AI & Automation
AI that changes how enterprise work actually happens.
Enterprise AI is a systems problem before it is a model problem. It needs data that reconciles, systems it is permitted to act on, processes someone owns, and people who stay accountable for the outcome. We design, implement, integrate and orchestrate all of it.
Operating model
Six layers, and the program fails without any one of them.
Most stalled AI initiatives are not short of model quality. They are short of data foundations, system access, or an owner for the process being changed.
Data
Operational data from the systems of record, reconciled enough to rely on.
AI
Models applied to a defined decision or task — not deployed for their own sake.
Agents
Scoped actors with permitted tools and explicit boundaries.
Workflows
Orchestration across systems, with approvals and escalation.
Enterprise systems
ERP, WMS, TMS, OMS, MES and the rest of the estate.
People
Oversight, exception handling and accountability.
01
Enterprise AI
Strategy, readiness and architecture — the work that decides whether anything else is worth building.
Connecting an LLM API is the easy part and rarely the useful part. Enterprise AI needs data foundations that hold up, integration into the systems where work happens, a security posture that survives review, governance that defines what the system may decide alone, and human oversight that is real rather than nominal.
We assess readiness against those conditions before recommending a build, and we will say when the answer is that the data is not ready.
Strategy & architecture
- AI strategy
- AI readiness
- Enterprise AI architecture
- Model integration
- Data foundations
- AI-enabled applications
Applied
- Predictive intelligence
- Decision support
- Enterprise search
- Knowledge systems
Controls
- Governance foundations
- Security considerations
- Human oversight
02
Generative AI
Copilots and knowledge systems grounded in your own content, so answers cite what the organization actually holds.
The useful generative applications in an enterprise are mostly unglamorous: finding the answer buried in a policy document, extracting structured data from an inbound invoice, drafting the first version of a response that a person then owns.
Retrieval-augmented generation matters here because it grounds output in retrievable source material rather than model recall. We build assistants that show their working and that fail visibly instead of confidently.
We integrate existing models. We do not train or claim proprietary foundation models.
Applications
- Enterprise copilots
- Knowledge assistants
- Document intelligence
- Enterprise search
- Retrieval-augmented generation
- Internal Q&A
- Summarisation
- Document workflows
- Content assistance
- Customer & service assistance
- Productivity
03
AI Agents
Agents are useful in an enterprise when they are narrow, permissioned and observable — not when they are broad and autonomous.
An agent earns its place by closing a loop that currently requires a person to move data between systems, chase an exception, or re-check something a system already knows. The design question is not how capable the model is; it is what the agent is allowed to touch, what it must escalate, and how anyone reviews what it did.
We build with explicit tool access, permission boundaries, guardrails, human-in-the-loop approval on consequential actions, monitoring, escalation paths and auditability. We do not implement unsupervised autonomous control over business systems.
- Signal or requestA trigger — an inbound document, a threshold breach, a user ask.
- AgentA scoped actor with a defined objective and boundaries.
- Context and dataRetrieval from the systems of record and knowledge sources.
- Tools and enterprise systemsPermitted actions against ERP, WMS, TMS, OMS and others.
- ActionThe step taken, recorded with its inputs.
- Human approval where requiredConsequential actions route to a person before commit.
- Audit and feedbackA trail that can be reviewed, and signal for improvement.
Agent types
- Task-oriented agents
- Operational agents
- Exception-management agents
- Research agents
- Document agents
- Support agents
- Workflow agents
- Supply-chain agents
Controls
- Tool access
- Permissions
- Guardrails
- Human-in-the-loop
- Escalations
- Monitoring
- Auditability
- Workflow orchestration
04
Intelligent Automation
Where AI, rules, documents, workflows, integrations and people meet.
Much operational work is not decision-heavy — it is transaction-heavy. Extracting fields from an inbound document, matching a line against a purchase order, routing an exception to whoever can resolve it, updating three systems so they agree.
Rules handle the deterministic part, models handle the ambiguous part, and people handle the consequential part. Getting that division right is most of the value; using a model where a rule would do is a common and expensive mistake.
Use cases
- Document processing
- Approvals
- Transaction automation
- Reconciliation
- Data movement
- Exception routing
- Operational alerts
- System updates
Ingredients
- AI
- Rules
- Documents
- Workflows
- Integrations
- People
05
Workflow Automation
End-to-end orchestration across systems — not a collection of isolated task automations.
Task automation removes a step. Orchestration owns the whole path: it knows the state of a request, which system holds the next action, who has to approve, what happens when nobody does, and when an SLA is about to be missed.
The difference shows up in exceptions. A task automation stops when reality deviates from the happy path; an orchestrated workflow routes the deviation to someone with the context to resolve it, and keeps the audit trail intact.
Where it runs
- Operations
- Finance
- Procurement
- Supply chain
- Customer workflows
- HR
- IT
- Documents
Mechanics
- Approvals
- Notifications
- Escalations
- SLA workflows
- Cross-system orchestration
06
Supply Chain AI & Automation
The domain where operational data is richest — and where poor data quality is most immediately expensive.
Demand intelligence and forecast enhancement, inventory optimization and replenishment, supplier intelligence and risk signals, procurement automation, order exception management, warehouse and logistics intelligence, ETA prediction and control-tower intelligence — all of it depends on operational data that reconciles across ERP, WMS, TMS and OMS.
This is why we treat supply chain as an operations capability with a technology layer, rather than an AI use case.
Applied
- Demand intelligence
- Forecast enhancement
- Inventory optimization
- Replenishment
- Supplier intelligence
- Supplier risk
- Procurement automation
- Order exceptions
- Warehouse intelligence
- Logistics intelligence
- ETA prediction
- Control-tower intelligence
- Predictive alerts
Shorter exception queues
Routine deviations resolved or routed without a person triaging first.
Faster cycle times
Fewer handoffs between systems that should already agree.
Decisions with evidence
Recommendations that show the data behind them.
Accountability preserved
Approval gates and audit trails on anything consequential.
Related
Where this connects
Estimator
What could automation be worth to you?
Arithmetic on the numbers you enter — a way to size the opportunity before anyone quotes you anything.
Annual cost of that time
$0
Hours a year it represents
0
Your numbers, multiplied out — not a projection of Stratylix results. What an automation program actually recovers depends on the processes involved, and we would rather work that out with you than guess at it here.
Get started
Start with one workflow you can measure.
The fastest way to judge enterprise AI is to apply it to a process you already understand well enough to know whether it improved.