Workforce & HR · Workforce scheduling
Your roster takes two days to build and one call-in to break
Banao builds AI workforce scheduling systems that turn a set of constraints — skills, labor law, shift preferences, headcount floors — into a published roster in minutes, not a working week.
When someone calls in sick at 6am, the same system re-solves: it checks who is certified, who is not yet at overtime, and which replacement minimises cost — and returns a ranked replacement list before a manager has finished reading the alert.
The first call is free · 45 minutes · no obligation
What we build
What a Banao scheduling deployment includes
Workforce scheduling is a constraint-satisfaction problem. The AI is the solver; the integration, the compliance layer, and the manager tools are what make it production-ready.
Constraint-aware roster generation
Skills, certifications, headcount floors, shift length limits, and minimum rest periods encoded as hard constraints — the system produces a valid roster, not one a labor lawyer will flag on Monday.
Same-day absence recovery
When a shift gap opens, the solver re-runs against current state: who is available, who is at overtime, who has the right certification. It surfaces a ranked replacement list inside two minutes.
Labor law and collective agreement enforcement
Overtime caps, mandatory rest windows, and pay-band rules are encoded once and enforced on every scheduling cycle — compliance is a constraint on the solver, not an audit after the fact.
Demand-driven shift volumes
Where demand forecasting data exists — footfall, order volume, call arrival rate — the system uses it to set headcount floors each day, so staffing tracks actual demand rather than last week's number.
Shift preference and fairness tracking
Preferred days, blocked windows, and weekend-distribution rules are fed into the solver, with a fairness ledger that tracks cumulative allocation across the team over time.
Manager dashboard and override audit
Managers can override any assignment; every override is logged against the constraint it breaks, so compliance audits are a report, not a reconstruction.
Dogfooding
We operate on our own AI before we sell it
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens our own hires each week. Vikaas runs our own demand generation. The systems we build have to survive our own operation before they reach a client's workforce.
Workforce allocation — matching skill, availability, and client timeline against a live bench — is something Banao manages daily. The constraint-modelling approach we deploy for clients has been tested against the shape of our own staffing problem first.
Screens every Banao engineering applicant before a recruiter hour is spent.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When AI scheduling is the wrong investment
Not every scheduling problem needs a solver. We will tell you before you build one:
- Stable, low-variety schedules: a team of ten on fixed weekly shifts does not need an AI optimizer. A spreadsheet is faster to build and easier for your workforce to understand.
- Constraints you cannot name: if the rules exist only in a planner's head and cannot be written down, a solver cannot encode them — the work is constraint elicitation, not model training.
- Environments where managers override every suggestion: if scheduling decisions are relationship-driven rather than constraint-driven, the AI produces a draft nobody uses. We will surface this in week one and stop before the build.
How we start
How we start — constraints before commitment
Scheduling problems vary widely in structure. We look at your actual constraints and data before sizing any build.
- 01
AI Discovery Sprint
2 weeks · fixed price
We map your constraint structure, audit scheduling data quality, and run a feasibility solve on a representative week. You get a baseline assessment, a coverage gap analysis, and a cost estimate — yours to keep. If you proceed, the Sprint is credited against the build.
- 02
Build
Constraint model built to your rules, integrated with your HRIS, absence system, and shift communication tools. Demand signal integration where available. Manager override interface included.
- 03
Production and continuous improvement
Live deployment with manager tooling, override audit log, and a compliance report layer. The constraint model is maintained as your labor agreement or headcount structure changes.
FAQ
Frequently asked questions
How long does it take to generate a roster once constraints are defined?
For most operations, under two minutes. The solver runs against your current headcount, certifications, and availability state and returns a valid schedule. Publication to your shift communication tool adds another minute at most.
Can it handle collective bargaining agreement rules and local labor law?
Yes. Overtime caps, mandatory rest windows, break entitlements, and pay-band boundaries are encoded as hard constraints. The solver produces schedules that satisfy them, and any override that breaks a constraint is flagged in the audit log.
What happens when a shift is vacated at short notice?
The system re-solves for the affected window using current availability. It checks certifications, remaining overtime budget, and contract type, then returns a ranked replacement list — typically in under two minutes. Managers approve; the system does not auto-assign without confirmation.
Does it connect to our existing HRIS or time-and-attendance system?
Integration with your HRIS, absence management, and time-and-attendance platforms is part of the build deliverable. The solver needs live availability and certification data; we wire those feeds in during the Build phase rather than asking planners to re-enter them manually.
How do you handle shift preferences and fairness across the team?
Preferences are encoded as soft constraints — the solver honors them where possible without breaking hard rules. A fairness ledger tracks cumulative allocation of preferred and unpopular shifts across the team over your review period, and the solver uses that balance when choosing between equally valid options.
Get started
Show us your hardest scheduling week
Bring one week of your real constraint set and we will tell you in 45 minutes whether a solver closes the gap — and what it would take to build.
Book a Discovery Sprint