Real solutions.
Ready to deploy.
ROI you can calculate before you start.
Every enterprise function has high-volume, multi-step knowledge work that costs a fortune in human time and delivers inconsistent results. These are the exact solutions — built from Agents, Teams, Tables, and AI Employees — that the world’s fastest-moving enterprises are deploying right now. With the cost math attached.
171% average ROI
from enterprise agentic AI — McKinsey/Landbase 2026
6 months
average payback period — Forrester Research
70% cost reduction
in automated knowledge work — multiple studies
Every solution on this page is built from
the same four building blocks.
Named Sequential Agent(s)
specific task execution
Named Agentic Team
where multi-specialist routing is needed
Table structure
with AI-enriched columns defined
AI Employee
orchestrating the full function
Trigger configuration
automated firing logic
ROI calculation
time saved, cost saved, revenue impact
These aren’t generic templates. They’re complete, named configurations — ready to deploy in 30 minutes.
Sales Solutions
Your sales team spends 40% of their time on work that shouldn't require a human.
2.5 hrs
daily on research & data entry
60%
leads never properly researched
$138K
fully loaded SDR cost
These four solutions eliminate the manual overhead from your sales operation — so your reps spend time selling, not preparing to sell.
4 Solutions
Lead Enrichment Pipeline
A new lead comes in. Your best rep spends 45 minutes researching the company, scoring the fit, and writing a personalised email. Everyone else spends 10 minutes and sends a generic template. The result? Inconsistent outreach, missed opportunities, and your highest-paid people doing work a machine should handle.
Solution Stack
Company Research Agent
Scrapes website, LinkedIn, Crunchbase. Extracts revenue, headcount, tech stack, recent news.
Prospects Table
14 columns, 4 AI-enriched. Stores all lead data with automated scoring and outreach drafts.
Lead Qualifier Agent
Scores fit against ICP criteria. Assigns priority tier (Hot / Warm / Nurture / Disqualify).
Maya — Sales Ops
Orchestrates the full pipeline. Assigns to reps, drafts personalised emails, handles follow-ups.
Before vs After
New lead lands in CRM
Rep opens LinkedIn (5 min)
Rep checks company website (10 min)
Rep searches Crunchbase/news (10 min)
Rep manually enters data into CRM (5 min)
Rep scores the lead mentally (5 min)
Rep writes personalised email (15 min)
Rep sends email and logs activity (10 min)
Inconsistent across reps
New lead triggers pipeline automatically
Company Research Agent enriches all fields
Data written to Prospects Table (14 columns)
Lead Qualifier Agent scores against ICP
Priority tier assigned (Hot/Warm/Nurture)
Maya drafts personalised email from research
Email queued for rep approval (one click)
Activity logged, follow-up scheduled
Every lead gets the same depth
ROI Calculator
| Leads per month | 200 |
| Time per lead (before) | 60 min |
| Time per lead (after) | 2 min |
| Time saved per lead | 58 min |
| Total hours saved / month | 193 hrs |
| Blended hourly cost | $65/hr |
| Monthly savings | $12,545 |
| Annual savings | $150,540 |
| Additional capacity | 10x more leads processable |
“At 200 leads/month, this solution saves one full-time equivalent in research hours — every month.”
Deal Intelligence & Pipeline Management
Sales managers spend 3 hours every Monday manually pulling deal data from the CRM, cross-referencing it with activity logs, and building a pipeline summary for the team meeting. By the time it’s ready, the data is already stale. Reps don’t update deals in real time, stale opportunities sit unnoticed for weeks, and forecasting is based on gut feel rather than data.
Solution Stack
Deal Intelligence Agent
Pulls deal data, analyses engagement signals, calculates velocity and health scores.
Pipeline Health Agent
Flags stale deals (>7 days no activity), identifies at-risk opportunities, spots gaps.
Deals Table
12 columns. Deal name, stage, value, last activity, health score, days stale, next action.
Maya — Sales Ops
Scheduled Monday 7AM pipeline brief. Condition trigger: flags deals when Days Stale > 7.
Before vs After
Manager opens CRM Monday morning
Exports deal data to spreadsheet (20 min)
Cross-references activity logs manually (30 min)
Calculates pipeline movement by hand (20 min)
Identifies stale deals from memory (15 min)
Builds slide deck for team meeting (30 min)
Presents static data (already outdated)
No follow-up system for flagged deals
Static, outdated by meeting time
Deal Intelligence Agent runs Sunday night
All deals scored and health-checked automatically
Deals Table updated with current velocity data
Pipeline Health Agent flags stale/at-risk deals
Maya sends pipeline brief to Slack (Monday 7AM)
Manager reviews brief before team arrives
Real-time alerts when deals go stale (>7 days)
Reps get nudges with suggested next actions
Real-time, always current
ROI Calculator
| Manager hours saved / week | 3 hrs |
| Annual manager hours saved | 150 hrs |
| Manager hourly cost | $85/hr |
| Annual manager time savings | $12,750 |
| Stale deals caught (avg per month) | 8-12 deals |
| Avg deal size recovered | $15,000+ |
| One recovered deal ROI | Pays for a year of the platform |
“One recovered deal pays for a year of the platform.”
Outreach Automation at Scale
Your top rep writes personalised emails that convert at 18%. Everyone else converts at 4%. The difference isn’t talent — it’s research depth and message relevance. But your best rep can only send 30 emails a day. Meanwhile, the rest of the team blasts generic templates that damage your brand and waste prospects’ attention.
Solution Stack
Sales Outreach Team
3 agents working together: Research Agent → ICP Analyst Agent → Outreach Writer Agent.
Outreach Pipeline Table
Tracks every prospect: research data, ICP score, draft email, approval status, send status.
Row Created Trigger
New prospect added → automatically kicks off the 3-agent outreach team.
Rep Approval → Gmail Send
Rep reviews and approves with one click. Email sends via connected Gmail.
Before vs After
Rep picks prospect from list
Researches company manually (15 min)
Checks ICP fit by memory (5 min)
Writes personalised email (15 min)
Reviews, edits, sends (5 min)
Top rep: 30 emails/day at 18% conversion
Average rep: 80 emails/day at 4% conversion
No consistency in quality or messaging
4-18% conversion (wildly inconsistent)
Prospect added to Outreach Pipeline Table
Research Agent enriches company data
ICP Analyst scores fit and identifies angles
Outreach Writer drafts personalised email
Rep reviews draft (30 seconds)
One-click approval sends via Gmail
500+ personalised emails / month
Every email has top-rep research depth
12%+ conversion (consistent)
ROI Calculator
| Emails per month | 500 |
| Previous conversion rate | 4% |
| New conversion rate | 12% |
| Additional meetings / month | 40 |
| Average deal value | $8,500 |
| Close rate | 25% |
| Additional monthly revenue | $85,000 |
| Additional annual revenue | $1,020,000 |
“Give every rep the research depth and writing quality of your best performer — at 15x the volume.”
Competitive Intelligence Monitoring
Your reps are losing deals to competitors they didn’t know had released a new feature last week. Your product team hears about competitive moves from customers instead of internal intelligence. Someone subscribes to a few newsletters and manually checks competitor websites when they remember — which is never consistently enough.
Solution Stack
Competitive Intelligence Agent
Monitors competitor websites, press releases, product updates, pricing changes, job postings.
Competitive Intelligence Table
Competitor, update type, date detected, summary, impact assessment, suggested response.
Schedule Trigger (Sunday 10PM)
Weekly automated sweep of all tracked competitors. Results written to table.
Weekly Brief to Slack
Formatted competitive intelligence digest sent to #sales-intel channel every Monday.
Before vs After
Someone remembers to check competitors (maybe)
Manual website browsing (30 min / competitor)
Checks social media and press releases
Writes summary in a doc nobody reads
Information stays siloed with one person
Reps discover competitive moves from prospects
No systematic tracking or history
Paying $200-500/mo for tools that still need manual review
Sporadic, incomplete, siloed
Competitive Intelligence Agent runs Sunday 10PM
All tracked competitors scanned automatically
Changes detected and classified by impact level
Results written to Competitive Intelligence Table
Weekly digest formatted with actionable insights
Posted to #sales-intel Slack channel Monday morning
Full history searchable in table
Reps go into calls armed with latest intel
Systematic, complete, shared
ROI Calculator
| Replaces competitive intel tools | $200–500/mo |
| Rep time saved per week | 5 hrs |
| Annual rep time savings (at $65/hr) | $16,900 |
| Win rate improvement | 15–20% |
| Deals saved by better intel (est.) | 2–4/quarter |
| Revenue impact at $15K avg deal | $30K–$60K/quarter |
“Your reps should never walk into a call not knowing what the competition shipped last week.”
Combined Impact
Deploy all four and your sales team gets back 250+ hours per month.
Each solution works independently, but they compound when deployed together. The Prospects Table feeds the outreach pipeline. Deal intelligence catches what enrichment surfaces. Competitive intel sharpens every email.
250+
hours saved / month
$150K+
annual cost savings
$1M+
potential revenue impact
4 hrs
to deploy all four
Your HR team is buried in repetitive work that shouldn’t require a human.
8 hours
screening candidates per role
23 days
average time to hire
$4,700
average cost per hire — SHRM 2024
Candidate Screening Pipeline
You post a role and receive 150 applications. Your HR coordinator manually reads every CV, copies data into a spreadsheet, and tries to rank candidates by gut feeling. It takes 8 hours per role, it’s inconsistent, and the best candidates accept other offers while you’re still screening.
Solution Stack
CV Parser Agent
Sequential Agent
Candidate Scorer Agent
Sequential Agent
Candidate Pipeline Table
11 columns, 6 AI-enriched
Trigger
On row created
Emma
HR Coordinator AI Employee
- 8 hours manually screening 150 CVs
- 3-day delay before shortlist is ready
- Inconsistent scoring based on gut feeling
- Best candidates accept other offers first
- 150 CVs screened in 2 hours, fully automated
- Ranked shortlist ready before HR arrives at work
- Objective scoring against role-specific criteria
- Top candidates contacted within hours of applying
ROI Calculation
| Screening time saved per role | 6 hours |
| Hourly cost of HR coordinator | $45/hr |
| Savings per role | $270 |
| 10 roles per year | $2,700/yr |
| Time-to-hire reduction | 5 days |
One top performer hired 5 days earlier = 5 days of productivity gained = $2,000+ for senior roles.
Employee Onboarding Automation
Your best new hires have a chaotic first week. IT provisioning takes 3 days, HR paperwork takes 2 days, and the new employee spends their first Monday watching mandatory training videos on a laptop that hasn’t been configured yet. Every manager runs onboarding differently. There is no consistency.
Solution Stack
Onboarding Orchestrator Agent
Sequential Agent
HR Knowledge Team
3 agents: HR Policy, Payroll, Benefits
Onboarding Tracker Table
11 columns
Trigger
On row created
Deployed to Slack
#new-joiners
- Chaotic first week with no consistent process
- 3 days waiting for IT provisioning
- 2 days for HR paperwork to be completed
- New hire questions go unanswered for hours
- Structured day-by-day onboarding, fully automated
- IT provisioning triggered automatically on hire date
- Paperwork sent, signed, and filed before day 1
- Questions answered instantly via Slack with cited policy
ROI Calculation
| Time saved per new hire | 4 hours |
| Annual hires | 50 |
| Total hours saved per year | 200 hours |
| Annual savings | $9,000/yr |
Structured onboarding improves 90-day retention by 82% — SHRM. One fewer regretted departure per year pays for the entire deployment.
HR Knowledge Team
Your HR team answers the same 50 questions every week. “How many sick days do I have?” “What’s the dental plan deductible?” “Do I need a doctor’s note for 3 days off?” Every answer requires looking up the same policy document. Your HR coordinator spends 16 hours a week on Q&A instead of strategic work.
Solution Stack
HR Knowledge Team
4 agents: HR Policy, Payroll, Benefits Advisor, Onboarding Guide
5 Knowledge Bases
Employee Handbook, Benefits Guide, Leave Policy, Expense Policy, Onboarding Docs
Deployed to Slack
#hr-help
Deployed to Web Chat
Employee self-service
- Employee asks HR, waits 4+ hours for a response
- HR coordinator resents being a human FAQ
- Inconsistent answers depending on who responds
- Policy updates require retraining every HR team member
- 30-second response with cited policy answer
- HR coordinator freed for strategic work
- Every answer references the same source of truth
- Update the knowledge base once, every answer updates instantly
ROI Calculation
| Questions handled per week | 100 |
| Average time per question (manual) | 10 minutes |
| Weekly time saved | 16 hours |
| Annual hours saved | 800 hours |
| Annual savings ($45/hr) | $36,000/yr |
This single deployment frees one HR coordinator to focus on strategic work — recruiting, culture, retention — instead of answering the same questions every day.
Combined HR Impact
1,000+
hours saved per year
$47,700
annual savings (conservative)
5 days
faster time-to-hire
Three deployments. One platform. Your HR team stops being a help desk and starts being a strategic function.
Finance teams spend 60% of their time on data processing work that AI does better.
Invoice processing. Expense monitoring. Month-end reporting. These are high-volume, rules-based workflows where every hour of manual work is a dollar wasted and an error waiting to happen. Here are three ready-to-deploy solutions with the cost math attached.
20 minutes
Manual invoice processing time
$12.90
Cost per manually processed invoice
IOFM 2024
$3.50
AI automated cost per invoice
4 days
Manual AP cycle vs same-day with AI
Invoice Processing Pipeline
THE PAIN
Your AP team receives 500 invoices a month. Each one needs to be opened, data extracted, matched to a PO, validated against contract terms, coded to the right GL account, routed for approval, and entered into your ERP. Every step is manual. Every step is a potential error. Every error is a delayed payment and a strained vendor relationship.
Solution Stack
Invoice Parser Agent
Extracts line items, amounts, dates, vendor info
AP Processor Agent
Matches PO, validates terms, codes GL accounts
Invoice Processing Pipeline Table
11 columns tracking end-to-end status
Trigger: Row Created
Fires instantly on new invoice arrival
Marcus — Finance Analyst AI Employee
Reviews exceptions, escalates anomalies, reports
Invoice Processing Pipeline
11 columnsBefore vs. After
ROI Breakdown
Pays for itself in the first 3 months.
Expense Anomaly Detection
THE PAIN
Expense fraud and policy violations cost US businesses $2.9 trillion annually (ACFE). Your finance team reviews expense reports manually — if they review them at all. Duplicate submissions, inflated amounts, out-of-policy spending, and split transactions to avoid approval thresholds all slip through. By the time you catch them, the money is gone.
Solution Stack
Expense Anomaly Agent
Detects duplicates, policy violations, unusual patterns
Expense Monitoring Table
11 columns tracking every expense line
Schedule Trigger: Friday 5 PM
Weekly automated scan of all expenses
Weekly Anomaly Report
Auto-generated and delivered to CFO
Expense Monitoring
11 columnsBefore vs. After
ROI Breakdown
Every dollar recovered is pure margin. The agent pays for itself with the first caught anomaly.
Automated Financial Reporting
THE PAIN
Your finance team spends 3 days each month-end pulling data from 6 different systems, reconciling numbers, building the same report format they built last month, catching transcription errors, and getting the package to the board 3–4 days late. Every month. Like clockwork. Except nothing about it is actually automated.
Solution Stack
Data Aggregation Agent
Pulls from 6 systems, reconciles, normalises
Report Generator Agent
Builds formatted financial package
Financial Reports Archive Table
Historical record of every report generated
Schedule Trigger: 1st of Month, 6 AM
Runs automatically every month-end
CFO Reviews
Human approval before distribution
Auto-Distributed
Board and stakeholders receive report
Financial Reports Archive
11 columnsBefore vs. After
ROI Breakdown
Same format every month. Zero transcription errors. The board has their report on day 1, not day 4.
Three finance workflows. $85,250+ in combined annual savings. 183+ hours freed every month. All deployable this week.
Invoice processing alone pays for the platform. Expense detection and reporting are pure upside.
Your customers are waiting. Your agents are drowning.
Both problems have the same solution.
The economics of human-only support are broken. Every ticket costs $22, customers wait hours for a first response, and your best agents spend 80% of their time on repetitive tier-1 issues. Here is exactly how to fix it.
4 hours
Average first response time
Industry average across mid-market support teams
$22
Cost per support ticket
Forrester Research
73%
Customers hate repeating themselves
Salesforce State of Service report
Ticket Triage & Resolution Pipeline
The Problem
Your support team receives 300 tickets a day. Every ticket needs to be read, categorised, routed to the right specialist, researched against your knowledge base, and answered. Your agents spend 80% of their time on tier-1 issues that have documented answers. Meanwhile, complex tickets sit in queue while customers grow increasingly frustrated.
Solution Stack
Customer Support Triage Team
3 agents: Billing Support + Technical Support + Account Management
Hierarchical Routing
Supervisor routes to the right specialist
4 Knowledge Bases
Product docs, billing policies, troubleshooting guides, account procedures
Table: Support Tickets
12 columns including priority, category, status, resolution, CSAT
Deployed to Web Chat + Slack + Teams
Customers reach you on any channel
Trigger: Row Created
New ticket arrives, triage begins instantly
Manual reading
Human reads every ticket, categorises manually
Manual KB search
Agent searches knowledge base, copies/pastes answers
Slow routing
Escalations sit in queue, no priority scoring
4 hours to first response
Customers wait, frustration builds, CSAT drops
AI supervisor routes
Ticket classified and routed to specialist in seconds
Automatic KB + CRM search
Specialist agent searches all sources simultaneously
Intelligent prioritisation
Severity, customer tier, and sentiment all factor in
2 minutes to first response
60–65% of tickets auto-resolved without human touch
ROI Calculation
| Monthly ticket volume | 1,000 tickets/month |
| AI-handled tickets (60%) | 600 tickets/month |
| Cost per ticket: before | $22.00 |
| Cost per ticket: after | $2.50 |
| Savings per ticket | $19.50 |
| Monthly savings | $11,700 |
| Annual savings | $140,400 |
First response time 4 hrs \u2192 2 min = 34% CSAT improvement
Customer Health Monitoring
The Problem
You find out a customer is about to churn when they send the cancellation email. By then, it’s too late. The warning signs were there for weeks — declining usage, increasing support tickets, missed check-ins — but nobody was watching. Your CSMs manage 40+ accounts each and cannot manually track health signals across every customer, every week.
Solution Stack
Customer Health Score Agent
Pulls usage data, support history, engagement signals
Customer Health Dashboard Table
9 columns: company, health score, usage trend, ticket count, NPS, last contact, risk level, CSM, actions
Condition Trigger: Score < 60
At-risk accounts flagged immediately
Schedule Trigger: Monday 7 AM
Weekly health recalculation across all accounts
Notifies CSM in Slack
Right person alerted with context and recommended actions
Reactive discovery
You find out a customer is churning when they send the cancellation email
Manual health checks
CSMs manually review accounts quarterly at best
No early warning system
Declining engagement goes unnoticed for weeks
Lost revenue
By the time you intervene, the customer has already decided to leave
Proactive detection
Health scores recalculated weekly, risk flagged instantly
Automated monitoring
Every account tracked continuously without manual effort
Immediate alerts
CSM notified in Slack the moment a score drops below threshold
Retention playbooks
Recommended actions accompany every alert
ROI Calculation
| Accounts saved per month | 5 accounts |
| Average ARR per account | $20,000 |
| Retained revenue per month | $100,000 |
| Retained revenue per year | $1,200,000 |
One prevented churn pays for the entire platform.
Two solutions. $140K+ in annual ticket savings. $1.2M in retained revenue.
Deployed in days, not months.
Unplanned downtime costs manufacturers $50 billion a year.
Most of it is preventable.
The data that predicts failures already exists in your maintenance logs, equipment sensors, and supplier records. The problem is that nobody has time to analyse it. These solutions turn your existing operational data into preventive intelligence.
$260,000/hr
Cost of downtime in automotive
Aberdeen Group
42%
Outages had detectable warnings
Deloitte Analytics Institute
30%
Maintenance cost reduction with predictive
McKinsey & Company
Preventive Maintenance Planning
The Problem
Your maintenance team manages 50 pieces of critical equipment. Each has different service intervals, manufacturer recommendations, and failure patterns. PM schedules live in spreadsheets, get overridden by production demands, and compliance hovers around 60%. Equipment runs until it breaks, and reactive repairs cost three times what preventive maintenance would have.
Solution Stack
PM Planner Agent
Analyses equipment records, manufacturer specs, and maintenance history
Equipment Maintenance Master Table
All critical equipment with service intervals, last PM date, condition, and priority
Maintenance History Log Table
Full history of every service event, parts replaced, and technician notes
Equipment Maintenance Library KB
Manufacturer manuals, service bulletins, and parts catalogues
Schedule Trigger: Sunday 8 PM
Weekly PM plan generated before the work week begins
Riley — Manufacturing Ops AI Employee
Autonomous coordinator that manages the full maintenance workflow
PM compliance ~60%
Scheduled maintenance frequently skipped due to production pressure
Reactive repairs
Equipment runs to failure; reactive repairs cost 3x preventive maintenance
Manual tracking
Spreadsheets, whiteboards, and tribal knowledge determine maintenance schedule
Parts shortages
Parts ordered after failure, not before — extending downtime by days
PM compliance 95%+
Every piece of equipment tracked with automated reminders and scheduling
Proactive parts ordering
Parts needed for upcoming PMs identified and ordered in advance
Data-driven priorities
Equipment ranked by criticality, failure risk, and production impact
Full maintenance history
Every service event logged automatically, building institutional knowledge
ROI Calculation
| Reactive-to-preventive shift | 50% of reactive work eliminated |
| Maintenance cost savings | $100,000/year |
| Prevented downtime events | 5 events/year |
| Downtime cost avoided | $25,000–$250,000 |
| Combined annual savings | $125,000–$350,000 |
Shifting 50% of reactive work to preventive saves $100K+/year. Five prevented downtime events = $25K\u2013$250K additional savings.
Quality Shift Reporting
The Problem
Your quality team produces a shift report at the end of each production shift. It takes 45–60 minutes of manual data collection, formatting, and analysis. By the time the report reaches management, the next shift is already two hours in. Critical quality issues that started in the first hour of a shift are not flagged until the shift is over.
Solution Stack
Quality Shift Report Agent
Collects production data, compares against targets, identifies defects and trends
Quality Shift Reports Table
12 columns: date, shift, line, units produced, defects, defect rate, top defect type, scrap cost, OEE, operator, notes, status
Scheduled Trigger: End of Each Shift
6 AM / 2 PM / 10 PM — report generated automatically at shift change
60 minutes manual compilation
Quality team manually gathers data from multiple systems at shift end
Delivered 2 hours post-shift
By the time the report reaches management, the next shift is well underway
Inconsistent formatting
Each quality lead produces reports differently — hard to compare across shifts
Delayed defect response
Critical quality issues discovered hours after they began
Report ready in 5 minutes
Automatically generated the moment shift ends — no manual data collection
Critical issues alerted immediately
Defect rate spikes trigger instant notifications before shift report is complete
Standardised format
Every shift, every line, every day — same structure, easy trend analysis
Trend detection built in
Agent identifies recurring defect patterns across shifts and lines
ROI Calculation
| Time saved per shift report | 55 minutes |
| Daily time saved (3 shifts) | 2 hours 45 minutes |
| Annual hours saved | 1,095 hours |
| Labour cost at $55/hr | $60,225/year |
| Faster defect detection value | Reduced scrap and rework |
3 hours/day saved across 3 shifts. 1,095 hours/year at $55/hr = $60,225 in direct labour savings. Plus faster defect detection reducing scrap.
Supplier & Procurement Intelligence
The Problem
Your procurement team manages 40 suppliers. Tracking performance, compliance certifications, delivery reliability, and financial health across all of them is a full-time job that nobody has time for. Supplier reviews happen quarterly at best. Between reviews, a critical supplier’s quality can deteriorate, certifications can lapse, or financial distress can go unnoticed — until your production line stops.
Solution Stack
Supplier Due Diligence Agent
Researches supplier performance, compliance, financial health, and risk signals
Supplier Registry Table
11 columns: supplier name, category, risk score, quality rating, lead time, on-time delivery %, cert expiry, last audit, spend YTD, contact, status
Condition Trigger: Risk > 70 OR Cert < 30 Days
At-risk suppliers and expiring certifications flagged automatically
Manual supplier tracking
Procurement team manages 40 suppliers across spreadsheets and email
Quarterly reviews at best
Performance reviews happen quarterly — problems emerge between reviews
Compliance blind spots
Certification expirations discovered during audits, not before
Reactive crisis management
Supply chain disruptions hit without warning; scramble for alternatives
Continuous risk monitoring
Every supplier scored and monitored in real-time against risk thresholds
Proactive cert management
Expiring certifications flagged 30 days out with renewal reminders
Performance trending
Quality ratings, delivery performance, and lead times tracked automatically
Early warning system
Financial health signals, news events, and risk indicators monitored continuously
ROI Calculation
| Supply chain disruption cost | $100,000–$500,000 per event |
| Compliance penalty risk | $50,000+ per violation |
| Procurement time savings | 15+ hours/week |
| One prevented disruption | Entire platform ROI for the year |
Identifying one at-risk supplier before failure = platform ROI for the year.
Three solutions. $185K–$660K in annual savings. Zero unplanned surprises.
Built from the same platform your office teams already use.
Your IT team is the most expensive help desk your company has. They shouldn’t be answering “how do I reset my password?”
40%
of tickets are Tier-1
$22
Tier-1 cost by human
$2
Tier-1 cost by AI
45 min
lost per employee per IT issue
Gartner
IT Helpdesk Automation
Your IT team of 4 handles 200 tickets a week. 80 of those tickets are: password resets, VPN setup instructions, “how do I install X?”, basic troubleshooting guides, and access requests for standard software. Every one of them pulls a senior engineer off real work.
SOLUTION STACK
IT Helpdesk Team
Agentic Team · 4 agents5 Knowledge Bases
IT Tickets Table
11 columnsDeployed To
Trigger
On row created — fires automatically when a new ticket is submitted via form, Slack, or MS Teams.
BEFORE & AFTER
BEFORE
- All 200 tickets hit the IT team directly
- Senior engineers triaging basic requests
- 2–4 hour response time for all tickets
AFTER
- Tier-1 auto-resolved (40% = 80/week)
- Tier-2/3 pre-triaged with context attached
- 2-minute Tier-1 response time
ROI CALCULATION
| Metric | Value |
|---|---|
| Tier-1 ticket savings | 80 tickets/wk × $20 = $1,600/wk |
| Annual ticket savings | $83,200/yr |
| Senior time freed | 40 hrs/wk × $85/hr = $176,800/yr |
| Total annual savings | $260,000+/yr |
Access Provisioning Automation
A new employee starts on Monday. By Thursday, they still don’t have access to half the systems they need. IT is chasing down approvals. The manager is frustrated. The new hire is sitting idle. Meanwhile, the employee who left two months ago still has admin access to your CRM.
SOLUTION STACK
Access Provisioning Agent
Sequential AgentMaps role to required systems, generates provisioning checklist, monitors access lifecycle, flags orphaned accounts.
Access Registry Table
9 columnsTrigger 1
On row created — New employee added to the registry triggers provisioning checklist generation.
Trigger 2
Condition: Last Login > 30 days — Flags orphaned accounts and triggers access review workflow.
BEFORE & AFTER
BEFORE
- 3 days for full system provisioning
- Orphaned accounts from departed employees
- Manual quarterly access audits (2 days each)
AFTER
- Same-day provisioning for all new hires
- Continuous automated access monitoring
- Orphaned accounts flagged within 30 days
ROI CALCULATION
| Metric | Value |
|---|---|
| Provisioning time | 3 days → same day |
| Security risk reduction | Continuous monitoring vs. quarterly audits |
| Manual audit time saved | 2 days/quarter → automated |
73% of data breaches involve excessive access privileges — IBM. Continuous automated monitoring eliminates the blind spots between quarterly manual audits.
Across all six functions.
The math is impossible to ignore.
| Department | Solution | Time Saved/Month | Annual Cost Saving | Payback Period |
|---|---|---|---|---|
| Sales | Lead Enrichment | 193 hrs | $150,540 | < 1 month |
| Sales | Outreach Automation | — | $85K added rev | Immediate |
| HR | Candidate Screening | 60 hrs/role | $9,000/yr | 2 months |
| HR | HR Knowledge Team | 67 hrs/mo | $36,000/yr | 2 months |
| Finance | Invoice Processing | 147 hrs/mo | $56,400/yr | 3 months |
| Finance | Anomaly Detection | — | $16,250 recovered | 1 month |
| Support | Ticket Triage | 600 tickets auto | $140,400/yr | 2 months |
| Support | Health Monitoring | — | $100K ARR retained | 1 month |
| Manufacturing | PM Planning | 27 wks/yr | $100,000+/yr | 1 month |
| Manufacturing | Shift Reporting | 1,095 hrs/yr | $60,000+/yr | 2 months |
| IT | Helpdesk Automation | 40 hrs/wk | $260,000/yr | 1 month |
| IT | Access Provisioning | 40 hrs/qtr | Compliance + time | 2 months |
Deploy all 12 solutions: $1M+ annual value identified before accounting for revenue upside and retention improvement. | ||||
Which solution does your operations team need most?
Pick one. We’ll show you exactly how to deploy it — the agents, the tables, the employee configuration — in 30 minutes. Running in production this week.
Tell us your department and we’ll walk you through the exact configuration for your use case.