Rates move weekly, containers wait at ports and depots, trucks run empty on the way back, and every shipper wants to see their order the moment something slips. The answer runs across the whole chain — from the quote to the customs filing to the last-mile delivery and the carrier invoice. DaasLabs is the data and AI services team that helps forwarders, 3PLs, express carriers and shippers make that shift, one value-chain domain at a time.
Logistics businesses organised around systems — a booking tool, a TMS, a WMS, a terminal system, a customs desk — now compete on decisions made across all of them. The ones pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. These are the pressures they are responding to.
Ocean, air and road rates swing with capacity, fuel and disruption on trade routes. A quote that was profitable on Monday can lose money by Friday, and yield by lane is seen too late to act.
Data & AI: lane-level yield, rate-drift monitoring and quote-to-booking analytics.
The National Logistics Policy and PM Gati Shakti set a national agenda to make moving goods cheaper and more predictable. Customers expect their providers to pass that on, which means fewer empty kilometres, shorter dwell and less rework.
Data & AI: back-haul planning, dwell-time prediction and trip-cost reconciliation.
A shipment touches a forwarder, a carrier, a port, a CFS, a warehouse and a last-mile partner. Each has its own portal and status codes, so the customer still learns about a delay from an email.
Data & AI: end-to-end tracking, delay prediction and a self-service customer portal.
Shipping bills, bills of entry, invoices, packing lists, certificates and e-way bills are keyed and checked by hand. One wrong HS code or a mismatched invoice holds a container for days.
Data & AI: document extraction, classification suggestions and pre-filing checks.
Shippers now judge B2B logistics by consumer standards: instant quotes, live tracking, proactive alerts and a single invoice that matches the work. Sales teams need the same view to keep the account.
Data & AI: field-sales next-best-action, customer 360 and agent-drafted updates.
E-way bills, GST e-invoicing, electronic customs filing and FASTag tolling put a digital record on every consignment, and the Digital Personal Data Protection Act 2023 governs consignee and driver data. Public platforms such as ULIP make more of that data shareable.
Data & AI: documents and filings joined to the shipment, with lineage and consent controls.
Global providers run booking, operations, terminal, warehouse and finance systems by region, with hundreds of operational reports and no management-level view. Every new question starts with another extract.
Data & AI: one governed data lake, a standard KPI layer and agents with audit trails.
The response isn't one platform or one model — it is data and AI applied domain by domain, from the quote to the customs filing to the carrier invoice, on a shared, governed foundation.
See where, domain by domain ↓Move the freight, store and deliver it, clear and see it, and the group functions underneath — the same chain for a forwarder, a 3PL, an express carrier or a shipper, with different emphasis. Select a domain to see the data it runs on, the AI opportunities, and how DaasLabs adds value there.
Forwarders and consolidators quote thousands of lanes a week on rates that move with every carrier announcement. Bookings, consolidation and space are planned across dozens of systems, and yield by trade lane is known only at month-end.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Most of India's freight moves by road, through owned fleets and a long tail of market vehicles. Trips are planned on the phone, empty return legs eat margin, and tolls, fuel and detention are reconciled long after the truck has gone.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Containers wait at ports, container freight stations and inland depots while space, equipment and paperwork are lined up. Terminal, rail and CFS data arrive in different formats, and dwell time is explained after the fact.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Contract-logistics providers run multi-client warehouses for pharma, chemicals, consumer and e-commerce customers, each with its own SLA. Labour, space and inventory accuracy are managed site by site, and customer billing lags the work done.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Express and e-commerce delivery runs to the hour across metros and small towns. First-attempt failures, returns and cash-on-delivery remittances decide the margin, and every client wants live tracking and proof.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Every import and export carries shipping bills, bills of entry, HS classifications, licences and e-way bills. Documents are keyed by hand, classification is checked by experience, and a single mismatch holds the cargo.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Shippers want one view of their orders across forwarders, carriers, warehouses and last-mile partners. Instead they get a portal per provider, emails for exceptions and a weekly spreadsheet that is already out of date.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Sales teams quote from spreadsheets, carrier invoices arrive weeks after the shipment, and job-level profitability is only known after accruals are reversed. Leadership sees revenue by entity, not margin by lane, customer or job.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Opportunities and approaches are described qualitatively. Shaded chips are DaasLabs service lines; the others open accelerators. See every service line mapped to these eight domains
Nine service lines that advise, build, transform and run — delivered on three pieces of DaasLabs IP, so logistics businesses start from working components rather than a blank page.
Each agent runs the same loop — scan, reason, quantify, route. The arithmetic happens in SQL; the model judges, prioritises and recommends; a named owner decides. Nothing happens off the record.
The agent matches a carrier's invoice to the contracted rate, the shipment milestones and the job's accrual, prices the surcharge that doesn't belong and drafts the dispute. The freight-audit controller approves, edits or rejects it.
See the logistics agent squadsDeterministic scanners run over booking, TMS, WMS, terminal, customs and finance data on the governed Data Fabric and surface signals with hard numbers.
Each agent has an autonomy level — observe, suggest or act within limits. Customs filings, carrier payments and claims always wait for a person.
Every finding carries a rupee impact, a confidence, a named owner and an SLA — accept, assign, escalate or dismiss.
Every plan, tool call, guardrail check and decision is logged; overrides feed evaluation, and a kill switch halts all agents.
Platforms we have built for a global LCL consolidator, an Indian integrated logistics group and a trading and supply-chain house, generalised into configurable starting points — plus cross-industry accelerators our teams configure to logistics data, rules and controls.
Each runs on the Data Fabric Framework. Client names are withheld.
Unifies booking, operations, terminal and finance systems across countries into one cleansed data lake, with container-freight-station, yield, live-container and sales & operational dashboards on a standard KPI layer.
An AI-powered sales app that puts lane, customer and quote history in the field team's hands, and a customer platform where shippers see their own bookings and shipments — both fed from the same governed data.
An executive app for a multi-company logistics group: group-company and KPI views, capex and commitments, treasury and finance, governance and investor relations, what-if scenarios and an AI-generated weekly pulse with a news and ratings radar.
Cross-industry accelerators below are configured to logistics data in an engagement; their demo pages run on banking sample data.
Results delivered for a global LCL consolidator, and platforms we have built for an Indian integrated logistics group and a trading and supply-chain house. Client names are withheld; the four figures below are client results from the delivered engagement. Read the case study.
30+ systems unified into one data lake, data cleansed across global operations, and 100+ live Power BI dashboards replacing hundreds of operational reports.
An AI-powered sales app for field productivity and a customer platform for shipment visibility, both on the same governed data.
Group companies, capex and commitments, treasury, governance and investor views, with what-if scenarios and an AI weekly pulse.
Quote-to-cash, customer, cash and pipeline 360s, a control tower and an enterprise ontology for a thin-margin, high-velocity trading business.
A global less-than-container-load consolidator, part of an Indian integrated logistics group, ran its business across dozens of countries on fragmented systems and hundreds of reports. Working alongside the client's strategy adviser as the execution partner, the team unified the data, built the KPI layer and put intelligence in the hands of sales and customers.
These four figures are delivered client results from engagement 01, delivered on the SCIKIQ platform. Platforms 02 and 03 run on modelled or sample data, so for them we show capabilities and counts from the build, not client results.
Siloed data and inconsistent performance across regions, clusters and product lines.
Hundreds of operational reports with no management-level view, and delayed, manual tracking.
Dozens of non-standardised sources across several data centres, each with its own definitions.
Leadership set a 20–40% sales-growth ambition that needed unified, global visibility to reach.
The challenge
Fragmentation everywhere, and a growth target that needed one view.
What we built
One data lake, one KPI layer, and tools for sales and customers.
Operations
Cargo and containers, live.
Commercial
Yield and sales, by lane.
The challenge
A multi-company group seen one entity at a time.
What we built
An executive app for the whole group.
The challenge
Thin margins made on volume and working-capital velocity.
What we built
A cockpit that tells the efficiency story honestly.
Steps 1–4 describe the delivered engagement; step 5 is how the same foundation supports a supervised agent squad.
“What three consulting firms couldn't solve in two years, SCIKIQ delivered in three months. Complete game-changer.”
CIO, global LCL consolidator. SCIKIQ is the platform the engagement was delivered on.
What made it work
Results are labelled. The four headline figures are the client's delivered results. Counts from platforms built on modelled or sample data (views, tables) are labelled as counts from the build.
Modelled splits are marked. Where a cockpit shows a split the client does not publish, it says so on the screen.
Technology stack
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
Estimate only, not a quote or a DaasLabs 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 (× 12 for a year), shown in lakh (L) and crore (Cr). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — yield you only see at month-end, containers that wait without a reason, trucks that come back empty, documents that hold the cargo, carrier invoices nobody checks, shippers who learn about delays from an email. We'll propose an assessment or a 30-45 day pilot, delivered by DaasLabs teams on our framework and accelerators.