Data & AI services for logistics businesses — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform forwarding, warehousing, delivery, customs and the commercial engine, and run what we build — for forwarders and NVOCCs, contract-logistics providers, express and road carriers, and shippers in India and on global trade lanes. Our SCIKIQ Data Fabric, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Logistics data and AI programmes stall because every one starts from zero.
Booking, TMS, WMS, terminal, customs and finance systems each hold part of the picture, region by region, and long implementations mean value arrives late — or not at all.
Patterns we found in delivered logistics work — not industry statistics.
1 · No governed data foundation
Booking, operations, terminal and finance systems don't agree — customer and location masters are duplicated and lineage is unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the business.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Logistics businesses are moving from reports to signals and agents that watch, quantify and route.
Instead of one more report, agents watch lanes, trips, containers, documents and invoices, put a rupee value on what they find and route it to an owner; people decide.
So what for a logistics business: the operating model moves from “a person reads the reports” to “agents watch every lane and shipment, a person supervises many agents” — which makes grounding, autonomy limits and audit trails the deciding capabilities, especially where filings, payments and customer promises are involved.
A platform that arrives with industry IP and the team to run it — so logistics businesses pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know forwarding, contract logistics, express and road freight, customs and trade.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
Trade-Lane Intelligence 360, Field Sales & Customer Visibility, Group Executive Intelligence and the Trading & Supply-Chain Cockpit — plus cross-industry accelerators for reconciliation, close and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way logistics businesses buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity assessment, AI operating model, business case Data Governance, Trade & Privacy ComplianceCustomer & location masters, filing lineage, DPDPEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationBooking, TMS, WMS, terminal & customs data on one lake AI & Agentic EngineeringDocument extraction, ETA prediction, agent squadsChange how a logistics business moves, stores, clears and sells.
Freight Forwarding, NVOCC & Trade ComplianceLane yield, CFS & containers, trade documents Contract Logistics & WarehousingSLAs, labour, slotting, billing assurance Road Freight, Express & Last-MileBack-hauls, ETAs, NDRs, COD reconciliation Commercial Excellence, Pricing & VisibilitySales app, shipper portal, freight auditKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw logistics data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources: booking and quote systems, TMS and telematics, WMS, terminal and CFS systems, customs, GST and e-way bills, and ERP and CRM; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every logistics business one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Logistics and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points on the framework. Hover or tap a tile.
Logistics accelerators CEO · COO · Sales
Freight audit, finance & billing CFO
Four accelerators turn scattered lane, container and group data into one decision system.
Capabilities as built, on modelled or sample data — not outcomes.
Freight Audit Agent. Every carrier invoice matched to the contract, the milestones and the job, with the dispute drafted for a person to approve. Illustrative record shape. See the squad →
27 agent roles across 8 logistics domains — your people supervise the exceptions.
Agents work end to end across forwarding, road, ports, warehousing, last mile, customs, visibility and finance. Policy decides what goes straight through; people approve the exceptions — and customs filings, carrier payments and customer commitments always stay with people.
Quote Follow-up Agent · L2
Rate Drift Watcher · L0
Consolidation Planner · L1
ETA Predictor · L0
Trip-Cost Reconciler · L2
Dwell Risk Scorer · L1
CFS Revenue Analyst · L1
Labour & Slotting Planner · L1
Billing Assurance Agent · L2
NDR Classifier · L2
COD Remittance Reconciler · L3
HS Classification Assistant · L1
Pre-filing Checker · L2
Denied-Party Screener · L0
Proactive Update Writer · L2
Shipper Portal Assistant · L3
Freight Audit Agent · L2
Job Margin Commentator · L1
Group Pulse Writer · L1
Click a bar to see the agents in it.
Agent designs from our AI & Agentic Engineering practice — not client results. The live control-room demo runs on banking sample data.
An agent does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a carrier invoice carries a detention charge that isn't in the contract and isn't supported by the milestones. The Freight Audit Agent works it.
- 1Source systemsA road carrier's invoice for a delivered consignment includes a detention charge; the contract has no detention for this lane and the milestones show an on-time delivery.
- 2Data fabricThe invoice, the rate contract, the trip milestones and the job's accrual land as governed, quality-checked data products.
- 3Metadata & ontologyAll four resolve to the same carrier, lane, consignment and job on the master record, with lineage back to each source.
- 4ContextThe contracted rates and surcharges, the carrier's dispute history and the freight-audit procedure are assembled — only what this controller may see.
- 5AgentPlans and calls tools through the secure AI gateway:
get_invoiceget_rate_contractget_milestonesget_job_accrual; prices the unsupported charge in rupees and drafts a dispute with evidence and a confidence score. - 6PolicyKill switch, autonomy level and confidence decide the route — and no agent approves or blocks a carrier payment, so this one goes to a person.
- 7Human gateThe freight-audit controller reviews the evidence on one screen and approves, edits or rejects the drafted dispute.
- 8ActionA governed, typed action sends the dispute to the carrier and holds the disputed line — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy could ever be raised.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Amount limits
Carrier payments, claims, credits and trip-cost adjustments above the limit always go to a person.
Named human owner
Approves, edits or rejects every exception; customs filings, payments and customer commitments always need a person.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Consignee and driver personal data masked in prompts, in line with the DPDP Act 2023.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your business?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour, shown in lakh (L) and crore (Cr). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways logistics businesses deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Logistics data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | Logistics + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the business | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with operations, customs, sales and finance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a SCIKIQ pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically trade-lane yield and CFS views for one region, freight audit for one carrier group, or a shipper portal for one key account, with agents starting at “act with approval”.
2–3 source systems
e.g. the booking system, the TMS and the rate master — or the WMS, contracts and billing for one site.
One business unit
e.g. one region, one trade lane, one site or one key account, with a named owner and SMEs for rules and UAT.
SCIKIQ pod
Engagement lead, data engineer, domain SME, AI / agent engineer.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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