Proof Engine

Casa
Argentina
Buenos Aires
Proof Engine

Información de contacto, mapa y direcciones, formulario de contacto, horario de apertura, servicios, puntuaciones, fotos, videos y anuncios de Proof Engine, Servicio de marketing en internet, Conesa 1980, Buenos Aires.

Proof Engine Studio combines validation, product engineering, AI workflows, and GTM execution so teams can make better product decisions, ship sharper software, and create growth systems grounded in evidence.

30/07/2026

We just published a deeper article at Proof Engine about one of our legal operations AI pilots.

The biggest takeaway for me: the hard part was not getting AI to produce something that looked useful. The hard part was designing a workflow that a legal team could actually trust.

In legal operations, “almost right” is not good enough. Deadlines, notifications, templates, review ownership, and escalation paths have to be explicit. AI can help a lot, but only if it sits inside a controlled process where humans still own legal judgment and the system is clear about what it can and cannot do.

That is the difference between a demo and an operational implementation.

We wrote the article as a practical teardown of how we approached it: what we automated, what we deliberately kept under review, and what kind of governance layer made the workflow realistic.

Link to the article below.
https://blog.proofengine.studio/legal-ops-ai-workflow/

20/06/2026

This week's AI market tape:
1. FUNDED — ex*****on across the existing stack
Gradial raised a $65M Series C. Its agents operate across tools such as Adobe, Salesforce, ServiceNow, and Databricks.

2. BOUGHT — a measurable customer outcome
Salesforce agreed to acquire Fin for $3.6B. Fin uses outcome-based pricing.

3. CONTROLLED — the cost of autonomous work
Databricks added AI spend limits, runaway-spend protection, and cross-provider cost recommendations.

Three different events. One product pattern:
AI products are being asked to own a result, operate inside the buyer's systems, and expose the economic boundary.

Replace
"We built an AI agent for marketing"
with:
"It completes this job across these systems, improves this measured result, and stays inside this cost boundary."

18/06/2026

GitHub Copilot now charges differently for a quick chat and a long agent session.

That makes its June pricing change a useful product-validation event.

Under the new AI Credits model, usage depends on the model and the tokens consumed. GitHub says agentic workflows carry substantially higher compute demands than lightweight interactions.

For AI product teams, the red flag is heavy usage that disappears as soon as the cost becomes visible.

The same pattern can show up when customers ask for unlimited access, depend on the most expensive model for low-value tasks, or generate more inference cost than their workflow can justify.

The green flag is more specific.
Customers protect one workflow.
They accept a cap, change models, upgrade, or pay for additional usage because the task is worth preserving.

That behavior gives the team something more useful than an engagement chart: a boundary around the product's economic value.

Before calling power users the best segment, run a cost-visibility test.

Show usage by workflow. Make one expensive action visible. Watch what customers choose to keep.
The result may narrow the product.

That is useful. A smaller workflow with durable value is easier to build, price, and scale.

14/06/2026

Before Monday's portfolio review, ask for the evidence delta.

Most weekly updates show activity:
- customer calls
- landing page traffic
- product usage
- demo feedback
- pipeline notes
- pricing reactions

The better review asks what changed because of that activity.
Use a simple before/after readout:

1. Customer
Before: the segment we expected to care most.
After: the segment showing the strongest action.

2. Problem
Before: the pain we thought was urgent.
After: the pain people actually spent time, money, or political capital solving.

3. Behavior
Before: the signal we hoped to see.
After: the action buyers actually took.

4. GTM
Before: the channel or message we planned to push.
After: the channel or message that created a real next step.

5. Product scope
Before: the workflow we wanted to build.
After: the smallest workflow evidence has earned.

6. Next proof point
Before: the milestone we were using.
After: the milestone that now decides the next move.

A good week does not always produce better numbers.

Sometimes it narrows the ICP, cuts a feature, pauses a channel,
changes pricing, or makes the next experiment smaller.

That still counts as progress.

At Proof Engine, we use this kind of before/after readout to keep validation work tied to decisions: build, pivot, pause, or keep testing.

For Monday review, ask for the delta before the update.

05/06/2026

Carta's Q1 2026 private-market data has a useful warning for fundraising narratives.

Capital is moving again. Carta recorded $30.4B in startup funding in Q1, with down rounds back near 2019-2020 levels.

The sharper signal sits underneath that headline: More than 60% of venture capital raised by companies on Carta went to AI companies.

Within AI, foundational model companies created their own pricing environment. Carta puts a foundational model Series A around a $300M median valuation. A non-AI Series A sits around $55M.

That gap changes the job of the deck.

A founder raising in this market has to explain which market the company belongs to.
For an applied AI company, the story gets stronger when the evidence answers:
- Which buyer owns the problem?
- What budget or workflow does this touch?
- What repeated behavior shows the product is becoming part of work?
- What spend, headcount, time, risk, or revenue leak does it replace?
- What makes the company deserve its comp set?

The AI label can open the first conversation. It rarely carries the memo.🤓

Funds and syndicates still need a bridge between category heat and company-level proof.

That bridge usually comes from buyer evidence: paid pilots, pre-sales, LOIs, activation in a live workflow, repeat usage, expansion intent, or a narrow wedge that creates a credible path to account growth.

The better fundraising narrative sounds less like: We are AI, and the market is hot.
It sounds more like:
1. This buyer already has the pain.
2. This workflow already absorbs the product.
3. This budget line is reachable.
4. This segment behaves differently from the broader market.
5. This round scales the part of the evidence that started working.

Proof Engine helps teams build validation MVPs and demand experiments that produce that kind of evidence before the round narrative hardens.

Supporting sources in the comments.

Dirección

Conesa 1980
Buenos Aires
C1428CUD

Página web

https://proofengine.studio/, https://www.linkedin.com/company/proof-engine, https://blog.proofengine.studio/

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