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.
Legal Services
Contract Review & Risk Analysis
The Real Problem
Mid-size firms handle 200-500 contracts monthly. A junior associate costs $150-250/hour and takes 2-4 hours per contract. That's $300-1,000 per contract, with inconsistent quality depending on who reviews it and how tired they are. Senior partners still review everything because they don't trust the output.
What FlowMason Enables
- • Parallel extraction of 8+ risk categories in a single pass
- • Cross-reference against your firm's clause library and policies
- • Confidence scoring so partners review only flagged items
- • Version-controlled pipelines that update when policies change
Realistic Expectations
| Metric | Before | After (6mo) | Notes |
|---|---|---|---|
| Time per contract | 2-4 hours | 20-40 min | Human still reviews AI output |
| Cost per contract | $400-800 | $80-150 | Includes AI + human time |
| Partner review rate | 100% | 15-25% | Only flagged contracts |
| Consistency | Variable | Standardized | Same criteria every time |
Implementation Reality
contract_input
│
├── extract_clauses (parallel)
│ ├── indemnification
│ ├── liability_caps
│ ├── termination_rights
│ ├── ip_assignment
│ └── data_privacy
│
├── risk_scoring
│ └── compare_to_policy_library
│
└── conditional_routing
├── low_risk → auto_approve + summary
├── medium → associate_review_queue
└── high_risk → partner_escalation Why not just use ChatGPT?
You could. But you'd need to: paste each contract manually, remember to check all 8 risk categories every time, hope the attorney uses the same criteria as last time, manually log the results somewhere, and do it again when policies change. FlowMason makes this repeatable, auditable, and consistent.
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
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.
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
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.
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
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
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.
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
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 |
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
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
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
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
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.