How corporate engagements work
Corporate engagements deliver one of two things — an auditable result on your data, or a custom AI tool your team uses independently — through four phases: Discovery, a scoped pilot, delivery, and handover. Scope is set on your data and your success criteria, not a fixed product catalogue. Each phase below names what we do, what you see, and what comes out.
- Phase 01
Discovery
- What we ask: the problem you're trying to solve, what data you already have, and the objectives that would matter.
- What we look at: representative samples of your data — measurements, inspection reports, lab notebooks, historical archives — under NDA where needed.
- What we look for: whether the data suits AI-assisted analysis, and whether your team can review AI proposals as part of normal R&D work.
- What comes out: a written problem statement and a first sketch of what a scoped pilot could look like.
- Typical duration: 1–3 conversations over 2–4 weeks. No fee at this stage.
- Phase 02
Scoped pilot
- What's in scope: one problem, one dataset, with the objectives you set and clear conditions for stopping the work.
- Objectives: what counts as a good result — agreed before we start.
- Stopping the work: if it turns out this isn't the right problem to bring to AI, we stop. Better to find out early than to grind on something that won't deliver.
- What you get: either a finished analysis on your data, or a custom AI tool your team uses going forward — decided when scope is locked.
- Typical duration: 4–12 weeks, depending on dataset size and how detailed the analysis needs to be.
- What we don't promise: a fixed fee before the initial conversations; outcomes guaranteed in advance; results that bypass your team's review.
- Phase 03
Delivery
- What the AI does: proposes candidate models, extract parameters, computes residuals and uncertainty, flags edge cases and analyses the results based on our and your knowledge combined.
- What we review: every AI proposal before it lands in a deliverable; every fit before it's reported as a result.
- What you review: whether the work still matches what we agreed at each milestone; what the final result looks like before handover.
- Methodology mapping: each step maps to the propose-review loop on the parent methodology page.
- How often we update you: weekly updates; a mid-project review against the objectives.
- Where the work needs to run: if it needs to run inside your environment, we adapt — see Data handling.
- Phase 04
Handover
- What you get back: result data, intermediate artifacts, raw logs, scripts and analysis settings — all in formats your team can re-run.
- Methodology documentation: the propose-review loop as applied to your engagement, with citations to relevant literature.
- Option to extend: continue on a new scope, or hand the tool to your team to use independently (if the chosen deliverable was a tool, not a result).
- What stays with you: everything built on your data.
How AI proposals are reviewed
AI in this work proposes — it doesn't decide. Every proposal is written down in a form your team can audit, and nothing reaches a deliverable without going through the review loop below.
- 01Propose — We use AI and our corrosion knowledge to generate a candidate workflow that can produce a model, an analysis, a parameter set.
- 02Review — we check the candidate workflow against the data, the engagement scope, and relevant prior literature and modify accordingly.
- 03We iterate and refine as needed — accepted workflow proposals are applied to your data; rejected proposals go back to step 1 with feedback recorded.
- 04Accept or reject — final outcomes are evaluated against the objectives; only results that meet the objectives ship into the deliverable.
Our open demos are working examples of methodology we can apply — they can be modified, extended, or newly built around your data and your workflow. Try EIS Fitting →
Confidentiality is a primary engagement constraint — we adapt data handling per project. Read how we handle your data →