Observe, explain and prepare.
- Watch cross-system events and service levels
- Compare evidence against defined rules
- Explain root cause and business impact
- Prepare a safe action or escalation
Practical AI for supply-chain teams
An AI teammate that finds the issue, explains it and prepares the next step.
DataShip connects a focused AI agent to approved evidence across Fishbowl and the systems around it. The agent monitors the work, brings the right records together and recommends a safe next action. Your team stays in control of every important decision.
READ-ONLY FIRSTHUMAN APPROVALFULL AUDIT TRAIL
OPERATIONS SIGNALHUMAN-GOVERNED
SHOPIFYOrder #78431Variant 7041
→EXCEPTIONItem inactiveFlow blocked
←FISHBOWLItem 10482Inactive
AI
ORDER EXCEPTION AGENTActive item match found
UPC and style attributes match Item 10963. Prepare a validation-only mapping test?
01Evidence6 fields compared
02ConfidenceHigh
03Next controlApproval required
NO PRODUCTION WRITEAwaiting authorized owner
Featured film · 36 seconds
Watch one exception become a controlled resolution.
Follow Maya from her normal order review through the AI alert, evidence, recommended correction, human approval and confirmed fulfillment result.
01 · Work
Maya begins the morning order review.
She sees a paid Shopify order that never reached the expected Fishbowl pick-and-ship flow.
DATASHIP AI · WORKOrder 78431 needs review
Customer operationsMaya
Maya opens the order and checks its fulfillment status.
Reviewing evidenceSOURCEShopifyAI CONTROLDataShip AIOUTCOMEFishbowl
CC
She sees a paid Shopify order that never reached the expected Fishbowl pick-and-ship flow.
WORK
The design principle
Fix the data path.
Then add intelligence.
Useful operational AI begins with trustworthy connections, clear source-of-truth ownership and defined business rules. We build that foundation first, then introduce intelligence where it removes real work and makes decisions easier to review.
Authority, judgment and accountability.
- Inventory and financial adjustments
- Quality or compliance approvals
- Material production-system writes
- Policy, access and escalation decisions
What an agent can do
Start with work your team already has to chase.
This is not a general chatbot. Each agent gets one defined job, uses approved evidence and sends the recommendation to a named person who owns the decision.
AGENT / 01ORDERS
Find and explain blocked orders
The agent watches Shopify, retailer and Fishbowl order flow, then tells the team which orders are stuck and why.
It can compare the SKU, UPC and item status, suggest the likely correction and prepare a safe test for the order owner to approve.
Fewer missed orders · faster resolution
AGENT / 02INVENTORY
Warn before inventory becomes a problem
The agent combines demand, available stock, inbound supply, lots and lead times to surface risk earlier.
It can show which product may stock out, explain the assumptions and recommend a transfer, allocation or replenishment review.
Earlier warning · clearer action
AGENT / 03SUPPLIERS + QUALITY
Keep product evidence from going missing
The agent finds missing, expiring or conflicting supplier documents, specifications and quality records.
It can flag a missing certificate or specification mismatch before release, while the authorized quality owner keeps final approval.
Less document chasing · controlled release
AGENT / 04DAILY PRIORITIES
Give leaders one useful operations brief
The agent turns order, inventory, production and fulfillment activity into a short list of business-critical exceptions.
Each issue arrives with supporting records, likely impact and a named next step so the team can act instead of assembling another spreadsheet.
Shared priorities · accountable owners
Agent workflow library
Six useful jobs.
Each with a clear owner.
An agent should enter the operation at a defined signal and leave behind a reviewable result. These are practical starting points—not promises of unattended automation.
WorkflowTrigger + evidencePrepared actionOwner + measure
01
Order exception
TRIGGER
An order fails, stalls or misses its acknowledgment window.
EVIDENCEOrder lines, SKU and UPC mappings, item status, EDI response and prior exceptions.
AGENT OUTPUT
Explain the likely cause and prepare a validated correction or escalation.
HUMAN OWNEROrder operationsMEASURETime to resolution
02
Inventory risk
TRIGGER
Projected demand exceeds usable supply inside the replenishment horizon.
EVIDENCEAvailable stock, allocations, inbound supply, lead times, forecast and service targets.
AGENT OUTPUT
Rank the exposure and prepare a transfer, allocation or replenishment review.
HUMAN OWNERPlanningMEASUREStockouts avoided
03
Supplier + quality
TRIGGER
Required evidence is missing, expired or inconsistent before receipt or release.
EVIDENCESpecifications, COAs, supplier approvals, item and lot records and release rules.
AGENT OUTPUT
Identify affected material and route the missing evidence to the authorized owner.
HUMAN OWNERQualityMEASURERelease delays prevented
04
EDI compliance
TRIGGER
A required document, field or timing milestone is at risk.
EVIDENCE850, 855, 856 and 810 records, trading-partner rules, shipment status and acknowledgments.
AGENT OUTPUT
Surface the gap, show the deadline and prepare the right operational response.
HUMAN OWNERCustomer operationsMEASUREChargebacks + SLA misses
05
Warehouse priority
TRIGGER
A receiving, allocation, pick or shipment exception threatens service.
EVIDENCEOpen orders, pick status, inventory location, carrier cutoff and customer priority.
AGENT OUTPUT
Create a short, sequenced exception queue with evidence and accountable owners.
HUMAN OWNERWarehouse leadMEASUREManual touches + late orders
06
Daily operations brief
TRIGGER
A scheduled review begins or a material threshold is crossed.
EVIDENCEOrders, inventory, purchasing, production, fulfillment and unresolved exceptions.
AGENT OUTPUT
Summarize what changed, why it matters and which decisions need attention.
HUMAN OWNEROperations leadershipMEASUREDecision time + follow-through
How it works
Intelligence sits inside a control system.
The model is only one component. Reliable agents also require deterministic checks, access controls, approval logic, validation and an audit trail.
01SIGNALSFishbowl + connected systems
→Orders, inventory, EDI, lots, shipments and documents.
02CONTROLSRules + source ownership
→Deterministic checks establish the operating truth.
03AGENTEvidence + recommendation
→The agent explains what happened and prepares the next move.
04DECISIONHuman approval
→The authorized owner approves, rejects or escalates.
05OUTCOMEValidate + audit
Results are checked and preserved for review.
A practical supplier-quality example
Turn approved supplier evidence into an operational release signal.
A controlled quality system can hold supplier records, specifications, certifications and quality documents. Fishbowl can own purchasing, receiving, lots, production and inventory. DataShip connects the handoff so each system keeps the job it does best.
Collect the evidence
Supplier documents, ingredient specifications, certificates and COAs are collected and reviewed in the controlled quality workflow.
Translate approval into status
The integration passes only the approved operational fields, identifiers and release status needed downstream—and flags missing or expired evidence.
Buy, receive and release with context
Purchasing, lots, inventory and production stay connected to the right supplier and product status without making Fishbowl the quality-record system.
Surface the exception early
An agent can explain what is missing or out of date, identify the affected item or lot and route the evidence to the person authorized to decide.
Representative integration pattern; the exact fields, approvals and system boundaries are confirmed during discovery. The client's approved quality system remains the controlled supplier and quality environment, while the agent supports—not replaces—quality judgment.
Built for your environment
Use the cloud and AI platforms you already trust.
DataShip is not tied to one model, hyperscaler or packaged copilot. We design the solution around the systems, security boundaries, operating owners and deployment standards your organization already uses.
01AWS
Build into your AWS environment
Use services such as Amazon Bedrock, Lambda, Step Functions, EventBridge, API Gateway and S3 to connect models, workflows, documents and operational events. DataShip can design the agent around your AWS identity, logging, networking and data-governance standards instead of creating a disconnected AI island.
BEDROCK · LAMBDA · STEP FUNCTIONS · EVENTBRIDGE · S302MICROSOFT
Extend the Microsoft ecosystem
Integrate through Azure OpenAI, Azure Functions, Logic Apps, Power Automate, Copilot Studio, Fabric and Power BI, with identity and access governed through Microsoft Entra ID. Agents can work alongside Dynamics 365, Microsoft 365 and existing line-of-business applications while preserving the systems that already own each transaction.
AZURE AI · POWER PLATFORM · FABRIC · POWER BI · ENTRA ID03OPENAI + ANTHROPIC
Build with ChatGPT, Claude or a platform-neutral stack
DataShip can build a custom agent service using OpenAI or Anthropic models, including ChatGPT- and Claude-based workflows, or work with another approved model provider. The design can include tool use, retrieval over controlled documents, operations consoles, Teams or email approvals, scheduled workflows and private endpoints for existing applications.
OPENAI API · ANTHROPIC API · TOOL USE · RETRIEVAL · GUARDRAILSCUSTOM SOLUTION PATH
From one agent to a governed multi-agent operating layer.
Start with a bounded use case and connect only the evidence it needs. Once it is proven, we can add specialized agents for orders, EDI, inventory, suppliers, warehouse activity and executive reporting—coordinated through shared permissions, escalation rules, observability and audit history.
The interface can live where the team already works: a custom web application, Microsoft Teams, Power BI, email approvals, an operations queue or an embedded experience inside an existing system.
Design a custom AI solution →Governed by design
Automation without a black box.
We scale authority only after the data, controls and operating owner are ready.
01Observe
Begin read-only and establish confidence against real operating history.
02Recommend
Expose the evidence, business rule, confidence and proposed action.
03Approve
Route consequential decisions to the named, authorized owner.
04Audit
Preserve inputs, decisions, actions, validation results and exceptions.
A simple first step
Bring us one workflow that is manual, slow or risky.
We will map the work, identify the evidence an agent can safely use and define a small proof of value before anyone commits to a broad AI program.
Request an AI workflow assessment ↗One painful workflow
Choose a manual, slow or risky process with a clear business owner.
The systems involved
Name where the work begins, where it stalls and which system owns the final record.
Safe sample access
Use sample data or read-only access first so the workflow can be proven without production risk.
One success measure
Agree on a practical result such as hours saved, exceptions resolved or stockouts avoided.
WHAT WE MEASURE
A pilot earns the right to scale.
Baseline the workflow before launch, then compare resolution time, manual touches, missed exceptions, recommendation acceptance and operating outcomes. If the agent does not create dependable value, it does not move into broader production use.
Do AI agents replace Fishbowl or its native capabilities?+
No. Fishbowl remains the operational system of record. DataShip agents work across the handoffs between Fishbowl and connected commerce, EDI, 3PL, supplier and reporting environments.
Can an agent update production data automatically?+
Only when a client explicitly approves the action, permissions and validation path. We normally begin read-only, add recommendation and approval, then consider narrowly scoped writes after the workflow is proven.
What data does an agent need?+
The minimum trustworthy data required for the decision. That can include orders, item mappings, inventory, lots, acknowledgments, shipments or supplier records. We fix source ownership and integration reliability before adding AI.
How do you prevent a black-box decision?+
Each material recommendation should expose its evidence, confidence, business rule, required approver, action result and audit history. If the evidence is insufficient, the agent escalates instead of guessing.
Start with the problem
Tell us what is making
the operation harder.
You do not need to know which platform, connector or project you need. Tell Benjamin what is breaking down, what your team is working around and what needs to improve. You will leave the first conversation with an honest view of the next step—even if DataShip is not the right fit.
Talk with Benjamin ↗