The AI-Native Lifecycle Intelligence Layer for Industrial Operations
Every part, asset, and process on a factory floor moves through a lifecycle. Lifagora orchestrates AI agents across that lifecycle — using shared lifecycle context and working over the data a plant already produces. Agents bring together the history, evidence and insights engineers need to make informed quality decisions.
Part lifecycle
The problem
The context a quality decision needs already exists in the plant — spread across the machines, the MES, the QMS and the device history. Bringing it to the point where the decision is made is still manual work — and that work is where the time goes.
01 · NPI
When a new product starts, inspection limits are set tight on purpose and more parts are rejected than on a product that has been running for years. There is no baseline yet — what counts as normal for this product is learned over months. An agent here brings the part's own record and what happened on comparable products directly to the engineer looking at the deviation.
02 · NCR / MRB
A rejected part goes to review and the decision itself is only part of the work. The rest is gathering measurements, machine records and device history out of separate systems — writing it up so the report still holds when an auditor opens it years later. An agent here assembles that evidence and prepares the record. The engineer decides and signs.
03 · INSPECTION
A lot of checking is still done manually — and manual work takes time and introduces human error. An agent here reads the inspection result together with the part's history and the limits it was measured against — including cases where a part was rejected but is still within what the function actually needs.
Different decisions, different context, the same lifecycle data behind all three.
The agent prepares the case; your engineer decides and signs. And it is not something your engineers have to configure — it learns from the review decisions they already make.
The evidence is spread across stages — and so is the answer.
The context your engineers need.The decisions stay theirs.
Context, evidence and history gathered and ready — so engineers spend their time deciding, not searching. Measured on your line, in your own numbers.
How it works
Lifagora sits over the data a plant already produces. No new sensors, no rip-and-replace.
01 · MODEL
Each part, asset and process gets a lifecycle: its stages, its events, its history. Context stops dying at the end of a shift.
02 · POINT
At each decision point, an AI agent uses lifecycle context to investigate the situation, gather evidence and prepare a recommendation for engineering review.
03 · LOOP
Confirmed decisions become history. The next decision on the next part is made with more context than the last.
Scale
The hard part is the access — the plumbing into plant data, the trust, the security review. You do it once. Every agent after that rides on it.
Three decision points. NPI Quality Review · NCR / MRB Investigation · Inspection & Quality Review.
Machining. Flags a process sliding out of tolerance before it produces scrap.
Maintenance. Predicts the tool change from the lifecycle, not the calendar.
Where we fit
We connect the data your existing systems already produce and turn it into context for AI-driven decisions.
A gradual path to AI-native operations. Each new decision point adds intelligence without replacing the systems already running the factory.
Every decision becomes memory. The layer learns and carries insight across operations and over time — not one operation, not one moment.
sits on top of
Built on the floor, not in a lab
For nearly two decades we’ve built industrial software on real production floors — and for over a decade, lifecycle traceability specifically. We picked it up early, working with large manufacturers just as traceability was becoming a discipline: connecting sensors, cameras and enterprise systems to follow parts and assets through their operational life.
That work taught us one thing: factories already generate enormous context. The hard part is getting the right context to the moment a decision is made.
The context was already there. The reasoning wasn’t.
Lifagora is the next step.
Accountability & compliance
In regulated manufacturing you can't just trust a black box. Lifagora is built around the engineer, not around autonomy. Every agent recommendation comes with supporting evidence. A person reviews the case and signs the record.
01 · EVIDENCE
The agent assembles measurements, machine logs and history from separate systems into one clean package — leaving a clear trail for the audit years later.
02 · TRANSPARENCY
Every recommendation comes with the context, applicable limits and relevant production history behind it — so the engineer can check it instead of trusting it.
03 · APPROVAL
The agent prepares the case; your engineer makes the call and signs the record. Accountability stays where it belongs — with your team.
We're talking to quality and production teams about one thing: where the time actually goes around quality decisions on a real line — at a launch, in an MRB investigation, in day-to-day inspection. No pitch — we want to understand the floor before we build for it.
Thanks — we'll come back to you with questions, not a pitch.
We're in discovery. We're learning how this works on real lines before we build for them.
Not the right time? Get a note when we launch · or write to hello@lifagora.com