line shutdown, not proactive intelligence. Disruption cost: $500K-5M per event. Current monitoring: manual Google alerts and quarterly reviews.

What FlowMason Enables

  • • Continuous scan of news, regulatory filings, social media (12+ languages)
  • • Classify risks: financial, geopolitical, operational, regulatory
  • • Map impact to your supply chain (which products, which factories)
  • • Alert with recommended mitigation (dual sourcing, buffer stock)

Realistic Expectations

Metric Before After Notes
Early warning time 0 (reactive) 5-15 days Before production impact
Suppliers monitored Top 50 All (Tier 1-3) Automated scanning
Risk events caught 20-30% 60-75% With time to react
Disruption cost $1M avg $200-400K Earlier mitigation

Implementation Reality

Timeline 3-4 months (supplier data cleanup is the bottleneck)
Integrations ERP (SAP, Oracle), supplier master data, news APIs, translation services
Data Quality Need accurate supplier hierarchy and product mapping
Tuning 1-2 months to reduce alert fatigue to acceptable level
Pipeline Pattern (runs every 4 hours)
scheduled_trigger
    │
    ├── foreach: suppliers (parallel, batched)
    │   ├── scan_news_sources
    │   ├── check_regulatory_filings
    │   ├── monitor_social_media
    │   └── check_financial_indicators
    │
    ├── classify_risk_type
    │   └── [financial, geopolitical, operational, regulatory]
    │
    ├── map_supply_chain_impact
    │   └── affected_products + factories
    │
    ├── conditional: severity
    │   ├── critical → immediate_alert + mitigation_plan
    │   ├── high → daily_digest + monitoring
    │   └── low → weekly_summary
    │
    └── trycatch: data_source_failures
        └── continue_with_available_sources

Key Challenge

Your supplier master data is probably a mess. Before implementing, invest in cleaning up: supplier hierarchy, geographic locations, product mappings. Garbage in = garbage out.


Media & Entertainment

Content Localization & Compliance

The Real Problem

Streaming platforms release content in 50-190 countries with different cultural norms and age-rating requirements. Each title needs localized descriptions, appropriate ratings, and compliance checks. Manual process: 4-8 weeks per title. International release delays cost $50-500K in lost revenue per major title.

What FlowMason Enables

  • • Analyze content for violence, language, drug use, cultural sensitivities
  • • Generate age-appropriate descriptions per market
  • • Check against 50+ regional regulatory requirements in parallel
  • • Flag issues with timestamp precision for editing

Realistic Expectations

Metric Before After Notes
Localization time 4-8 weeks 3-7 days Human review still needed
Markets per release 20-40 100+ Parallel processing
Compliance violations 5-10/year 1-2/year Consistent checks
Team size 12 localization 6 + AI Quality review focus

Implementation Reality

Timeline 3-5 months (regulatory database is the hard part)
Integrations Content management system, transcription services, rating board APIs
Challenge Building/maintaining regional regulation database
Tuning Significant calibration needed for cultural sensitivity
Pipeline Pattern
content_file
    │
    ├── analyze_content
    │   ├── violence_detection + timestamps
    │   ├── language_analysis
    │   ├── drug_alcohol_references
    │   └── cultural_sensitivity_check
    │
    ├── foreach: markets (parallel)
    │   ├── check_regional_regulations
    │   ├── generate_age_rating
    │   ├── create_localized_description
    │   └── flag_required_edits
    │
    ├── conditional: content_type
    │   ├── children → stricter_review_pipeline
    │   └── general → standard_pipeline
    │
    └── generate_compliance_report
        └── per_market_documentation

Why FlowMason Here?

The key is parallel market evaluation. Processing 100 markets sequentially takes weeks. FlowMason's ForEach pattern processes all markets simultaneously—the limiting factor becomes API rate limits, not processing time.


Real Estate

Commercial Property Due Diligence

The Real Problem

PE real estate funds evaluate 100-300 properties monthly. Each requires reading a 50-100 page offering memorandum, analyzing tenant financials, reviewing lease terms, checking zoning, and running valuation models. Analysts spend 8-15 hours per property. Good deals close in 2-3 weeks—analysis paralysis means losing to faster competitors.

What FlowMason Enables

  • • Extract key metrics from OMs (cap rate, NOI, occupancy, lease terms)
  • • Pull comparable sales and market data automatically
  • • Run multiple valuation scenarios with sensitivity analysis
  • • Generate investment memo in your firm's format

Realistic Expectations

Metric Before After Notes
Initial analysis time 8-15 hours 2-4 hours AI extracts, human validates
Properties screened 30-50/month 100-150/month Per analyst
Time to LOI 2-3 weeks 4-7 days Faster deal velocity
Hidden issues caught Variable More consistent Same checklist every time

Implementation Reality

Timeline 2-3 months (less regulated than healthcare/finance)
Integrations CoStar/REIS for comps, your investment model templates
Training Past deals to calibrate what your firm looks for
Challenge OM formats vary wildly—extraction accuracy varies
Pipeline Pattern
offering_memorandum
    │
    ├── extract_property_details
    │   ├── financial_metrics
    │   ├── tenant_roster + lease_terms
    │   ├── capital_structure
    │   └── risk_factors
    │
    ├── enrich (parallel)
    │   ├── pull_comparable_sales
    │   ├── submarket_analysis
    │   ├── zoning_check
    │   └── demographic_trends
    │
    ├── conditional: property_type
    │   ├── retail → foot_traffic_analysis
    │   ├── office → wfh_impact_assessment
    │   ├── industrial → logistics_proximity
    │   └── multifamily → rent_growth_projection
    │
    └── generate_investment_memo
        └── valuation_scenarios

The Value Prop

In competitive markets, the buyer who can evaluate and bid fastest often wins. FlowMason doesn't make you smarter—it makes you faster. Same quality analysis in 25% of the time means you can bid on 4x as many opportunities.

Common Patterns Across Industries

Regardless of industry, the value comes from the same FlowMason capabilities:

Parallel Processing

Analyze 8 risk categories, 200 trials, or 5,000 suppliers simultaneously—not one at a time. This is the difference between "possible" and "practical."

Intelligent Routing

Simple cases get fast handling, complex cases get deep analysis. Humans review only what actually needs human judgment.

Graceful Degradation

Missing data doesn't crash the workflow. TryCatch patterns handle incomplete records, unavailable APIs, and edge cases—flagging issues instead of failing.

Audit Trail

Every decision is logged. When the auditor asks "why did you approve this contract?" you can show exactly what was analyzed and how.

Version Control

When policies change, pipelines update instantly across all environments. No retraining humans, no hoping everyone got the memo.

Observability

See which steps take longest, which fail most often, which cost most. Optimize based on data, not guesses.

Ready to explore your use case?

Every implementation is different. We'd rather understand your specific situation than give you generic demos.