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Customers · Year One

Teams that stopped guessing
and started forecasting.

From a circular-fashion scale-up closing the largest deal in company history, to a leading Data & AI boutique building a commercial engine to match its book, to a Paris computer-vision deeptech where we built the sales machine from scratch, two SDRs, one sales rep, three playbooks, these are the revenue teams that run on ValueOrbit. Customer-approved. Numbers verified by their finance teams.

Become a reference customer → See the aggregate numbers
The roster

Revenue teams we've rebuilt from the forecast call outward.

Reference customers

What the book of business looks like

Three reference customers.
One honest set of numbers.

These are averages across the active book of ValueOrbit customers as of Q1 2026, measured against each customer's own pre-engagement baseline. No cherry-picking, no hero metrics from one account. Ask us for the underlying methodology and we'll send it.

93%
Forecast accuracy
after 6 months
+15%
Win rate
on qualified pipeline
-34%
Sales cycle
length (days)
-50%
Admin time
per rep per week
Featured engagements

Three stories. Three kinds of problem.
Same method.

These three engagements represent the buyer archetypes ValueOrbit serves best: the seed-to-Series-A scale-up building a repeatable enterprise motion, the established services boutique whose commercial discipline needs to match the ambition of its book, and the deeptech turning long enterprise POC cycles into a forecastable pipeline.

Circular fashion · Seed → Series A · 2026

From founder-led deals to a repeatable enterprise engine.

A circular-fashion platform with strong PMF and global brand partnerships, readying for Series A. We installed the qualification methodology, trained the commercial team, and embedded methodology into the daily CRM workflow, and landed the largest deal in company history along the way.

1st
Largest deal in company history
Series A
Forecast-ready pipeline
100%
Team on shared method
 Data & AI services · Luxury & retail · 2026

Scaling a Data & AI boutique through sales excellence.

A leading Data & AI partner for luxury and retail, building a commercial engine to match the ambition of its book. We deployed the AI Revenue Execution Platform on top of Salesforce and installed the pipeline, forecast and deal-review discipline to scale predictably.

Salesforce
Native, no migration
Data & AI
Luxury & retail book
Scale-ready
Forecast discipline
 Computer-vision deeptech · Retail intelligence · 2026

A sales machine built from scratch, and two large deals won.

A Paris-based computer-vision deeptech selling AI store-analytics into enterprise retail and luxury. We built the commercial engine from the ground up: hired and onboarded two SDRs and a sales rep, defined the lead-gen, deal-management (SPICED-X) and forecasting playbooks, coached the team into a weekly cadence, and the new team closed two large enterprise deals inside the first cycles.

2 deals
Large enterprise wins
2 SDRs
+ 1 sales rep
3
Playbooks installed
Case study 01 · SaveYourWardrobe

"We closed the largest deal in our history,
and built the engine to keep doing it."

"Together, we closed the largest deal in our company's history, and built a repeatable commercial engine to keep doing it."

Mehdi Doghri · Chief Operating Officer, SaveYourWardrobe

Situation

Strong PMF, Series A path, ad-hoc commercial motion.

A circular-fashion platform partnering with leading global brands, progressing from seed to Series A. No shared qualification language, limited pipeline visibility, no structured deal-review rhythm. The commercial engine needed to match the ambition of the mission.

What we installed

Methodology, CRM-native workflow, weekly coaching.

Combined advisory + platform engagement. Full GTM diagnostic, tailored methodology training for the commercial team, platform deployed on top of the existing CRM, and ongoing coaching with structured deal reviews embedded into the daily workflow.

What changed

Largest enterprise deal ever. Series A-ready forecast.

Landmark enterprise contract closed, the largest in the company's history. Shared sales language across the team. Consistent methodology adoption on all active opportunities. Pipeline predictability in place ahead of the Series A fundraise.

Case study 02 · OliveSoft

"A commercial engine to match a
leading Data & AI partner."

"As one of the leading Data & AI partners for luxury and retail, we needed a commercial engine to match. The ValueOrbit Revenue Intelligence Platform on top of Salesforce gave us the pipeline, forecast and deal-review discipline to scale predictably."

Marouan Fakhfakh · Chief Revenue Officer, OliveSoft

Situation

Leading Data & AI boutique, ambitious book, ad-hoc commercial motion.

A leading Data & AI services partner for luxury and retail, with strong brand-name engagements and a pipeline of high-value opportunities. What the book needed next: a repeatable commercial discipline to match the ambition, pipeline visibility, forecast accuracy, and a deal-review rhythm the leadership team could trust.

What we installed

AI Revenue Execution Platform on Salesforce, qualification discipline.

ValueOrbit deployed natively on top of the existing Salesforce instance, with no migration and no seat rip-and-replace. Qualification embedded in the pipeline. Deal-review cadence installed. Forecast roll-up wired into the leadership rhythm.

What changed

Pipeline, forecast and deal-review discipline, built to scale.

A commercial engine that scales predictably with the book. Pipeline hygiene embedded in the Salesforce workflow. Forecast the CRO can take to the board. Deal reviews the team runs on a weekly cadence without a spreadsheet assist.

Case study 03 · Anavid

A sales machine built from scratch,
and two large deals won.

"ValueOrbit didn't just consult, they built our sales engine. Two SDRs, a sales rep, the lead-gen, deal-management and forecasting playbooks our team now runs on every week, and two large enterprise deals won along the way."

Anavid commercial leadership · Paris

Situation

Strong tech, no commercial team, no playbook.

A Paris-based computer-vision deeptech delivering real-time store analytics to enterprise retail and luxury chains. The product had traction and the founders had won the early deals, but there was no SDR function, no dedicated sales rep, and no shared playbook. Lead generation was ad-hoc, deal management lived in the founders' heads, and forecasting was a guess.

What we installed

A team plus three playbooks, lead gen, deal management, forecasting.

We built the sales machine end-to-end: hired and onboarded two SDRs and one sales rep, then wrote and embedded the three playbooks the team runs on. Lead generation: ICP, targeting, outbound cadence, qualification standard. Deal management: SPICED-X applied to every active opportunity, weekly deal reviews, mutual action plans. Forecasting: commit / best-case / pipeline categorization, weekly forecast call, monthly roll-up to leadership. Then we coached the team week after week until the cadence was self-sustaining.

What changed

Two large deals won. A commercial engine that runs without the founders.

The new team, two SDRs and a sales rep, won two large enterprise deals inside the first cycles of the engagement, with the playbooks running on every opportunity. Anavid's leadership now has a commercial team that prospects, qualifies, manages and forecasts on its own, using a shared language and a weekly rhythm. The founders are out of the day-to-day deal flow. Pipeline coverage is visible. The forecast is something the leadership can take to the board with confidence. The sales machine is the company's, not ours, exactly as it should be.

The next customer story

Your name on this page
in ninety days.

Every customer above started exactly where you are, a revenue team with ambition, a pipeline with gaps, and a leader tired of guessing. The difference is the day they picked up the phone.

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