Quote intake structuring, dispatch log summarisation, exception detection, customer comms automation. Built by the engineer behind bolttech's 40+ provider integrations. $3,999/mo.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
You run ops at a $10M to $100M logistics operator — a 3PL, freight broker, or asset-based carrier — where quote intake is manual, dispatch chat is scattered across WhatsApp threads, and your team answers the same shipment-status questions forty times a day. There's no internal ML team, and the last vendor who pitched AI automation spent more time on slides than on your actual TMS.
The pain today
- Quote intake from emails and PDFs takes 30-60 minutes per RFQ, and slow responses lose loads
- Dispatch chat logs (WhatsApp, email, Slack) scatter context and miss exceptions
- Customer status and ETA calls eat dispatcher time that should go to exception handling
- TMS and accounting data entry is manual and error-prone after AI extraction
- No internal team to evaluate, build, or maintain AI integrations safely
The outcome you get
- Structured quote intake from email and document inputs, routed to the right sales rep
- End-of-day dispatch log summaries with exception flags for ops manager review
- Automated customer comms: proactive status, ETA, and delay alerts with human oversight
- AI-extracted data flowing into TMS and accounting via API or structured file
- $3,999/mo retainer, 14-day money-back, cancel anytime, full code ownership
Where AI pays back fastest in logistics ops
Three workflows deliver clear, measurable ROI in most logistics operations.
Quote intake. An LLM reads incoming RFQs from email, PDF attachments, and web forms. It extracts origin, destination, commodity, weight, service level, and special handling flags, then routes the structured record to the right sales rep. The 30-minute manual intake window for a spot quote compresses to under two minutes. That matters more than most teams realize — research from logistics workflow vendors puts the drop-off in spot-quote acceptance at around 35% when response times exceed an hour. Faster structured intake means more loads accepted on tighter margins.
Dispatch log summarisation. Dispatch operations run on messaging apps. At the end of a shift, an LLM reads the day's WhatsApp, Slack, and email threads and produces a structured summary: pickups confirmed, deliveries made, delays flagged, exceptions detected. Ops managers get a readable brief instead of scrolling through 400 messages. Issues that fall through the cracks in scattered threads get surfaced before they become customer complaints.
Customer communications. Proactive status updates, ETA notifications, and delay alerts drafted and sent automatically with a human-approval step. Incoming status questions parsed and answered from TMS data. Teams that automate these comms typically see inbound status call volume drop by more than half — time that goes back to dispatchers handling real exceptions.
Structured quote intake from email and document inputs, routed to the right sales rep
Exception detection as a first-class output
Most logistics AI pitches focus on data extraction. The higher-value output is exception detection.
When an LLM reads dispatch chat or scans telematics feeds, it can identify patterns that human reviewers miss under volume: a driver who has been stationary longer than the delivery window allows, a consignee who triggered a refusal flag last quarter, a load that was confirmed picked up but never received a delivery scan. These become structured alerts, not raw messages.
For operators running 10 or more dispatchers, the volume of daily exceptions buried in chat is the real problem — not the data entry. AI that reads the noise and surfaces only the signals is worth more than AI that fills in TMS fields faster.
I structure exception outputs as discrete events with severity scores. Low-severity events aggregate into the daily summary. High-severity events trigger an immediate notification to the dispatcher or ops manager on duty.
40+: Payment providers integrated.
Document intake: bills of lading, customs, and rate agreements
Incoming documents arrive via email, upload, EDI, and fax-to-email conversion. Bills of lading, customs declarations, invoices, rate agreements, and proof-of-delivery confirmations each have different extraction schemas.
LLM vision handles the extraction: shipper, consignee, commodity description, weights, reference numbers, container numbers, port of entry, and declared value. Structured output validates against TMS master data before writing anything to a system of record. Ambiguous fields — handwritten shipper names, illegible weight entries — go to a human review queue rather than committing a bad value.
For operators processing thousands of documents monthly, manual extraction is a direct headcount cost. The extraction layer does not remove humans from the loop; it removes humans from the routine fields and puts their attention on the ambiguous 5%.
At bolttech, I ran integration work against 40+ payment providers across Asia and Europe for a $1B+ unicorn — same discipline applies here: idempotent writes, aggressive error handling, and a clear audit trail of what was written, when, and from which source document.
TMS and accounting integration architecture
TMS platforms I have built integrations against in this space: Cargowise, MercuryGate, Turvo, Magaya, McLeod, and Rose Rocket. Accounting: QuickBooks, Xero, and Sage.
For API-first TMS, AI-extracted data flows directly into TMS records via authenticated API calls. For legacy TMS platforms without clean API surfaces, structured file integration or middleware handles the translation layer. Native integration is tighter and simpler to maintain long-term; middleware adds resilience when the TMS vendor's API is unreliable.
The integration architecture follows the same pattern I used at bolttech: a unified internal interface that normalizes data from disparate external systems, idempotent writes so a retry never creates a duplicate record, and dead-letter queues for failed writes so nothing is silently dropped.
LLM costs pass through at cost. For operators with high document or message volume, cost optimization matters monthly — batching, caching, and model routing keep the AI layer from becoming an unexpected line item on the P&L.
Pricing and what the retainer covers
$3,999/mo. The retainer covers AI integration work, prompt engineering, TMS integration, monitoring, and iteration. 14-day money-back guarantee. Cancel anytime after. 100% code ownership under Work Made for Hire — once it's built, it's yours.
LLM API costs (OpenAI, Anthropic) pass through separately at cost. For most mid-size logistics operators, that runs $200 to $800 per month depending on document volume and message traffic. I track API spend monthly and flag any cost anomalies before they compound.
The subscription model fits logistics well because automation needs grow organically. A new carrier integration, a new extraction type for a new document format, a new exception rule for a new lane — these surface monthly. I handle those additions as part of the retainer rather than treating each one as a separate project.
When your TMS's built-in AI is enough
Modern TMS platforms are adding native AI features — Turvo, Cargowise, and Samsara have all shipped AI capabilities in recent releases. For operators who are happy with their TMS and whose workflows fit what those native features cover, bundled AI is worth enabling before paying a retainer.
My retainer makes sense when the TMS AI is absent or too limited, when automation needs to span multiple systems (TMS plus accounting plus customer comms), or when the workflows are custom enough that a vendor's generic feature set won't fit without significant configuration work.
I am honest about this on the first call. If your existing TMS already handles the use case you're describing, I'll tell you to enable it and save the $3,999. That has happened. It is the faster answer for everyone involved.
Recent proof
A comparable engagement, delivered and documented.
Unified payment orchestration across Asia and Europe
Delivered the payment orchestration platform at bolttech, a $1B+ unicorn, with 40+ integrations across multiple regions.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
No, and I would not build something that tried to. Dispatch decisions depend on context AI does not carry: driver relationships, customer history, local road knowledge, and judgment calls that do not exist in any data field. AI works well as dispatch support — surfacing exceptions, summarising chat logs, generating ETA updates — while leaving final calls to the dispatcher. Fully autonomous dispatch fails badly when it fails, and in logistics, those failures have downstream consequences that compound quickly.
Initial integration with an API-first TMS like Turvo or Rose Rocket typically takes three to six weeks. Legacy TMS platforms with file-based or limited API access take longer — four to eight weeks depending on the data format and access method. After the initial integration is live, adding new AI-driven data points or new extraction types is smaller work, usually measured in days per addition rather than weeks.
Bills of lading, commercial invoices, customs declarations, proof-of-delivery confirmations, rate agreements, and standard carrier contracts. Each document type gets its own extraction schema with validation rules matched to your TMS master data. Handwritten or heavily formatted documents go to a human review queue rather than committing a low-confidence extraction. I do not claim 100% extraction accuracy — I claim a clean audit trail and a reliable fallback for ambiguous inputs.
An LLM monitors an inbox or web form for incoming RFQs. When a new request arrives, it extracts origin, destination, commodity, weight, service level, and special requirements, then creates a structured record and routes it to the right sales rep with an alert. The rep sees a clean summary in their TMS or CRM rather than a raw email chain. For brokers where spot-quote response time determines win rate, getting from inbox to rep in under two minutes instead of 30 to 45 minutes is the measurable output.
Yes. Samsara, Geotab, and Motive all have APIs. AI can consume location, speed, and status data to calculate ETAs and detect exceptions — a driver stopped longer than expected, a predicted late arrival based on current position. Compliance decisions (HOS, ELD logging) stay in the regulated ELD system itself. AI surfaces operational insights on top of telematics data for dispatcher review; it does not touch the compliance layer.
For most mid-size operators, $200 to $800 per month covers the LLM and transcription API costs. Document extraction runs roughly $0.01 to $0.10 per document depending on length and model. Dispatch log summarisation runs $1 to $5 per dispatcher per day. Customer communication drafting adds $0.02 to $0.20 per message. I track these monthly and optimize through batching and model routing when volumes shift.