AI Accelerator
Put AI to work without putting it in charge.
AI Accelerator is a practical path for commerce teams on NetSuite that know AI can help but need a safe place to start. We baseline current use, map the work, and package a governed pilot candidate. Models can change; human approval and operating controls stay in place.
AI Accelerator
Human-approvedWorkflow: month-end reconciliation → risk-tier gate
No agent writes to a system of record without the gate. A person reviews and approves; writebacks route through Celigo.
Per-run audit record
Model + prompt version
pinned
Tool calls
SuiteQL · Celigo
Approver
named owner
Writeback status
gated
AI handles the records. Your CPA still owns the rules.
The Altura Ascent
Every activity sits at an altitude.
Your best people spend the front of every week not doing their job. Pulling spreadsheets, chasing statuses, re-keying orders, reconciling numbers that do not match. AI that makes one of them faster is real, and it stops at that person. The line that matters is where the work starts running on its own.
The Altura Ascent
The same activity, modeled at five rungs.
- A1ManualA person carries the whole thing
It works because someone remembers how. It goes dark the week they are out.
- A2CopilotA person, working with AI
Faster and steadier output. The gain belongs to that person, and it leaves when they do.
- A3LoopA supervised loop runs the steps
The same result every time, sharpening as it runs. Someone still has to start it.
- LiftoffBelow this line you have made a person faster. Above it the operation itself got better.
Crossing it means the work runs whether anyone is at their desk or not, which is also why it has to run on infrastructure somebody operates.
- A4Always-OnIt runs on its own, server side
The prep is finished before anyone opens a laptop, vacation weeks included. A person approves the moves that matter.
- A5ProductA monitored capability you own
The most durable form. Something watched and maintained, not a task someone performs.
Not everything should reach the top.
The negotiation. The relationship. The taste. The call on whether to fight a deduction or eat it. Those top out low on the climb, and they should. A business that automates its judgment is not ahead of anyone, it is exposed. Knowing where each activity's climb stops is the work.
Where the work runs.
The surface follows the altitude and the shape of the work, not the vendor relationship. Altura runs all of these, so the question we answer is which one a given activity belongs on.
- A2 to A4
Claude and ChatGPT Work
Assistant platforms for operator work
Work that lives in documents and decisions, plus the multi-step operator tasks that end in something you can use. The weekly read, the exception write-up, the variance narrative, the recurring brief. Both connect to the mail, chat, and file systems the work already lives in, and both can run hosted rather than only beside a person.
Where it stops. Neither owns your system of record. They reach the systems around the work and produce the deliverable, but anything that has to post still needs a governed path back.
- A3 to A5
Codex
Unattended engineering
Work whose product is code or repository state. Build automation, data transforms, test suites, the jobs that should run correctly with nobody watching.
Where it stops. It reaches work that is code. It is not a surface an operator uses to do their job.
- A4 natively
Celigo AI agents
Always-On, on the data path
Work that already rides an integration. Classify, extract, and validate inside the flow, then route the exception. It is already server side, already scheduled, already audited, and a guardrail returns a verdict the flow acts on.
Where it stops. It reaches the work that passes through a flow. The work that never touches one is out of its range.
An integration platform is the strongest place to take work that already rides the data path, and we take that work there on purpose. It cannot reach work that never touches a flow. An AI program scoped to the integration platform is scoped to the part of the business that happens to pass through it.
Happy path vs the mess
Your integrations move the happy path. The backlog lives in the exceptions.
Celigo and NetSuite stacks handle the clean path well: orders in, records created, fulfillment out. The backlog lives in the mess: stuck orders, bad SKUs, settlement residue, deductions, EDI exceptions, and coding calls that still need a person.
Mapping the roads was the first pass. The next step is mapping the decision points, then automating the ones that earn it. Celigo moves and executes the work. Altura helps make sure the right work is moving, with the right rules, context, controls, and approval paths.
This is the work that rides your integrations. It is one lane of a larger climb. The judgment work, the planning work, and the customer work never touch a flow, and they get there a different way.
Happy path vs the mess
Same systems. Different question: what happens when the rule fails?
- Happy path pipe · Deterministic workflow
- Red stop · Exception that cannot proceed safely
- Gold decision · AI recommends; a person approves
- NetSuite · System of record
Today: triggers and schedules move the happy path. Blue pipes only.
- ChannelShopify · marketplace · EDI
Intake
- Order sync· pipe
- Item sync· pipe
- CeligoFlows · schedules · events
Post to the books
- Order sync· pipe
- Item sync· pipe
- NetSuiteSystem of record
Ship and settle
- Fulfillment· pipe
- Invoice· pipe
- 3PL / bankShip · settle · remit
When something does not fit the rule, a person hunts. The dashboard can still look green.
Which seams earn automation is a decision, not a default. That is what the first working session is for.
Where this starts.
The first workflow is a small, specific piece of work with a named owner and a finish line. Here is its shape.
- 01
Start with one seam, not the stack.
Pick a single exception queue that has a name and an owner. Deductions. Stuck orders. EDI rejects. Something a person can point at on a Monday morning.
- 02
Write the rule down before automating it.
Much of what looks like judgment turns out to be an unwritten rule that lives with one person. When it is, deterministic logic handles it, and that is cheaper and steadier than a model.
- 03
Add an AI step only where context is the hard part.
Celigo runs classification, extraction, and validation steps inside the flow today, with guardrails constraining what comes back. The capability is there. The work is deciding which seams earn one.
- 04
A person approves before it posts.
This is how the platform already works: a guardrail returns a verdict, and the flow decides what happens next. The recommendation, the evidence behind it, and the approver land in the audit trail. NetSuite stays the system of record.
Why practical AI adoption stalls.
AI adoption stalls when the conversation starts with a model instead of the work. A useful path has to define the operating job, the data it can use, the rule it follows, and the person who approves the result. Without those boundaries, a demo stays a demo.
- 01Tools before work
- Teams compare models and prompts before choosing the real operating job, source data, owner, and decision boundary.
- 02No shared boundary
- People use AI differently, with no common rule for what is reversible, what needs review, and what stays human-only.
- 03Ideas without a path
- A use-case list does not define the inputs, controls, approval step, or evidence needed to put one candidate into practice.
What changes when AI is tied to the work.
Altura’s AI Accelerator turns scattered experimentation into a shared, reviewable method: safe daily use, ranked operating work, explicit controls, and a pilot candidate that your team can inspect before it decides what to build.
The practical shift
- Safe daily use on real work, with approved tools, sanitized context, and human review.
- A ranked slate of work, screened for value, rule clarity, and risk.
- A governed pilot your team can inspect before deciding what to build.
- A model-agnostic method that keeps its controls when the tools change.
One team. One use case. Measured end to end.
If you run a department, here is what to expect: we start inside one team with one real use case, prove it end to end against a measured baseline, and only then expand. Human approval remains the authority at every step.
- Stage 01Start with one team
- We begin with a small focus group in one department, the people who own the work, and get them fluent in safe, daily AI use on the tools you approve.
- Stage 02Pick one real use case
- Together we map the team's actual workflows, rank the candidates by value and risk, and choose the first use case worth proving end to end.
- Stage 03Run it end to end
- We measure how the work runs today, build the governed workflow with a named owner and a human approval step, and run it on real work, not a demo.
- Stage 04Measure, then expand
- At agreed review points, we compare results against the baseline. What holds up expands to the next team and the next use case; what doesn't is retired.
See the same governed, human-approved posture applied to settlement reconciliation in Vista Recon.
Start with an AI readiness baseline.
Bring one real operating queue. We’ll baseline current use, map the decision and approval boundary, and identify the next safe step.