That operations manager you are paying $90,000 a year? She is spending three hours a day copying data between spreadsheets. She was hired to think strategically. Instead, she is a human middleware layer between systems that should already talk to each other.
We build AI systems that catch bottlenecks before they become crises, monitor quality in real time instead of after defects ship, automate the reporting that eats your team's week, and fix the handoffs that break every time work crosses a department line. For mid-market companies with 50-500 employees who need operations to run smoothly, not as a daily fire drill.
Your operations look efficient on paper. The reality is held together by heroics and workarounds.
Bottlenecks that nobody can see until they have already cost you a week
Your production line slowed by 18% last month. Nobody noticed until the weekly review because the data sits in three systems and nobody cross-references them in real time. The root cause: a single approval step adding two days to every order above a threshold set three years ago. Your operations team is capable. They are efficient within a process that is silently broken, and they cannot fix what they cannot see.
Quality issues you catch after they have already shipped
Your quality team catches defects at a 70-80% rate on a good day. The ones that slip through become customer complaints, returns, and warranty claims costing 5-10x what it would have cost to catch them in process. Your cost of poor quality runs 15-20% of revenue. AI-driven quality monitoring catches deviations the moment the process drifts, not at the end of the line.
Weekly reporting that consumes 40% of your ops team's Thursday and Friday
Every Thursday, your operations lead pulls data from the ERP, copies into a spreadsheet, cross-references with the production log, and reformats for leadership. That same report gets rebuilt by three other department heads. Leadership makes decisions based on data that is a week old and manually compiled. Your operations team cannot optimise operations when two days of every week are consumed by data assembly.
Handoffs between departments where work goes to die
Sales closes a deal and sends the order to fulfilment via email. Fulfilment asks clarifying questions that take two days to resolve. Production gets specs with missing dimensions and builds to assumption. The customer receives something close to what they ordered but not exactly right. The handoff points between departments have no structure, no validation, and no visibility. Every broken handoff costs rework time and customer goodwill.
Find the invisible friction. Build the system. Measure the difference.
We do not sell you an operations platform. We study how your work actually flows across people, departments, and systems, then build AI that makes the invisible visible and the manual automatic. You own everything we build.
Operations Process Audit
Days 1-5
We sit with your operations managers, department heads, and the people who actually do the work, including the ones who never make it into meetings. We map every process end to end: how orders flow from sales to fulfilment, how production schedules get built, how quality checks happen, how reports get assembled, and where work stalls when it crosses a department line. We measure cycle times, handoff delays, rework rates, and reporting hours. Most companies find that 20-35% of their operations time goes to activities that produce no value: manual data transfer, redundant approvals, status chasing, and report formatting.
Deliverable: Operations process map with cycle time analysis, bottleneck identification, handoff failure points, and AI opportunity scoring by ROI
System Design & Data Integration
Days 6-14
For each AI system in scope, we design the architecture and connect your data sources. Process mining models need event logs from your ERP, CRM, and project management tools. Quality monitoring needs sensor data, inspection records, and production parameters. Automated reporting needs access to every system your team currently pulls data from manually. We clean the data, map relationships between systems, and validate that the inputs are reliable enough to generate trustworthy outputs. You review and approve the design before we build.
Deliverable: System architecture documents, data pipeline specifications, integration plan for ERP, CRM, and operational tools
Build, Train & Test
Week 3-5
We build the AI systems and train them on your operational data. Process mining models learn your actual workflow patterns and flag deviations from optimal paths. Quality monitoring models learn the parameters that predict defects before they manifest. Reporting engines pull from every source your team was manually querying and generate dashboards that update in real time. We back-test against historical data: would this model have flagged the bottleneck that cost you two weeks last quarter? Would it have caught the quality drift that led to that batch of returns? Every system includes explainability, telling you what it found and why it matters.
Deliverable: Trained and tested AI systems with accuracy benchmarks, back-testing results against historical incidents, and integration with your operational tools
Deploy, Validate & Handover
Week 5-7
We deploy to production and run parallel testing with your operations team for one to two weeks. Process mining alerts run alongside your existing reviews so your team can validate whether the AI flags real issues. Quality monitoring runs in shadow mode before going live. Automated reports generate alongside manual ones so your team can compare accuracy and completeness. We train every user, hand over complete documentation, and set up monitoring dashboards that track system performance. Your team owns it from day one of handover.
Deliverable: Production-deployed AI systems, team training sessions, operations playbook, monitoring dashboards, and 30-day performance review
Operations Process Audit
Days 1-5
We sit with your operations managers, department heads, and the people who actually do the work, including the ones who never make it into meetings. We map every process end to end: how orders flow from sales to fulfilment, how production schedules get built, how quality checks happen, how reports get assembled, and where work stalls when it crosses a department line. We measure cycle times, handoff delays, rework rates, and reporting hours. Most companies find that 20-35% of their operations time goes to activities that produce no value: manual data transfer, redundant approvals, status chasing, and report formatting.
Deliverable: Operations process map with cycle time analysis, bottleneck identification, handoff failure points, and AI opportunity scoring by ROI
System Design & Data Integration
Days 6-14
For each AI system in scope, we design the architecture and connect your data sources. Process mining models need event logs from your ERP, CRM, and project management tools. Quality monitoring needs sensor data, inspection records, and production parameters. Automated reporting needs access to every system your team currently pulls data from manually. We clean the data, map relationships between systems, and validate that the inputs are reliable enough to generate trustworthy outputs. You review and approve the design before we build.
Deliverable: System architecture documents, data pipeline specifications, integration plan for ERP, CRM, and operational tools
Build, Train & Test
Week 3-5
We build the AI systems and train them on your operational data. Process mining models learn your actual workflow patterns and flag deviations from optimal paths. Quality monitoring models learn the parameters that predict defects before they manifest. Reporting engines pull from every source your team was manually querying and generate dashboards that update in real time. We back-test against historical data: would this model have flagged the bottleneck that cost you two weeks last quarter? Would it have caught the quality drift that led to that batch of returns? Every system includes explainability, telling you what it found and why it matters.
Deliverable: Trained and tested AI systems with accuracy benchmarks, back-testing results against historical incidents, and integration with your operational tools
Deploy, Validate & Handover
Week 5-7
We deploy to production and run parallel testing with your operations team for one to two weeks. Process mining alerts run alongside your existing reviews so your team can validate whether the AI flags real issues. Quality monitoring runs in shadow mode before going live. Automated reports generate alongside manual ones so your team can compare accuracy and completeness. We train every user, hand over complete documentation, and set up monitoring dashboards that track system performance. Your team owns it from day one of handover.
Deliverable: Production-deployed AI systems, team training sessions, operations playbook, monitoring dashboards, and 30-day performance review
AI systems embedded in your operations. Built to be used daily, not checked once and forgotten.
Discovery & Design (Week 1-2)
- End-to-end operations process map with cycle time analysis across every department
- Bottleneck identification report with cost-of-delay quantification for each friction point
- Cross-department handoff audit showing where information is lost, delayed, or corrupted
- Data quality assessment across ERP, CRM, and operational systems
- AI opportunity matrix scored by ROI, feasibility, and implementation speed
Build & Test (Week 3-5)
- Process mining engine that finds bottlenecks and process deviations in real time
- Predictive maintenance system that forecasts equipment failures before they cause unplanned downtime
- Quality monitoring system that detects parameter drift before defects occur
- Automated reporting engine replacing manual weekly data assembly with live dashboards
- Cross-department handoff validation system with structured data transfer and exception alerts
- Back-testing reports showing model accuracy against your historical operational incidents
Deploy & Handover (Week 5-7)
- Production deployment with parallel testing alongside existing operational processes
- Hands-on training for operations team, department heads, and leadership (recorded)
- Complete documentation: system logic, data sources, alert thresholds, and maintenance guides
- Monitoring dashboards tracking system accuracy, alert response rates, and operational KPIs
We build AI for operations intelligence. These are different engagements.
We scope tightly so timelines stay honest and results stay measurable. Each of these is available as a separate engagement.
End-to-end workflow automation (approvals, routing, notifications)
We build the intelligence layer: the AI that identifies what is broken, what is drifting, and what needs attention. If you need to automate the workflows themselves (approval chains, document routing, notification triggers, task assignment), that is a business automation engagement with different tooling and scope.
Business AutomationCompany-wide BI dashboards and data warehouse design
Our automated reporting replaces manual operational reporting with AI-generated insights. If you need a full business intelligence layer across the company (data warehousing, cross-functional dashboards, self-serve analytics), that is our Data Analytics practice.
Data AnalyticsSales-to-fulfilment pipeline and revenue operations
We fix handoff failures at the operational level. If your core problem is the sales-to-delivery pipeline (lead scoring, deal velocity, pipeline forecasting, revenue attribution), that is a RevOps engagement with a commercial focus rather than an operational one.
Revenue OperationsIs this right for you?
Right for you if
- You are a mid-market company (50-500 employees, $2M+ revenue) where operations complexity has outgrown your team's ability to manage it manually, and you are seeing the symptoms: missed deadlines, quality escapes, reporting that consumes days instead of minutes.
- Your operations span multiple departments with handoff points that regularly break, and nobody has end-to-end visibility into how work actually flows across the organisation.
- Your operations team spends more than 10 hours per week assembling reports manually from multiple systems, and leadership is still making decisions on data that is days or weeks old.
- You have an ERP or project management system with at least 12 months of operational data that process mining models can learn from.
Not right if
- You have fewer than 50 employees or a single-department operation. Your process complexity does not justify AI-powered process mining. A good process consultant and some automation will serve you better.
- You do not have any structured operational data: no ERP, no project management tool, no production logs. AI needs data to learn from. We can help you build that foundation, but that is a different engagement.
- Your primary problem is technology, not process. If your systems are fundamentally broken or outdated, you need infrastructure first, not an intelligence layer on top of a broken foundation.
What this looks like in practice.
Problem
A mid-market auto parts manufacturer with $14M revenue and 280 employees had a quality problem they could not locate. Defect rates on one product line had crept from 2.1% to 3.8% over six months, but end-of-line inspections could not pinpoint when or where the drift started. Their quality team was running manual checks at three stations and logging results in spreadsheets that nobody analysed until the monthly review. By the time the trend was visible, three months of production had shipped with elevated defect rates. Customer returns on that line increased by 40%, costing them $22,000 in warranty claims and rework.
What we did
Deployed a quality monitoring system that ingested production parameters (temperature, pressure, cycle time, material batch data) and correlated them with defect outcomes in real time. The model identified that defect probability spiked when a specific machine exceeded a temperature threshold that the manual checklist did not include. Built automated alerts that flagged parameter drift within minutes instead of months.
Outcome
Defect rate on the problem line dropped from 3.8% to 1.4% within two months. Customer returns decreased by 55%. Quality team shifted from manual logging to exception-based monitoring, saving 15 hours per week. Annual savings from reduced rework and warranty costs estimated at $40,000.
Problem
A 160-person consulting firm with $9M revenue was losing an average of 4.5 days on every client engagement due to handoff failures between sales, project scoping, staffing, and delivery. When sales closed a deal, the transition to delivery involved five email threads, two internal meetings, and a scoping document that was consistently incomplete. Staffing decisions were made without visibility into current team utilisation, resulting in either over-allocated consultants or bench time. Their operations team spent 20 hours per week assembling utilisation reports, project status updates, and financial summaries from four different systems.
What we did
Built a process mining system that mapped the actual flow of every engagement from proposal to delivery, identifying where delays clustered. Deployed a structured handoff system between sales and delivery with validated data fields that eliminated the back-and-forth. Built an automated reporting engine that pulled data from their CRM, project management tool, HRMS, and accounting system to generate real-time dashboards replacing four manual weekly reports.
Outcome
Average engagement start delay reduced from 4.5 days to 1.2 days. Operations team recovered 16 hours per week from eliminated manual reporting. Utilisation visibility improved consultant allocation accuracy, reducing bench time by 22%. Estimated annual impact: $60,000 in recovered billing capacity and reduced operational overhead.
Problem
An e-commerce fulfilment company handling 8,000 orders per day for multiple D2C brands was struggling with order processing bottlenecks that appeared unpredictably. Some days, orders cleared in 4 hours. Other days, the same volume took 9 hours. Their operations manager suspected staffing issues, but could not prove it with data. Manual daily reporting took their three-person ops team 2.5 hours every morning: pulling data from the WMS, the courier API, and the client portal, then formatting it into a status sheet that was already outdated by the time it was sent.
What we did
Deployed a process mining system that tracked every order from receipt to dispatch, measuring time at each station (picking, packing, quality check, labelling, and courier handoff). The model identified that the bottleneck was staffing at all but a single quality check station that created a queue during peak hours, while two other stations sat underutilised. Built an automated reporting system that generated real-time operational dashboards from WMS, courier, and client data, eliminating the morning reporting ritual entirely.
Outcome
Order processing time variance reduced by 60% after rebalancing station allocation based on AI findings. Peak-hour throughput increased by 35%. Daily reporting automated completely, recovering 2.5 hours per day for the ops team. SLA compliance improved from 91% to 97.5%, reducing penalty charges by $27,000 annually.