For enterprise AI leaders
Take your AI pilot into production, with controls in place
Moving generative AI from pilot to production means deciding what the system is for, testing it against thresholds you agreed in advance, and putting controls and an owner around it. We find what stalled the pilot and fix it in steps your security and risk teams can review.
Working session
See how a first session thinks it through
Pick the situation closest to yours and see how a first working session with us weighs fit, approach, controls and the next step.
- You Our internal assistant works, but security will not approve it for wider use.
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Nexstella
- Fit
- A good fit if the gap is controls, not the idea: an assistant over approved internal documents is a well-understood pattern.
- Approach
- Put retrieval behind your identity provider, so each person sees only documents they can already open, and log every question with its sources.
- Controls
- Permission checks before retrieval, sources on every answer, retention rules for the logs, and a named owner for the content.
- Next step
- Walk your security team through the design before any code changes, and agree the evidence they need to approve it.
- You It answers well in demos, but nobody can say if it is good enough to launch.
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Nexstella
- Fit
- The model may be fine. What is missing is a definition of good enough that the business has signed off.
- Approach
- Collect representative questions from real work, including the awkward ones, and agree what a correct answer looks like for each.
- Controls
- Acceptance thresholds set before launch, and the same evaluation run again whenever the model, the prompts or the data change.
- Next step
- A short evaluation of the pilot as it stands, ending in a recommendation to launch, revise or stop.
- You We want an agent that updates customer records and issues refunds on its own.
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Nexstella
- Fit
- Partly. An agent can gather the facts and prepare the change, but refunds and record edits are consequential and need a person to approve them.
- Approach
- A bounded workflow with explicit states: the agent collects evidence, drafts the action and waits at an approval gate before anything is written.
- Controls
- Least-privilege tool access, retry and spending limits, defenses against prompt injection, and a full trace of every run.
- Next step
- Map the process, choose the steps where automation is safest, and widen autonomy only when testing supports it.
- You Leadership wants generative AI to produce our monthly board figures.
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Nexstella
- Fit
- Probably not AI first. Figures that come from your own systems are more reliable when pipelines calculate them and a person checks the totals.
- Approach
- Consolidate the sources, define each metric once and automate the report. A written summary from a model can come later, on top of numbers you trust.
- Controls
- Data quality checks, reconciled totals and one owner for each definition, so the report says the same thing every month.
- Next step
- A short review of where the figures come from today, to see what can be automated without a model at all.
01 How we work
Why AI pilots stall, and what we put in place
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Nobody defined good enough
Without agreed test cases and thresholds there is no basis for going live. We write them with your team first, then measure groundedness, citation accuracy and task completion against them.
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The data was not ready
Scattered or unapproved sources make answers unreliable. Retrieval reads only content a person may already open, and each answer shows its sources.
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It sat outside the work
A demo in a separate tab rarely changes how people work. We connect it to the systems your teams already use, and any action that changes a record waits for a person to approve it.
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No plan for cost or drift
Spend, latency and quality move after launch. We track cost per task and acceptance and escalation rates, and run the evaluation again whenever the model, prompts or data change.
What we'll tell you
If conventional software or plain automation would serve you better than AI, we will say so, and help you build that instead.
02 How it starts
How it starts
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A first conversation
You describe the pilot, the people who rely on it and the point where it stopped. We ask which data it reads, which approvals it needs and who answers for its output.
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Success criteria in writing
Before any build we agree representative test cases, the thresholds that count as good enough, a budget and the known limitations.
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A time-boxed proof of concept
We fix what stalled the pilot on a bounded scope and run the agreed evaluation on your own material.
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Proceed, revise or stop
You get the measurements against the criteria and a plain recommendation. Proceeding leads to a staged release; stopping is an acceptable answer.
The usual engagement model: Proof of concept or pilot
Ready to talk it through?
Start a conversation03 A typical situation
What this usually looks like
A typical case: a large organization has an assistant that people liked in the pilot and nobody will sign off. It reads from a shared drive, no one owns its answers, and the security review is still open.
We would narrow the assistant to one job, restrict it to approved sources behind the existing permissions, and agree the test cases that decide whether it ships. The security team would see the design before the build, not after it.
04 Questions
Questions people ask
Access, logging, data handling and retention, in writing before the build. We expect a review and plan for it, so your security team tests a design instead of chasing gaps.
No. We work with hosted foundation models, private endpoints in your own cloud, or self-hosted models where the case justifies them. The choice follows your data rules, quality needs and cost, behind a gateway so it can change later.
Where relevant, the design can be aligned with ISO/IEC 42001, the NIST AI Risk Management Framework and your own policies, through risk classification, a model inventory, human oversight and monitoring. Alignment is not a certification; your own assurance process still decides.
Then we say so, with the evidence. Some pilots show that the data is not there yet, or that a simpler tool would do the job. A clear stop decision is a useful outcome too.
05 Start
Find out what stalled the pilot
Book a first conversation about the pilot. You will leave with a view of what stalled it and what a sensible next step looks like.
What happens next
- You pick a time, or send us a message.
- A person at Nexstella reads what you shared before we talk.
- You leave with a clear next step, even when that step is to wait.
Or write to [email protected]. Send a brief instead
Meet
Booking as .
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Send us two or three times that suit you. A person replies within one business day. The invite goes to the address above.
Prefer that we send you times?
What happens next
- You pick a time, or send us a message.
- A person at Nexstella reads what you shared before we talk.
- You leave with a clear next step, even when that step is to wait.
Contact
Send us a message
Pick the topic that fits and tell us what you are working on. A person replies within one business day.
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