delivery · CAT-30030742 · rev 1.0 |
AI Data Infrastructure Foundation. @ai-data-foundation
5.0Reviews ▾
Rated 5.0 / 5 by clients on GoodFirms.
Read verified reviews on GoodFirms →Vetted by Scrums.com Platform
Provider Scrums.com
Last review 2026-08-14
What you get
the numbers that matter≈ 2 weeks
signed to first PR
96%
engagements renewed
96%
to your stack & domain
Set up the data pipelines, versioning, feature flow, and storage patterns required for repeatable ML and AI work. Finish with an ML-ready foundation running in your cloud.
How this operator works
every way of working, already decidedOwns the system, not the ticket
Takes end-to-end ownership of a service or surface. Design, delivery, on-call. And is measured on outcomes, not hours.
Embedded, async-first, instrumented
Works inside your repos, your CI and your rituals. Daily written standups, decisions logged. No status-meeting tax.
Runbooks, canaries, reversible deploys
Every change gated and reversible. Incidents get a timeline and a postmortem; nothing ships without a rollback.
Plugged into your Slack & rituals
Joins standups and retros, reports weekly against the goal. You get an operator, not a queue.
Brings a pre-wired stack or adopts yours
Infrastructure and observability as code by default. No bespoke setup tax to absorb.
Scoped, gated, reversible
Week-1 shadow, week-2 ownership, swap on request inside the trial window. No long-tail handover risk.
Overview
Most AI initiatives stall on data, not models. This package sets up the pipelines, versioning, feature flow, and storage patterns that make ML and AI work repeatable. The finish state is an ML-ready data foundation running in your cloud: governed ingestion, versioned datasets, and a documented path from raw data to training and inference.
The build uses proven tooling fitted to your stack: orchestration with Airflow or Prefect, transformations in dbt, and lakehouse or warehouse storage on Databricks, Snowflake, or your cloud's native services. Where the goal is GenAI, the same foundation covers vector storage and retrieval patterns so RAG workloads stay fresh instead of going stale.
What's included
Governed ingestion pipelines
Scheduled, monitored pipelines that move data from your source systems into versioned, query-ready storage.
Dataset and model versioning
Version control for datasets, features, and model artefacts, so every experiment and training run is reproducible.
Feature flow design
A documented path from raw data to features, training sets, and inference inputs, with clear ownership at each step.
Storage and access patterns
Lakehouse or warehouse layout, access controls, and cost-aware retention rules matched to your workloads.
How it works
- Scope: map source systems, target AI use cases, and the current data estate. Agree the foundation architecture and acceptance criteria.
- Build: stand up pipelines, versioning, and storage in your cloud accounts, with tests and monitoring on every flow.
- Handover: walk your team through the architecture, hand over runbooks and documentation, and agree the first ML workload to run on it.
Part of every Delivery Plan
The AI Data Infrastructure Foundation is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.
FAQs
Does this package include building an ML model?
No. It delivers the data foundation that models depend on. The Forecasting & Predictive Analytics Pilot or GenAI Pilot Build menu items are the natural next step once the foundation is live.
What do we need before the sprint starts?
Access to your cloud accounts and source systems, plus a named owner for data decisions. No existing pipeline tooling is required; the sprint can start from a blank estate.
Who operates the foundation after handover?
Your team owns it. Everything is codified and documented, so it runs without us. If you want ongoing operation of pipelines and models, the Model Ops Retainer covers that as a managed service.
What's included
in every engagement · no add-onsTrack record
deployments on real systems · anonymized| Sector | System | Outcome | Span | Status |
|---|---|---|---|---|
| Fintech | payments-core ledger | 99.97% achieved | 14 mo | ● complete |
| Commerce | checkout platform | −38% incident rate | 9 mo | ● complete |
| Health SaaS | data plane | 0 SEV1 in 6 mo | 11 mo | ● active |
| Logistics | routing engine | zero-downtime cutover | 7 mo | ● complete |
| AI infra | inference cluster | p99 −120 ms | 5 mo | ● active |
Works inside your stack
surfaces this operator binds to| Surface | Binding | Direction | Auth |
|---|---|---|---|
| Source control | github.com/<org> | reviews + writes | OIDC |
| CI / CD | scm-flow · deploy-service | gates deploys | OIDC |
| Observability | otlp://collector:4317 | metrics + alerts | mTLS |
| Comms | slack://<workspace> | standups, incidents | SSO |
| Secrets | vault://scrums/op/<id> | short-lived creds | SPIFFE |
| On-call | pagerduty://<org> | primary / secondary | API token |
Boundaries
what to deploy insteadScoped to this discipline. For an adjacent capability, compose a second operator into the squad. compose →
Not a fractional advisory engagement. For advisory-only, contact platform@scrums.com.
Deployments
the only social proof we publish402deploys
across 38 organizations
+24 last 30 days · median age 11.4 mo · retention 96%
Pricing
one number · one footnoteAvailable at all Delivery Plan Tiers →
All-in: the operator, delivery manager and replacement guarantee. No recruiter fee, no markup surprises.
Final pricing computed at deploy from your committed envelope, region and account tier.
FAQ
common questionsHow is AI Data Infrastructure Foundation priced?
Pricing is shown to signed-in accounts. Sign in to view the rate; pricing is computed from your engagement scope, region and account tier.
Is AI Data Infrastructure Foundation available now?
Yes. It is published and deployable directly from the Scrums.com catalog.
Can a AI Data Infrastructure Foundation deployment be reversed?
Yes. Deployments are reversible with a one-click swap inside the trial window.
Who provides AI Data Infrastructure Foundation?
Scrums.com, vetted by the Scrums.com platform.
How it compares
vs other delivery| Option | From | Stack | Status |
|---|---|---|---|
| AI Data Infrastructure Foundation · this one | 🔒 Sign in for pricing | delivery · outcome-driven-sprints · ai | ● available |
| Codebase Quality Audit | 🔒 Sign in for pricing | delivery · code-audit · maintenance | ● available |
| Product Discovery Sprint | 🔒 Sign in for pricing | delivery · discovery · workshop | ● available |
| Clickable Product Prototype | 🔒 Sign in for pricing | delivery · prototyping · design | ● available |
Commonly deployed with
more deliveryRun this scope on your Delivery Plan
Core costs Was $8,000£6,700R140,000 now $4,500£3,750R79,000/month for one active workstream. Your scope sets the delivery schedule and the total cost. You can use the same plan for your other development work.
Add this scope to your backlog. Your delivery owner confirms the scope with you, and the team delivers it.
One plan for your software backlog.
Work is scoped and delivered within your plan’s capacity. Priorities can change; additional parallel workstreams require more capacity.