VahdetLabs · milestone delivery for ops & product-led teams

Scoped reporting, automation, and ML your team can run after sign-off.

For Europe-facing small and mid-sized teams stuck in messy inputs, drifting KPI definitions, or one-off spreadsheets: fixed milestones, documented logic, and handover—not a permanent vendor seat or open-ended augmentation.

Start a scoped brief See all work

KPI and reporting surfaces with definitions your team can defend
Repeatable pipelines and schedules—runs the same rules every time
Batch ML or HTTP inference only when milestones and acceptance are written

Primary fit

Ops, revops/data, or product-facing leads in small and mid-sized teams—Europe-friendly, remote-first—who want a bounded milestone, not unlimited capacity.

Fixed-scope milestones

Engagements centre on named outputs—what ships, what “done” means, and where the boundary stops—not open-ended hours.

Written acceptance

Metric logic and delivery expectations stay in writing before build expands—so sponsors in ops, data, or product share one reference.

Operator handover

You get artefacts and notes your internal owner can run: less dependency on a standing vendor seat after sign-off.

Curated evidence

Selected proof of work

Three snapshots of the usual range teams hire for: trustworthy inputs, KPI surfaces people can defend, and batch scoring behind clear rules—not a catalogue of every niche. Browse Work for the full evidence set.

Data demo

Data cleaning toolkit

Streamlit
pandas
pytest

Upstream prerequisite, not a side utility: downstream models and dashboards ingest the same reviewed tables—multi-format inputs become auditable CSV/Parquet/JSON plus HTML step log and before/after views, capped near 100K rows; rules cover bad formats, duplicates, skewed categories, optional outliers, plus bundled samples for dry runs.

GitHubLive demo

Data demo

KPI dashboard implementation example

Streamlit
Sales & marketing CSV
Mapping presets

An interactive implementation reference—not a SaaS subscription: preset mappings and hard row gates produce KPI cards, trends, breakdowns, optional deltas, and a rule-first “what changed?” so stakeholders see how a scoped Sheets or BI dashboard engagement would behave.

GitHubLive demo

ML system

Batch scoring pipeline

Python
Batch
pandas

Batch job scores CSV rows with training-aligned preprocessing and writes score, label, model version, and timestamp on each row.

GitHubLive demo

Typical entry scopes

Where small teams usually start first.

Data preparation, KPI reporting, and spreadsheet-side automation—the three approvals we see most often from ops-led and product-led SMEs before anything heavier.

Each card is one outcome boundary and one operator handoff—not “platform”. Deeper ML and APIs follow once scope is sober on paper.
Engagements expect a named internal sponsor who can judge success—not a revolving door of stakeholders mid-build.

Primary storefront

Data Prep Sprint

Best for: Teams with conflicting exports, duplicates, drifting formats, or low trust in the numbers—before dashboards, forecasts, or models.

Cleaning, profiling, and clear documentation so the next dashboard, forecast, or model rests on inputs your team can explain and reproduce.

Typical project range: €120 – €550

Clean datasetsprofiling / change summarytransferable notes

See scope

Featured

Primary storefront

KPI Dashboard Package

Best for: Leads who live in spreadsheets or Power BI / Looker Studio and need one agreed KPI view—not a multi-year platform programme.

One workbook or BI report with KPI definitions in plain language, a repeatable refresh path, and checks so reviews focus on decisions—not arguing about the numerator.

Typical project range: €300 – €1,050

Dashboard artefact (Sheets / Excel / agreed BI export)KPI dictionaryQA checklist

See scope

Primary storefront

Reporting / Spreadsheet Automation

Best for: Teams that rebuild the same weekly or monthly report by hand and need the same recipe every run unless you change the rules in writing.

Scheduled or on-demand pipelines—Sheets, Apps Script, or lightweight Python—so each run produces the same outputs and logs reviewers can audit.

Typical project range: €200 – €850

Automation scripts/schedulesrunbookrollback notes

See scope

Review all service scopes

Ranges are indicative; exact quotes depend on data shape, source access, and agreed acceptance criteria.

Engagement model

How a scoped engagement actually runs.

A typical path for a sponsored milestone: freeze scope early, expose data limits plainly, ship something repeatable your owner can inherit.

1. Define scope and outputs

You and I lock what to solve, what goes in, what must come out, and whether the core is an API, batch work, or a smaller reporting slice. Scope stays clear in writing before anything is built.

2. Review data and limits

I check data shape, quality, and hard limits—what is reliable, what is missing, and what is out of scope. You get a straight answer on what the data can support.

3. Build the system

I build reproducible pipelines or services with clear steps. APIs and batch jobs come first when they fit; dashboards or extra reporting only when they belong in that system.

How delivery is built

Stack that supports handover—not the thing we sell.

Layers we usually touch: pipelines in Python and SQL, APIs when systems must integrate, repeatable batch runs—documented so your team can inherit them. Licenses and "our platform" are not part of the offer.

PythonSQLAPIsFastAPIBatch processingDocker

Next step

Ready to scope the first milestone?

If there is already a sponsor, a plausible first output, and a way to judge "done," the brief converts that into written acceptance and milestone one. Fits best with European SMB teams who want handover—not a standing bench.

Start a scoped brief Review services