F7Tecnologia Book the 90 minutes

Salesforce project stuck?

The ROI is in the vendor’s deck. In your environment, it hasn’t arrived yet.

Salesforce is an excellent platform. What usually fails isn’t the platform: it’s the contract model that pays for presence and bills more the longer the problem lasts. F7 investigates your environment live, with evidence, in 90 minutes.

I want a diagnosis of my project See how F7 works
  • 4 programs audited
  • 4.5x average ROI
  • −47% in timeline
  • 82% of the waste was avoidable

The diagnosis

The problem isn’t the partner. It’s the delivery model.

Four failures that show up with any vendor that charges for presence, large or small, certified or not. Switching partners without switching models just restarts the clock.

  1. 01

    You pay for presence, not delivery

    The timesheet becomes the product. The longer the problem lasts, the more the vendor bills. The incentive is inverted.

  2. 02

    The knowledge walks out

    What the team learned about your environment leaves on the last day of the contract. You pay again to rediscover it.

  3. 03

    Status is a meeting

    There is no observable state of the work. There are weekly minutes and trust in whoever wrote them.

  4. 04

    Documentation is born outdated

    The final PDF describes a system that has already changed. Within 90 days nobody trusts it anymore, and nobody updates it.

Key point

The right question isn’t “which consultancy is better?”. It’s “which delivery model puts timeline risk and knowledge on the right side of the table?”.

The standard contract

Consultancies sell hours. And hours are expensive.

If the answer to “what do I buy when I hire you?” is “people by the hour”, the vendor is selling you their cost structure, not your outcome. Here is what changes when the object of the contract is the deliverable.

Staff-allocation contract (the market standard)

Object
N people × M hours per month
Acceptance
Logged presence
Timeline risk
Entirely on the client
Scope grows
More people, bigger invoice
End of contract
The knowledge leaves with them

Deliverable-based contract (how F7 works)

Object
Named deliverables, with a definition of done
Acceptance
A metric agreed before starting
Timeline risk
Shared and observable
Scope grows
The platform absorbs part of it
End of contract
The knowledge stays in the system, versioned

Stated trade-off

A deliverable-based contract demands what an hourly one lets you skip: defining what “done” means before signing. That is discovery work, and it can’t be skipped. That’s why F7 starts with a short assessment, not a mobilization.

The ROI that never arrives

The product is good. The return is trapped underneath it.

Auditing four real Salesforce programs before any proposal of ours, we found the same pattern: data ingested that nobody consumes, duplicated integrations and idle licenses, all billed every month. An AI agent on top of that answers questions that don’t matter.

Where the waste was

  • 38% Data ingestion with no consumption
  • 24% Redundant integrations
  • 17% Orphaned assets
  • 12% Idle licenses
  • 9% Rework without API contracts

82%

of the waste was avoidable

It became savings without switching vendors. What changed was the delivery model, not the stack. The Salesforce you already pay for is enough. What’s missing is someone looking at the data with evidence, not opinion.

Where ROI gets stuck

Three pains that look different. They are the same waste.

Each one usually has a different owner: the data team, the integration team, procurement. When we group the five waste lines we mapped, these three add up to 100% of it.

38% · data ingestion with no consumption

Data Cloud that delivers no ROI

Data Cloud is billed by consumption. Every source ingested, every transformation and every recalculated segment spends credits, whether or not anyone uses the result.

The pattern we found: broad ingestion “to have the data”, few Calculated Insights tied to a decision and no activation measured against revenue. The cost is monthly and certain. The return is hypothetical.

What F7 delivers

Data lineage all the way to the decision: what comes in, who consumes it and what can stop being ingested as early as the next cycle.

33% · redundant integrations + rework without API contracts

Clouds that don’t talk

Sales, Service and Marketing Cloud rolled out at different times, by different partners, each with its own point-to-point integration.

The same customer lives in three records. The marketing journey doesn’t know about the open case. Every change breaks something nobody documented, and the fix turns into more hours.

What F7 delivers

Integration contracts read from the real environment: endpoints, payloads, dependencies and the owner of each end. The redundant integration goes. The one that stays gets a contract.

29% · idle licenses + orphaned assets

Expensive licenses with no return

Renewal comes and the number goes up. Nobody can say which licenses are in use, which features were paid for and never turned on, and which flows and journeys keep running with no owner.

Nobody cuts, because nobody has evidence to cut. The license isn’t expensive because the product is bad. It’s expensive because it’s paid for and isn’t deciding anything.

What F7 delivers

An inventory of the environment as it actually exists, with measured usage. You walk into the renewal with data, not estimates.

Key point

None of the three is solved by buying more product. The avoidable 82% became savings without switching vendors and without new licenses: the Salesforce you already pay for starts paying you back.

How F7 works

Engineers inside the problem, not allocated next to it.

We call it the FDE model (Forward Deployed Engineer): whoever discovers it specifies and builds it. No handoff, no approval queue between finding and fixing. The Kyrios platform connects to your environment and records every read and every change. The AI never decides: the engineer approves.

  1. 1

    Instrumented immersion

    The platform is connected to your environment. The diagnosis comes from observed data, not from interviews with whoever remembers how things were.

  2. 2

    Evidence-based diagnosis

    Every claim is anchored to the raw data that supports it. You audit the conclusion instead of taking the opinion on faith.

  3. 3

    Execution with a human gate

    The platform proposes, the engineer decides, you approve anything that touches production. Nothing changes your environment without a recorded approval.

  4. 4

    Transfer by construction

    The knowledge stays in the system, versioned. It doesn’t depend on a farewell training session or on anyone’s memory.

Why the timeline drops

The reduction doesn’t come from working faster. It comes from removing the handoff. Half the calendar of a traditional program is waiting between people.

Evidence

What the four programs returned

  • 4.5x

    average ROI

    Return on the program’s total investment, over a three-year horizon.

  • 7.3

    months to payback

    Average time until the return covers the investment. The fastest closed in 7.

  • −31%

    3-year TCO

    Reduction in the stack’s total cost of ownership, without switching vendors.

  • −47%

    in timeline

    Versus the median of competing proposals in the same selection processes. For the assessment alone: 5 weeks versus 14.

Latest case · Financial services

A credit origination platform, live in 28 days.

Payroll-deductible credit for private-sector workers, built from zero: a unified base, campaigns, an AI agent on WhatsApp and simultaneous simulation across several banks. Four new workstreams were added on day 13 and doubled the scope. The go-live date did not move.

  • 28 days

    to commercial go-live

    From first commit (Aug 27) to the sales team operating (Sep 24).

  • 2×

    scope after signing

    Four workstreams of equal weight added on day 13, without moving the date.

  • 5

    financial institutions

    Simulated in parallel through a single integration layer.

  • 1M

    contacts unified

    Base cleansed before any campaign.

  • 2,547

    WhatsApp conversations

    Handled with the AI agent inside the CRM.

  • BRL 652K

    in credit offered

    162 loan proposals entered and tracked end to end.

Sales Cloud · Service Cloud · Agentforce · Data Cloud · Marketing Cloud · Experience Cloud · MuleSoft

When we’re not the right choice

Three situations where we recommend something else.

  • A known, stable backlog

    If the work is already specified and only volume remains, hourly allocation is cheaper. Seniors are expensive for repetitive tasks.

  • You need to scale by headcount

    The model doesn’t scale by number of people: it’s four seniors instead of ten mid-level ones. That concentrates key-person risk, mitigated with pairing and rotation.

  • Deliberately open scope

    A deliverable-based contract requires defining what “done” means. If the work is exploratory to that degree, an hourly contract is the more honest arrangement.

Key point

A vendor who never says where their own model doesn’t fit is selling, not advising. We’d rather lose a poorly matched contract than poorly deliver one that should never have been ours.

Next step

Which question do you want answered first?

A 90-minute session: we pick a real problem in your environment and investigate it live on the platform. You leave with the diagnosis and the evidence, whether you hire us or not.

Pick one pain. That’s where we start, not with all of them at once.

What hurts most today?

3 minutes to fill in. A reply within 1 business day, from an engineer, not an SDR.

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