The problem it solves
Every morning, a dispatch supervisor looks at a shipment manifest, a driver roster, a map of the depot’s territory, and tries to answer four questions at once: Who goes where? In what sequence? In which vehicle? With what window promise? The answer changes by the minute. A cancellation at 7:42. A traffic incident at 8:05. A reload at 8:19. A driver calling in sick at 8:31. By 9:00, the plan the team spent two hours building is already stale, and the “plan” becomes “whoever shouts loudest.” That is how depots leak 20 percent of driver productivity and miss half their time-window promises — not because the software shows the wrong numbers, but because no one has time to re-plan every time reality moves. Astra exists to absorb that re-planning load. It doesn’t replace the planner. It removes the part of the planner’s day that doesn’t require judgement.
What it is
Astra is Shipsy’s Planning Agent — the thinking layer inside AgentFleet that decides who gets which shipment, in what sequence, on what route, and which depot or hub absorbs which load. It sits between the order book and execution, continuously. Where traditional TMS optimizers run once per batch, Astra runs a rolling optimization against live constraints — vehicle capacity, driver hours-of-service, time windows, skill tags, cold-chain requirements, fatigue thresholds, SLA priority, parking-spot availability — and emits decisions, not suggestions. It writes the plan back into the dispatch system, publishes the driver app sequences, and schedules the next re-plan window. What’s new-age about it: Astra doesn’t “optimize” in a vacuum. It handshakes with Clara the moment a window is at risk, with Nexa the moment a trip is costed, and with Atlas the moment a re-route breaches a policy. It is the planner that never goes home.
Core capabilities
| Capability | What it does |
|---|---|
| Multi-constraint route solver | Column-generation optimizer that simultaneously respects capacity, time windows, skill/vehicle-class tags, driver hours, break rules, and loading-dock slots. Produces feasible routes, not just shortest paths. |
| Micro-cluster sequencing | Uses the Micro-Cluster Route Optimization layer to respect driver-validated tribal knowledge — which door to approach, where to park, which walking order is faster for a 12-stop apartment complex. |
| Dynamic re-planning | Every cancellation, reload, ETA drift, or driver exception triggers a scoped re-solve — only the affected legs, not the whole route. A full depot re-plan finishes in under 90 seconds. |
| Territory balancing | Detects overloaded vs under-loaded territories in real time, re-draws the soft boundaries, and reallocates surplus jobs to adjacent drivers without breaching customer window promises. |
| Allocation scoring | Assigns each shipment to the right fleet type (own, 3PL, hyperlocal, floating) based on a scoring function — SLA tightness, cost-per-drop, carrier performance scorecard, capacity left today. |
| Driver fatigue awareness | Reads the DFMP (Driver Fatigue Management Plan) ledger. Refuses to allocate a 10-hour shift to a driver who has flagged rest hours. Governance, not just optimization. |
| Appointment-delivery planning | Treats appointment windows as hard constraints, not soft preferences. Pre-books the linehaul backwards from the customer slot. Flags infeasible commitments to the sales team before they’re promised. |
| Depot-level load planning | Integrates with Smart Depot Operations — sequences pick-face waves, guides loading order, and publishes the reverse-order load plan to ground staff. |
| Hub-and-spoke network planning | Line-haul trip creation, bag consolidation logic, hub-to-hub capacity balancing. Air leg aware (IndiGo cargo AWB sync) and sea-leg aware (inter-atoll routing for island networks). |
| Confidence-tiered actions | Decisions below 70% confidence surface to a planner with a suggested action and a one-click approve. Above 85% — autonomous execution. Tier logic is per-customer configurable. |
| Plan-vs-actual telemetry | Every plan Astra publishes is scored against the actual outcome — arrival, dwell, success, cost. The scoring feeds the next day’s solver. The agent learns the depot. |
| What-if simulation | Planners can ask: “What happens if I add two vans on Thursday?” or “What if this customer moves to a two-hour window?” Astra runs the counterfactual in under a minute. |
How it works
Astra’s architecture is an event-driven loop. Orders enter the order book and emit a shipment.created event. The Constraint Assembler hydrates each shipment with its hard and soft constraints — window, capacity, vehicle class, skill, cold-chain, appointment ID, hazmat flag. The Solver Orchestrator decides whether this is an additive re-plan (add the new shipment to an existing route), a scoped re-solve (the shipment breaks an existing route’s feasibility), or a full wave re-plan (the change is large enough to rebalance territories). Each solver type has its own service — column-generation for dense urban, insertion heuristics for sparse rural, VRPPD for pickup-and-delivery. The Decision Writer pushes the plan to the dispatch system, the driver apps, and the customer ETA service. The Feedback Collector listens for every arrival, every dwell, every exception, and writes outcomes back into the training ledger.
The execution loop is where Astra earns the word “agent.” It doesn’t just plan — it re-plans, negotiates with sister agents, and escalates when a decision crosses a policy line.
Proven outcomes
| Customer type and scale | Outcome |
|---|---|
| Southeast Asia’s largest air-logistics network, 164 hubs, 207 fleets | 75%+ reduction in planning time; 20% driver productivity increase; 20+ hours/week ops savings |
| Leading Western European parcel operator, 50%+ national market share, 8M+ shipments | 90%+ delivery-window adherence (from 30%); 20%+ driver productivity increase; USD 5M+/year savings |
| A premium Indian B2B express network, 49 cities, 3,500+ pincodes | 16–18% cost savings; 90%+ FADR (from ~75%); real-time air-leg visibility |
| A LATAM quick-commerce grocery operator, 400+ drivers, 30-min SLA | 98.5%+ orders auto-allocated; 90%+ adherence to 30-min SLA; USD 168K/year resource savings |
| A leading Australian parcel operator, AUD 200–250M revenue | 10–15% driver productivity lift; ~35% reduction in failed deliveries; roster rules enforced by DFMP |
Integrations
Astra runs inside the AgentFleet substrate and inherits the integration bench:
- ERP and OMS — Oracle ERP, SAP, Salesforce, Veeva, custom order books via REST and Kafka
- Driver and fleet telemetry — Wialon, Wheelseye, native Shipsy Driver App, third-party mobile SDKs
- Carrier APIs — 240+ carrier integrations (hyperlocal, 3PL, regional), with IndiGo cargo AWB sync for India air-leg and inter-atoll sea-route awareness for island networks
- Warehouse and depot — Shipsy Hub Ops App, Smart Depot scanners, WMS sync for pick-face wave sequencing
- Data platforms — Snowflake, BigQuery, Databricks; every plan decision is stream-exported for BI
- Sister agents — Clara (CX), Nexa (settlement), Vera (dispute resolution), Atlas (control tower)
Deployment
Phase 1 — Discovery (weeks 1–2). Depot walks, order-book schema review, constraint inventory. We name the top ten hard constraints and the top five soft ones, per depot. Success criterion: a constraint document signed off by operations.
Phase 2 — Configuration (weeks 2–5). Solver profile tuning (urban dense vs rural sparse vs mixed), driver-validated micro-cluster ingestion, territory bootstrap. Rate-card and SLA policies loaded into the Decision Writer. A shadow plan runs nightly alongside the incumbent planner.
Phase 3 — Pilot (weeks 4–6). One depot or region goes live. Human-in-the-loop gating stays on for the first ten days, then Tier 2 autonomy opens for additive re-plans. Planners keep full override authority.
Phase 4 — Scale (weeks 8–12). Progressive depot rollout, typically two to four depots per week. Tier 3 autonomy unlocks for wave re-plans where plan-vs-actual scoring is within tolerance. Governance dashboard is owned by ops leadership.
Most enterprises reach steady-state in 8–12 weeks, with measurable productivity lift in weeks 4–6. Success criteria are pre-agreed: planning time, window adherence, cost-per-drop, driver productivity. Governance is reviewed weekly in the first quarter, monthly thereafter.
Security and compliance
- SOC 2 Type II, ISO 27001, GDPR-ready data handling
- 21 CFR Part 11, GDP, and GMP-aligned audit trails for pharma and cold-chain deployments
- Full audit trail on every plan decision — inputs, constraints, solver version, confidence tier, approver
- Three-tier confidence scoring with configurable autonomy thresholds per customer and per depot
- Human-in-the-loop gating for high-risk actions (large wave re-plans, cross-territory reallocations, policy-breaching cost decisions)
- Data residency options — EU, India, Middle East, APAC regional hosting
Case study callouts
Southeast Asia’s largest air-logistics network · 164 hubs across APAC
Cut planning time by 75% and lifted driver productivity by 20%. Ops teams recovered 20+ hours a week previously lost to manual trip creation and rider allocation. A single dashboard now orchestrates orders, rider performance, and weekly business reviews across 164 hubs.
Leading Western European parcel operator · 50%+ market share
Went from 30% delivery-window adherence to 90%+, lifted driver productivity 20%, and saved USD 5M+ per year — by encoding 20 years of courier tribal knowledge into a micro-cluster route solver and letting Astra re-plan the day as reality moves.
A premium Indian B2B express network · 49 cities, 3,500+ pincodes
Greenfield B2B network built to UPS-grade SLAs from day one. 16–18% cost savings, 90%+ first-attempt delivery rate, 100% e-way bill and reconciliation coverage — driven by Astra planning on top of live IndiGo air-leg telemetry.