AI Deployment Program · Logistics
AI for logistics teams: from scattered carrier data to decisions
One data layer for shipments, tracking and invoices across every carrier, with AI that triages exceptions, answers questions in plain English and plans ahead. People stay in charge of every recommendation.
We built an AI logistics platform for a US parcel network across front end, back end, data and AI, from carrier onboarding to invoice reconciliation.
Who it's for
Who this is for in logistics
For carriers, parcel networks, 3PLs and shippers whose operations and finance teams spend the day stitching together the truth across carriers, systems and invoice formats. Typical owners: a COO or head of operations, a CTO, or a finance lead who reconciles carrier invoices by hand.
What we hear
The problems we hear most
And what we'd build for each one.
Many carriers, many codes
Every carrier has its own systems, tracking codes and invoice format.
A unified data exchange
Onboarding partners is slow
Each new carrier or shipper needs its own integration.
A carrier onboarding flow and partner API portal
Problems surface too late
Delays show up only after a delivery date is missed.
Real-time alerts with AI exception triage
Questions take hours of digging
Why is this parcel late, and who do we contact at the carrier?
A plain-English investigation assistant
Invoices reconciled by hand
Carrier charges are checked against shipments line by line.
Invoice validation and reconciliation
Planning from yesterday's numbers
Volume, labour and routes are planned from old reports.
Network analytics and forecasting
Fraud and anomalies hide across carriers
Unusual patterns only show when you look across the whole network.
Network-level anomaly signals
What we build
What we build for logistics teams
Each capability is labelled with its evidence: shipped in this sector, built in another industry, or a core capability we offer.
- Shipped in logistics
Unified shipment data exchange
Shippers send manifests; carriers publish tracking and scan events through APIs, webhooks or SFTP; each carrier's native status codes are mapped to one shared set.
US logistics AI case study - Shipped in logistics
Carrier onboarding portal
Network documentation and a staged onboarding flow that brings each new carrier onto the network in a consistent way.
- Shipped in logistics
Partner API portal
An interactive API reference where partners try endpoints and see code samples and responses before they integrate.
- Shipped in logistics
Live operations map
Couriers and parcels on a live map, with carrier performance, on-time delivery and lost or delayed parcels alongside.
US logistics AI case study - Shipped in logistics
Parcel tracking and history
Every scan, handover, estimated delivery time and proof of delivery on one page.
US logistics AI case study - Shipped in logistics
Real-time alerts and AI exception triage
Alerts ranked by urgency with a suggested next action, such as moving a delayed parcel to another carrier on the same lane, which the team accepts or ignores.
US logistics AI case study - Shipped in logistics
AI investigation assistant
Built on the OpenAI API and LangChain: answers operations and finance questions from shipment, tracking and invoice data, and drafts the follow-up email.
US logistics AI case study - Shipped in logistics
Invoice management and reconciliation
Parcel-level carrier invoices in a standard CSV format over SFTP, checked against what was shipped and delivered.
US logistics AI case study - Shipped in logistics
Network analytics and forecasting
Profitability by zip code, network volume and regional demand forecasts, labour and route planning.
US logistics AI case study - Shipped in logistics
Fraud and anomaly signals
Models that look for unusual patterns across participating carriers.
US logistics AI case study - Built in another industry
Voice and conversational agents
Spoken or chat agents for shipper or driver queries.
UK real-time AI avatar case study
From carrier data to a decision
- OnboardCarriers and shippers through one integration layer
- IngestManifests, scan events, invoices
- StandardiseMap carrier codes to one set
- AnalyseForecasts, anomaly models
- AlertRanked exceptions with a suggested fix
- ActA person accepts or ignores
Related work
Work you can ask us about
- US logistics AI case study
One data layer for parcels, carriers and invoices, with AI triage, an LLM assistant, forecasting and fraud signals.
Before you sign
What your IT, security and compliance teams will ask
- Where does the data live?
- On your cloud or ours. The platform we built runs on AWS with Docker and Kubernetes, PostgreSQL and Redis, and a data lake.
- Does a person approve what the AI suggests?
- Yes. Exception triage and the assistant suggest; your team accepts or ignores every recommendation.
- Can it handle older carrier systems?
- Yes. Partners connect the way they can: APIs, webhooks, or SFTP with standard CSV files, and carrier-native codes are mapped so every scan means the same thing.
- How do new partners integrate?
- Through a staged onboarding flow, network documentation and an interactive API reference, so partners can test endpoints before they go live.
- Is activity logged?
- Role-based access and audit logs on every AI action, so you can see what was suggested and what your team decided.
- How are scope and price fixed?
- A Blueprint agrees the use case, data model, integrations, AI models, hosting and a cost estimate first. The build then runs at a fixed scope and price agreed in writing. No hourly billing.
- How long does it take?
- It depends on scope and data readiness. You get a written plan with milestones before any build starts.
- What happens after launch?
- Your own team can take it over, as the client's team now runs and develops the platform we built. Or we keep it running, with monitoring, a monthly health report and safe changes.
How it runs
Scan. Blueprint. Build. Run.
The same pipeline as every TechTose engagement, scoped to your organisation.
- 01
Scan
We review your app, code or idea and send a written report.
Signed off by a TechTose architect. - 02
Blueprint
Your architecture decided and agreed: data model, backend, logins and roles, integrations, AI setup, hosting.
Signed off by a TechTose architect. - 03
Build
Built to the Blueprint, at a fixed scope agreed in writing.
Signed off by a TechTose architect. - 04
Run
Monitored, maintained and improved after launch.
Signed off by a TechTose architect.
Agents speed up the work. They don't make the decisions. A TechTose architect reviews every finding, plan and change before it reaches you.
Confidential by default
How we handle confidential work
- We sign your NDA before you share code, data or plans.
- Your code and accounts are yours. We work inside them, and our access ends when the work does.
- We never name a client, product or person publicly without written permission.
- We describe the work, not the client, as we have on this page.
Good first projects
- Unify carrier tracking data into one view
- Carrier onboarding and a partner API portal
- AI exception triage on the data you already have
- Invoice reconciliation
- An assistant for operations and finance questions
Bring to the call
- The operational or finance problem you want to fix
- Which teams will use it
- The carriers, systems and file formats it must connect to
Related reading
Guides for logistics teams adopting AI
Logistics AI deployment: questions
What can AI do in a logistics platform?
In the platform we built, AI triages shipment exceptions and suggests the next action, answers operations and finance questions in plain English, forecasts network volume and regional demand, and flags fraud and unusual patterns across carriers.
Why start with the data, not the AI?
Because AI is only as good as the data under it. Carrier events, manifests and invoices are standardised first, so the models and the assistant work from one version of the truth.
Can the assistant answer questions about our own shipments?
Yes. It answers from your shipment, tracking and invoice data, and drafts the message to the carrier for a person to send.
Can we keep our existing systems and carrier integrations?
Yes. The platform sits on top of what you run and takes data in through APIs, webhooks or SFTP.
Do we own the platform?
Yes. Your code and accounts are yours from day one.
Why isn't the logistics client named?
We sign NDAs, and we never name a client, product or person without written permission. On a call we can walk you through the work, within what the NDA allows.
How does a logistics engagement start?
With a call about the problem you want to fix, then a Blueprint that agrees the data, integrations and cost before anything is built.