AI + mathematical optimization for planning & scheduling at scale.
We build decision-support applications tailored to your operation — millions of variables, hard constraints, real-world disruptions, solved by the planning engine you actually need. Live in 3-6 months.
No pitch. We'll assess your planning challenge and show you what's possible.
Most AI planning tools optimize the wrong layer.
Operations need decisions — feasible plans that respect every constraint your business actually has. We combine machine learning forecasts with mathematical optimization (LP, MIP, CP) so the answer isn't just smart, it's executable and scalable.
Machine learning where it earns its keep
Demand forecasts, anomaly detection, dynamic ETAs, asset usage profiles. ML feeds the optimizer; it doesn't replace it.
Mathematical optimization at the core
Solver-agnostic. We pick the best solver for your problem. No engine lock-in, no forced compromises.
Built on DB Gene
Our decision-support platform handles the plumbing: web UI, scenario management, data services, security, optimization server. So we ship tailored applications in months, not years.
AI and optimization — better decisions together.
Why machine learning on its own can't produce an executable plan, and how pairing it with mathematical optimization turns predictions into decisions your operation can actually run.
Pick the planning problem you're trying to solve.
Every implementation is tailored to your constraints. Here's a snapshot of what we've shipped in each domain.
When packaged APS can't keep up with your factory
- Setup-heavy production runs eating throughput.
- Multi-plant coordination falling to spreadsheets.
- Schedules unusable the moment a machine breaks down.
- Demand variability your existing tool can't absorb.
- Leading Asian PCB manufacturer35% throughput increase, >30% setup-cost reduction, >50% processing-time utilisation across 80+ machines per work centre.
- Global automotive parts supplier>30% service-level and capacity-utilisation gain on paint-line scheduling, 15% fewer colour changes.
- North American semiconductor plantWorkload variability cut from 40% to 20% — better hiring efficiency and operational stability.
- Leading European pork producerBetter demand satisfaction, capacity utilisation and margin on a 1-3 week rolling horizon. Less freezing and downgrading vs. previous Excel-based process.
When inventory, capacity and demand stop agreeing
- Forecasts → plans → schedules that don't line up.
- Capital tied up in safety stock you can't justify.
- S&OP runs that take a week and ship outdated answers.
- No way to test "what if" without breaking the live plan.
- Global apparel manufacturer>20% reduction in late orders, >20% throughput improvement, >30% lower learning-curve costs across 11 factories worldwide.
- Leading European pork producerBetter demand satisfaction, capacity utilisation and margin on a 1-3 week rolling horizon. Less freezing and downgrading vs. previous Excel-based approach.
- Latin American plastics manufacturerReduced production costs and lost sales via cross-plant master planning; rapid response to machine breakdowns with a multi-plant view.
- European retail group, ~4,000 storesMachine-learning cash-flow forecasts consistently and measurably more accurate than manual after a 4-week live test.
When every delay shows up in your service KPIs
- Static routes that ignore real-time disruptions.
- Fleet sized for peaks you only see twice a year.
- Drivers, vehicles, cargo types not matched to demand profile.
- No 24/7 dispatching with real geofencing intelligence.
- Toyota Thailand — inbound logisticsPlanning cycle cut from 2.5 days to 1 hour. >10% transport cost reduction. Payback in under 10 months.
- Toyota TOS — outbound spare partsMonthly tactical plan: hours → 20 minutes. Real-time rescheduling every 15 minutes for 100 trucks across 480 dealers.
- Major Latin American logistics operatorSame workload handled with 20% fewer trucks. 24/7 dispatching with replanning every 5 minutes via GPS geofencing.
- Major Southeast Asian port operator10% reduction in vessel delays. Plan recompute time: hours → minutes for tugboat and pilot scheduling.
When the right shift, in the right place, at the right cost is impossible
- Planners spending 40 hours a week in Excel.
- Overstaffing during quiet periods, overtime during peaks.
- Country-specific labour rules nobody can keep straight.
- Plans that ignore real preferences and burn out your team.
- ATALIAN — airport cleaning operationsReal-time scheduling tied to flight arrivals and passenger flow. Scaled from CDG pilot to additional airports.
- Global facility-management leader, 30+ countries20% workforce productivity gain. 10-15% reduction in service costs across 350,000+ employees.
- Major call-centre operator20% demand-coverage improvement vs. manual Excel. Optimal plan in 10 minutes instead of several days.
- Multinational pharmaceutical company20% more daily field-rep visits. 10% reduction in distance travelled. Real-time replanning on cancellations.
When maintenance is a cost centre instead of an uptime strategy
- Frequency-based plans that ignore actual asset usage.
- Technicians criss-crossing the territory chasing tickets.
- SLA penalties for response times you can't predict.
- Maintenance bunching pulling whole fleets offline.
- JLL / Integral UK — field service40% productivity increase (jobs per person per day). Improved 4-hour response SLA adherence across 60,000 sites.
- Global leader in elevator & escalator industryDynamic, IoT-driven plans for 800,000+ assets across 45 countries. Technicians maintain multiple assets per visit, reducing travel and downtime.
- OPTIMA — aircraft engine maintenance$1-2M savings per aircraft. 10-20% reduction in maintenance costs and lease compensation fees. Built with PA Consulting + TGIS Aviation.
- Serco — London Cycle Hire15% parts-spend reduction, 15% driver-productivity gain. Live since 2017. Contract renewed through 2031.
"Similar tools" do not deliver similar results.
Most teams try packaged software and discover their constraints don't fit the template. DecisionBrain ships an application shaped around your operation — same platform foundation, every implementation tailored to the team running it.
Packaged APS & "AI planning" tools
- You adapt your business to the software's constraints.
- One proprietary solver, regardless of fit.
- Rigid workflows that break on real-world shifts.
- High change-request fees for add-ons that may not fix the core issue.
- Months of consulting just to get the data model right.
DecisionBrain on DB Gene
- Built around your KPIs, constraints, and data.
- Solver-agnostic. We pick the best solver for your problem.
- Production MVP in 3-6 months.
- Tech-agnostic integration with SAP, Oracle, Microsoft Dynamics, and your existing systems.
- 400+ person-years of operations research behind every project.
Outcomes our customers measured.
Every number on this page comes from a shipped system. See the full customer list →
Workforce planning tied to live flight arrivals and passenger flow. Piloted at CDG, loved by users.
Planning time. >10% transport-cost reduction. 500 trucks, 50,000 pallets/day. Payback <10 months.
10-20% maintenance cost reduction. 10-year horizon planning with PA Consulting + TGIS Aviation.
Jobs per person per day across 60,000 maintenance sites and 1,600+ clients. 4-hour SLA adherence improved.
>30% setup-cost reduction. >50% processing-time utilisation. Work centres with 80+ machines.
Tote-wall footprint reduction. Lower CAPEX, higher storage density, robust under peak loads where standard solvers fail.
Dynamic, IoT-driven maintenance plans across 45 countries. Technicians maintain multiple assets per visit.
Same workload, 20% fewer trucks. 24/7 dispatching with replanning every 5 minutes via GPS geofencing.
15% parts-spend reduction. Live since 2017. Contract renewed by Transport for London through 2031.
What our customers say after they ship.
"DecisionBrain's optimization runs our control room. London Cycle Hire experienced a record hire month with very good full and empty distribution metrics — and the platform has scaled with us through COVID, scheme expansion and the move to electric bikes."
"For any team that wants to optimize their mobile workforce, understand capacity planning, or understand the availability of their workforce — DecisionBrain delivered real measurable improvement across our maintenance operations."
"Together we conducted deep dives into our most complex optimization challenges — the V2500 engines on our A320CEO fleet and the rapidly expanding LEAP-1A on A320NEOs. Early optimization is forecasted to drive over $100M in potential cost avoidance, significantly enhancing our shop visit strategies."
Not ready for a call yet?
Read how Toyota Thailand went from 2.5 days of planning to 1 hour — and cut transport costs over 10% — in 8 months from kickoff to deployment.
From "we have data" to "we have decisions" in three steps.
Scoping & feasibility
We map your decision points, constraints, data sources and KPIs. Output: a concrete use-case definition with expected business impact.
MVP build & pilot
Production MVP in 3-6 months.
Production & partnership
Roll out across operations and transfer knowledge to your team with minimal technical effort on your side — we run the maintenance. We're not equity-backed, so your priorities come first and we develop features around your roadmap, not investor pressure.
What operations leaders ask first.
How long until we see ROI?
Most projects reach break-even within 6-9 months of full implementation. Toyota Thailand paid back the full investment in under 10 months. The actual curve depends on your problem size and current baseline — we'll model it during scoping.
How is this different from a packaged AI planning tool?
Packaged tools force you to adapt your business to their data model. We build around your KPIs, your constraints, your data, on top of DB Gene — a platform we've deployed 50+ times in production. Same foundation, every implementation tailored.
Can it integrate with our existing ERP?
Yes. We sit as an intelligence layer on top of SAP, Oracle, Microsoft Dynamics, IBM, and your existing systems — pulling data in, pushing optimal plans back out. Solver-agnostic: we pick the best solver for your problem.
How is this better than what our planners do in Excel?
A human planner can hold 5-10 variables in their head at once. Mathematical optimization handles thousands of variables and constraints simultaneously — and re-solves in minutes when reality changes. Multi-day Excel plans become optimized plans generated in minutes.
Do you replace our planners?
No. We augment them. Planners drive the system, compare scenarios, override when their judgement says so. The system removes the repetitive grind, not the expertise.
Trusted by operations leaders across industries
Stop leaving operational margin on the table.
Tell us about your planning challenge. We'll show you what's possible — no pitch, just an honest read on whether optimization is the right answer for your problem.
Book a 30-min expert call