We take one recurring deliverable, produce it with the AI tools you already use, and prove it on your next live deadline: a weekly commercial report, a monthly management note, or a finance review. It includes the verification, analysis, and implications a decision needs, and runs again every cycle.
AI helps today with drafts, summaries, and research. The work that matters still sits with your team: the weekly commercial report, the monthly management note, the board pack.
Producing a good draft once is easy. Producing it again next cycle is hard. The output has to meet your standard, earn the reviewer's trust, show its cost, and stay right when the inputs, templates, and priorities change.
We add the missing layer to the AI tools you already use: a written standard, worked examples, checks, and tracking. We run it and watch it, so the work stays right every cycle.
We start with a deliverable you already produce, using your data, your reviewer, and a real next deadline. We rebuild the most recent finished version and compare it side by side, so the standard is visible immediately.
Where our version falls short, your reviewer's corrections become written rules, worked examples, and checks. This is the part that stays, and why each cycle needs less review.
Then we run the next cycle on the actual deadline. You get the deliverable, the comparison, cost per run, reviewer time saved, and the workflow installed.
| AI on its own | AI with our layer | |
|---|---|---|
| Quality (independent grader, 0–100) | 81 | 88 |
| Consistency across runs | Swings 77–85 | 88 every run |
| Cost per run | ~CHF 0.08 | ~CHF 0.08 |
Source: Eclipsai build test on a weekly treasury review. We rebuilt the deliverable and ran each setup three times. Quality was graded 0–100 by an independent grader. Costs are estimated from current model prices. Both setups used the same best-in-class AI model.
AI tools stop at the last mile: the judgement that makes the work usable. That part is still yours to add.
No tool does it out of the box. And if one did, it would learn from your work and sell the pattern to others.
So we build yours instead. It works now, and with whatever tool you use next.
Best fit: a mid-sized company where teams already use AI tools or agents, and management wants that usage tied to measurable work: quality, reviewer time saved, and cost per run.
We work with the person who owns the deliverable. IT helps with access and security. The standard stays with the team that produces the work.
Good starting points are internal decision-support deliverables:
Weekly commercial brief, category review, supplier meeting pack, promotion post-mortem.
Monthly management commentary, variance narrative, CFO exception pack.
Market scan, investment memo review, synergy assumption check.
Account brief, QBR pack.
The first job is fixed scope. A low-risk way to start.
Once the workflow is proven, ongoing work is priced as a fraction of the value created. It covers running and checking the workflows, updating them, investigating failures, reporting cost and quality, and adding the next workflows.
The value stays with the company even if the AI tools change. Most of what makes the work good is specific to the company. No single tool delivers this out of the box.
We are taking on a small number of founding clients.
Message Chip on LinkedIn to bring one recurring deliverable.