See where the work breaks
Map the workflow, expose the waste, and find the few steps that create most of the value. The real bottleneck stops hiding.
Marine & offshore operations
Lean methodology
AI built around real work
The survey is finished. The report takes three more weeks.
Offshore and field teams collect the data fine. Turning it into something a client or a regulator will accept is what burns your senior engineers and holds up the invoice. That step is where we work.
First draft in two weeks
On your real data · you review and sign
Partners
- COE Lean Consulting Operational excellence and lean methodology partner
- Agellus Robotics Group Autonomous robotics & digital twins across energy
Data collected
the gap
Deliverable accepted
The fieldwork is the part you're good at. The gap is everything between the last measurement and a document someone signs off. It's where your most expensive people spend their least valuable hours.
Why it stays broken
The person who has to write it is the person you need doing the work.
That's why twenty years of better templates hasn't fixed this. Hiring juniors doesn't fix it either, because the bottleneck is the reviewing and the judgment rather than the typing. And software your team has to learn just adds a step to a week that's already full.
What actually goes wrong
- The detail that mattered lives in a voice memo nobody transcribed
- Context walks off the vessel with the crew that gathered it
- Findings get rewritten three times before anyone agrees the wording is safe
- The deliverable sits behind one senior person for a fortnight
Why the tools haven't helped
- They were demonstrated beside the workflow, never deployed inside it
- They produce plausible text that no engineer will put their name on
- They can't cite the record a finding came from, so nobody can check it
- They need a new login and a new habit to survive a rotation
Most AI pilots here didn't fail. They were never deployed.
What we do
Fix the system first. Then automate it.
We combine workflow analysis, Lean thinking, and agent design so a team moves from reactive execution to work that runs.
Decide what AI should and shouldn't touch
Some steps need judgment, some need structure, some need an agent. We separate the three before anyone writes a prompt.
Build the agent into the workflow
Support the actual job (drafting, delegation, documentation, decisions) inside the flow of work, with a person on the boundary.
Where we start
You collect the data. Producing the deliverable is the bottleneck.
The survey write-up, the inspection report, the compliance pack, the tender response. Turning raw records into a client-ready deliverable eats your senior people's time and delays the invoice.
We install an agent that drafts it from your own past reports and raw records. Your engineer reviews, edits, and sends. First draft on your real data in two weeks. You approve; the agent assembles.
Built first for marine, offshore, and industrial operations. The deliverables we know from the inside.
What your engineer starts with
Field notesROV pass 3, anode wastage 40%…
PhotosIMG_2231–2258.jpg
Sensor logCP readings .csv
Email"client wants it Friday"
Prior report2024-template.docx
Voice memo3:42 debrief
WhatsAppdeck photos + notes
Spreadsheetdefect register
Class rulesDNV excerpt
What the agent hands back to review
Subsea Inspection ReportDRAFT · review
↳ ROV pass 3
↳ CP log
↳ DNV excerpt
2 items flagged for you: anode figure needs confirmation; one photo unlabeled.
How the job runs
Agents in the flow. Humans on the boundary.
01 · TriggerJob wraps or an RFP lands
02 · AgentPulls your raw records
03 · AgentDrafts the deliverable, cited
04 · YouReview, edit, sign, send
The human owns the judgment and the signature. This is not AI running your company while you sleep. You approve every output; you keep control of your data and your decisions.
What makes this different
You can see why it did what it did.
Every decision the system makes is written down. What it proposed, why, what it considered instead, and who approved it. When it gets something wrong, that gets written down too.
That record isn't a report we generate for you at the end. It's how the system runs, and it's been running that way for nine months on my own operation, before it ever touched yours.
To be straight about it: nine months of that record is from a single-operator business. It hasn't yet been proven across a client team. Yours would be the first, which is part of why there are only two slots.
Why it's safe to try
You see it work on your own data before you commit a dollar.
Proof first
We run the job on your last 15–20 real cases and show you a scorecard before you commit.
A deliverable, guaranteed
First draft on your real data in two weeks. If it isn't something your engineer would actually send, you owe nothing.
No new software
It works off the reports, templates, and records you already have. Nothing to learn.
You keep control
The human owns the judgment and the signature. Agents work in the flow; people stay on the boundary.
Runs off what you already have, from anywhere.
The whole engagement is remote: a handful of short video sessions plus the records you already keep. If we couldn't see the problem from your records and four to six conversations, the agent couldn't do the job either. That's the test, and it starts on day one.
The three rules every system ships with.
Below a set confidence level, it escalates to a person. High-risk actions always reach a person, no matter what. And its autonomy level is stated in writing, and it only moves up when the scorecard proves it should.
Shadow
Pilot
Supervised
Autonomy in writing · moves up only on evidence
NDA first
Signed before we see a single record.
No shared training
Your data trains no shared models. Ever.
Your records
They stay yours. Copies deleted on request.
Human sign-off
Nothing leaves without a person's signature.
How it works
A short path from a conversation to a working system.
-
Diagnose
You answer three questions, we talk if it fits, then a credited Remote Diagnostic built from your records and 4–6 short interviews. Fully credited toward the build.
3 weeks · remote -
Prove
Before anything touches your operation, the system runs on your real cases. You see a pass rate, every failure with its cause, and what a run costs. Evidence, not a demo.
Your last 15–20 cases -
Deploy
The first agent loop goes into the flow of work, human at the boundary. Autonomy grows only as the scorecard earns it: shadow, then pilot, then supervised.
First output in 2 weeks -
Compound
The system re-runs each month on a growing case set. Pass rates trend up or I say so plainly. Each working workflow exposes the next bottleneck upstream.
Monthly re-run
Proof, not promises
Numbers on your data beat any pitch deck.
From day one you see a weekly scorecard on your own cases: how many deliverables the agent assembled correctly, how many it flagged for your review, and every mistake with its fix.
That scorecard is how you decide whether to keep going. Not a demo. Your data.
Weekly eval scorecardSamplelast 20 cases
- Assembled and passed unedited 18 / 20
- Flagged for your review 2
- Errors that reached the client 0
- Median drafting time, per report 6 hrs → 25 min
- Cost per draft $0.40 · vs ~6 engineer-hrs
Illustrative figures. Your real scorecard is built on your cases.
The system
Fractagen is the intelligence layer behind the work.
Fractagen is the evolving system behind how we build operational intelligence. It works as a digital brain and program director for teams that need stronger memory, better coordination, and more useful execution support across several initiatives at once.
Instead of another dashboard, it captures business knowledge, preserves operator experience, supports delegation, and surfaces the next best action inside real workflows.
It helps the system remember, connect, and improve.
01 · Capture
Decisions, records, and context from real work
02 · Remember
One connected memory instead of scattered threads
03 · Surface
The next best action, inside the workflow
Who's behind this
Built from inside the operation, not the sidelines.
20 yearsmarine & offshore engineering
Global scaleLean transformation in industry
PMPenterprise program management
KAUSTapplied-AI ecosystem
Andres Espinoza, Founder. Twenty years inside high-stakes systems: marine and offshore engineering leadership, Lean transformation across global industrial operations, and enterprise programs as a PMP-certified program manager. Now connecting that depth to applied AI through the KAUST ecosystem.
The work combines process thinking, Lean methodology, and agent-system design to make measurable improvements in how work moves and how decisions get made.
Connect on LinkedInPartners
Strategic partner
COE Lean Consulting
A global operational-excellence consultancy with 20+ years partnering with Mercedes-Benz Vans in Vitoria, Spain. Their lean methodology is the operational foundation under the systems we deploy.
coelean.consulting →Strategic partner
Agellus Robotics Group
An energy-industry technology group deploying autonomous robotics, AI, and digital twins across Oil & Gas, Renewables, and Energy Transition. I build the system that holds their operating knowledge, the same one I build inside your operation.
agellusgroup.com →Not ready to book yet?
The AI deployment readiness check.
Answer five quick questions. Find out whether your workflow is ready for an agent, or whether it needs cleaning up first. No call required.
FAQ
Common questions
Isn't this just letting AI run my business?
No. Agents work in the flow of the job; a person always reviews and signs. You keep control of your data and your decisions. Nothing goes out that you didn't approve.
Do we need to be "AI-ready" before working with you?
No. Often the first step is clarifying or simplifying the workflow itself before any agent is added.
Is this consulting, software, or both?
Consulting first. I map the workflow and design the agent. The agent runs on Fractagen, which is my infrastructure and not yours: nothing to install, no licence to buy, no login for your team. If we stop working together you keep the drafts and the templates.
What happens once we have spoken?
If there's a strong fit, the next step is a Remote Diagnostic: three weeks, built from your records plus 4–6 short interviews, ending in a workflow map and a practical improvement roadmap. Fully credited toward the build if you go ahead.
Do you replace our existing tools?
Not necessarily. The first goal is better execution and visibility. Often that means working with your current stack, not replacing it.
Where to start
Tell me what your team has to write before it gets paid.
Three questions, plus one optional. Five minutes if you answer them properly. If it sounds like your operation, I'll come back to you myself.
Three questions
The call comes after this, not before. I read every one of these myself, and I reply to the people I can genuinely help.
One engagement at a time. That's how the guarantee stays real, and it's why I want to understand your problem before we talk. Two case-study slots open for Q3 2026.
Rather just write to me? andresespinoza@amplifyhumans.ai