Production Intelligence Ops
We design and build data platforms and pipelines, ML and AI systems, and analytics foundations — then we own the ops (DataOps, MLOps, LLMOps, AIOps) so they stay cheap, reliable and accountable. On the stack you already have, or on a lean platform we stand up.
Free 30 min — we branch into data, ML, LLM or AI work in the first ten minutes Explore DataKru ↗
What we build and run
Not a vague “we do AI” blob. We design and deliver the full stack — data platforms and pipelines, ML and GenAI systems, analytics foundations — then we own the ops so it does not rot. Each lane has its own definition of done.
Design and build pipelines and the full data stack — ingestion, quality, transforms, orchestration, lakehouse and medallion patterns, batch and streaming. Then keep it honest: freshness SLAs, run-level lineage and a cost line you can defend.
Build and productionise ML — feature pipelines, training loops, registry and serving — as one reproducible loop rather than a notebook somebody remembers how to run. Then drift detection and rollback when production changes.
Design and ship RAG, agents and GenAI workflows on governed data. Then the ops layer: eval sets, prompt and index versioning, cost per answer, human gates — and right-sizing or removing AI that burns money without earning its keep.
Design the observability stack — metrics, alerts, runbooks — then run it: alert noise into diagnosis into remediation, with a human gate on anything that changes production.
Architecture for the entire data and AI estate — on your stack or a lean DataKru base. Migrations, governance, first production cut and handover with runbooks — not slides-only architecture.
Build the integration that wires models and agents into ERP, CRM, ticketing and ops tooling — with audit trails — then operate the rollout so it stays owned.
Also in scope: analytics engineering and BI foundations — semantic models, metrics layers, and dashboards your ops can trust (Power BI, Tableau, Metabase-ready marts).
Two ways to work with us
Most agencies sell you their stack. We sell the result. Choose the path that matches your team size, budget and compliance posture — the engineering discipline is identical.
Stand up a lean Data & AI base on DataKru (live beta), with services filling the gaps.
For teams with no platform engineers and no appetite for a lakehouse bill. DataKru today is a free beta: upload → pipelines → on-demand jobs. Scheduled cron, observability and governance are not product-live yet — we install and operate those as part of the engagement when you need them, then hand over with runbooks.
Best fit: lone data person, small team, seed-stage or SME operations.
Explore DataKru ↗Databricks, Snowflake, BigQuery, Kafka, dbt, Airflow — we work inside it.
For organisations with an existing estate and a platform team. No migration is required and no DataKru licence is involved. We bring playbooks, reliability engineering, lineage and governance to what you already own.
Best fit: corporate programs, funded scale-ups, existing platform teams.
Book a teardownDataKru is our product base layer (live beta) for teams that need pipelines without a lakehouse bill — services cover scheduling, observability and governance until the product ships them. Corporate programs can keep Databricks or Snowflake and still hire us.
Second question: where does it run?
Independent of the choice above. Both paths run in either place, and moving between them later is a migration we plan for rather than a rebuild.
Your AWS, GCP, Azure or on-prem estate. Data and credentials never leave your boundary, access is least-privilege under your controls, and you get the deployment documented with runbooks so your team can operate it without us.
Best fit: existing cloud account, security review, data residency rules.
We host and operate the platform for you under a monthly retainer — provisioning, patching, backups, monitoring and the pager. Fastest way to start when there is no cloud team and nobody to own the infrastructure.
Best fit: no platform or infra team, small estate, speed over control.
Open formats and standard components, no proprietary runtime. Start hosted with us and migrate in-house when you hire a platform team, or hand it back to us later. Exit is a documented step in the handover, not a negotiation.
Best fit: teams that expect their infrastructure position to change.
Straight about the limits: we are an engineering team that operates infrastructure, not a certified hosting provider. If your procurement needs certified infrastructure or a signed data-processing arrangement we cannot yet meet, we deploy into your account instead and say so early rather than at contract stage.
How to start
Nobody signs a delivery contract with a stranger. The ladder below is designed so you can stop after any rung.
We map what runs today, where it breaks and the two or three changes with the highest payoff. Within ten minutes we know whether this is a data, model, LLM or platform problem. You keep the notes either way.
One to two weeks, remote and fixed price. Sources, models, prompts and consumers mapped, gap analysis against a governed target, then a written report and a 90-day backlog with a live readout.
Hands-on delivery of the backlog — including greenfield build of pipelines, stack, ML/GenAI or analytics when that is the work — or hardening an existing estate. Fixed milestones, visible increments, documented handover. Always a separate decision from the sprint.
Optional. We keep pipelines, models and LLM workloads healthy after handover — monitoring, incident support and a monthly review. Covers systems hosted with us or running in your own account. Offered only where we built or reviewed the system.
Services
Named, bounded pieces of work inside the lanes above. Most engagements start with one and add the next only when it earns its place.
Greenfield or restructure: design and build pipelines, the data stack, ML/GenAI workloads and analytics marts — on DataKru or inside your existing estate — not only hardening what already runs.
Stop the 2 a.m. pages. Freshness SLAs, alerting with owners, retries, backfills and incident runbooks.
Run-level lineage, ownership, access control and an audit trail that answers where a number came from.
Move off scripts, spreadsheets or an unloved warehouse — in reviewed increments, with a rollback path.
Build feature and serving pipelines, tracked training, registry and retrieval or agent workflows on governed data.
A monthly retainer where we keep the platform healthy after handover — hosted on our infrastructure or running in your own cloud. Offered only where we built or reviewed it.
What we measure · where we focus
The left column is what we move; the right is where we can speak from experience. Figures shown anywhere on this site are illustrative until a client agrees to be named.
Outcome themes
Focus areas
Reference architecture
The upper layers are the same in both adoption paths. Only the foundation changes — DataKru for the lean path, your existing lakehouse or warehouse for the corporate path.
Pick one base layer. Everything above it is portable between them.
One lifecycle, whichever stack you keep.
Our products
We build our own products, and we are direct about the point where yours is the better choice.
Free beta: upload → pipeline → on-demand jobs. Scheduled cron, observability and governance are not live in the product yet. Preferred base layer when a client has no platform team and no lakehouse budget — we fill those gaps in the SOW when needed.
Visit datakru.com ↗A context graph for high-intent leads, auto and mobility first. Currently in early conversations and design — not a shipping product, and not part of any SOW today.
“Most AI projects fail on pipelines and ownership, not on models.”
Founder-led delivery
DataBeansAI is founder-led. You talk to the engineer who designs the architecture and stays accountable for it — platforms and pipelines at production scale, a master’s foundation in machine learning and AI, GenAI and RAG systems delivered and implemented for production use cases, and analytics foundations teams can actually run.
Founder experience before DataBeansAI
Outcomes from prior roles — not DataBeansAI client engagements. We can walk through the work under NDA.
What you will not find here: client logos we cannot name, compliance badges we have not earned, or metrics from engagements we cannot evidence. Ask in the teardown and we will walk you through real work under whatever NDA applies.
Straight answers
If the answer below is not the one you wanted, it is still the true one. Ask the rest in the teardown.
Both. We design and build data platforms and pipelines, ML and GenAI systems, and analytics foundations — then we own the ops so they stay cheap, reliable and accountable. Most engagements start with build or restructure; the teardown maps which you need first.
No, not by default. If you run Databricks, Snowflake, BigQuery or Kafka we work inside it. We propose a base-layer change only when the current one is the actual cause of the problem, and we will show you the arithmetic before you decide.
When you have no platform engineers, data volumes in the gigabytes rather than petabytes, and you want a lean base without a lakehouse contract. DataKru itself is in free beta (pipelines and on-demand jobs today); scheduling, observability and governance arrive via the product roadmap or via our services engagement. Above that scale, or where a platform team already exists, keeping your stack is the better call.
Week 0 is the free teardown. Weeks 1–2 are the architecture sprint: we map sources, transforms and consumers, run a gap analysis and deliver a written report plus a 90-day backlog with a live readout. Weeks 3–4 are either your team executing that backlog, or a scoped implementation SOW if you want us hands-on.
The teardown is free. The architecture sprint is $2,000 fixed and is the only price on this page for a reason — it is the one thing we can scope without knowing you. Implementation is a separate SOW priced from the sprint backlog. Retainers are monthly.
We design and ship the GenAI system first when it does not exist — then the ops layer: an evaluation set before a prompt or index change ships, versioning on prompts and retrieval config, cost per answer as a first-class metric, and a human gate on anything the model should not decide alone. We will also say when something should not be AI and help you remove it.
No. The order matters: design the observability and cut alert noise first, then automate diagnosis against runbooks, then automate remediation — and remediation stays behind a human gate until the diagnosis has earned trust. Skipping the first step gives you a confident model reasoning about noise.
Either, and it is a separate decision from whose stack you keep. We can deploy into your own AWS, GCP, Azure or on-prem environment, where data and credentials never leave your boundary and your team holds the keys. Or we host and operate it on our infrastructure under a retainer, which is usually the right call when there is no cloud or platform team. Because we build on open formats and standard components, starting hosted and moving in-house later is a planned migration rather than a rebuild — and we document the exit as part of handover.
You remain the data owner; we are the operator. We will sign an agreement covering access, retention and deletion before anything of yours lands on our infrastructure. Being straight about the limit: we are an engineering team that runs infrastructure, not a certified hosting provider. If your procurement requires certified infrastructure or a data-processing arrangement we cannot meet, we deploy into your account instead — and we would rather tell you that in the teardown than at contract stage.
Not for the teardown or most of the sprint. Schemas, DAG definitions, sample volumes and a conversation with whoever gets paged are usually enough. Implementation access is scoped and least-privilege under your controls.
Atlas is a planned context-graph product for finding high-intent buyers, starting with auto and mobility. It is roadmap and early conversations only. We will not sell it into a delivery contract until it ships.
Book the first conversation
Tell us what you need to build or fix, and what you already run (or that this is greenfield). You get a reply from the engineer who would do the work, usually within one business day.
Pick a time on Cal.com ↗Fastest route for a teardown. Prefer email, or scoping a sprint or SOW? Use the form.