AI and software for data, analytics and IT teams, across pipelines, definitions, the request backlog, internal support, and getting models into production.
None of these is a modelling problem. They are the reason the data team never gets to the modelling.
In brief
EigenSpark builds AI systems and the software around them for data, analytics and IT teams. We build and modernise the pipelines, and we settle the definitions, so margin means one thing across every report. We monitor quality, lineage and access so a broken feed is caught before the report is. We take models out of notebooks and into production, with versioning, monitoring and an owner. And we automate the internal request and support queues that consume the team’s week. Then your team runs it.
Your systems
Nothing here asks you to migrate your warehouse to work with us.
Use cases
Pipelines and definitions first, then answers, then models in production, then the support queue.
Sources connected, transformations tested and schedules monitored, so a broken feed raises itself before the report does.
Data Engineering & Platforms →The metrics that get argued about defined once, in code, with lineage from the number in the report back to the source rows behind it.
Data Engineering & Platforms →Business questions answered over the defined layer, with the query and the source shown, so an analyst can check the answer before it travels.
Generative AI →Freshness, volume, distribution and referential checks on the feeds that matter, with the owning team told which report is affected.
Data Engineering & Platforms →Who can read what, across warehouses, dashboards and extracts, reconciled against your identity system and reviewed on a schedule.
Continuous Compliance Monitor →A model out of the notebook and into a service with versioning, monitoring, rollback and a named owner, called by the system that needs it.
AI & Data Strategy →Drift, degradation and cost tracked after launch, with a defined trigger for retraining and a record of every version that ran.
AI & Data Strategy →A test set built from your own cases, with the failure modes named, so every change to a model is measured before it ships.
AI & Data Strategy →Access requests, report questions and common IT issues answered from your own runbooks and routed by rule, with the rest passed to your team.
Agentic AI →Undocumented stored procedures, reports and jobs read and described, so a migration starts from what the system does and not from memory.
Full-Stack Development →Interfaces built between your ERP, your warehouse and the applications around them, with failures visible and retries handled.
Systems Integration & APIs →Spend attributed to the teams, queries and pipelines that cause it, with the expensive ones named and a fix proposed for each.
Data Engineering & Platforms →How an engagement runs
Settle the definitions and the pipelines, automate one queue, then hand it over.
Two to three weeks agreeing the definitions that matter, building or repairing the pipelines behind them, and putting quality checks on the feeds. Everything else on this page depends on this.
One queue taken end to end: self-serve answers over the defined layer, the internal support queue, or one model taken to production. Small enough to prove in a quarter.
Definitions, quality rules, access policy, evaluation sets and deployment pipelines live in your own repositories. A new source or a new model does not need us.
Training
This is the function that carries everyone else’s AI questions, so training runs both ways.
Engineers and analysts
AI and machine learning, and alongside it SQL, data engineering, cloud, enterprise systems and cybersecurity, taught on your own platform.
Walk away withWorking code from every session, run against your own platform and kept afterwards.
The business teams you support
Training the functions around you to use the defined layer themselves, which is the only thing that actually shortens your backlog.
Walk away withEach team answering its own routine questions, with the definitions they may not change.
The CIO and heads of data
How to evaluate a model, what to require of a vendor, where the data may go, and what to insist on before a pilot is allowed near production.
Walk away withA checklist a pilot has to pass before it reaches production data.
This is the whole catalogue: Data & Analytics, AI & Machine Learning, Cloud & Infrastructure, Enterprise Systems and Leadership & Executive Readiness. See Training & Enablement.
The engineering half
Four layers, and the interesting one is the smallest. Most of this work is the three underneath it.
A customer, a product and a margin have to mean one thing before any interface over them is safe. This layer is also where governance actually becomes possible.
Your warehouse and your cloud stay where they are. Most of the effort is the sources at the edges and the systems that need the output back.
Built for the person who has to defend the number, with the query, the lineage and the freshness in one view, so an answer can be checked before it is sent on.
Defined once and used everywhere, so finance, sales and operations stop reconciling each other. Your team runs and owns it.
The training we run is the same catalogue your team would take: AI and machine learning, and alongside it SQL and data engineering, cloud, enterprise systems and cybersecurity. It is how the handover holds after we leave.
What we offer data and IT teams
Most teams start with the pipelines and definitions, and use three of the four.
Where a data programme starts.
Where a single workflow starts.
The catalogue your team learns on.
Where the specifics change.
The specifics change by sector. See how this lands in BFSI or Technology & SaaS.
Security and controls
Code lands in your repositories, access follows your identity system, and every model has an owner.
Pipelines, definitions, models and infrastructure live in your repositories, under your review process, so nothing here depends on us being available.
Permissions come from the groups you already manage. Nothing here creates a second, quieter set of credentials, and every grant is logged.
A model in production carries a version, a monitored metric, a named owner on your side and a tested way to turn it off.
Evaluation sets, quality rules and thresholds are held in your repositories, so your team can reproduce any number we report.
FAQs
No. Snowflake, Databricks, BigQuery, Azure or an on-premise estate all work, and a migration chosen for its own sake usually delays everything behind it. Where the platform genuinely is the constraint we will say so and show the cost, but that is a conclusion from your workload and not the opening position.
Often you should build it, and the honest answer depends on your backlog. Most in-house teams we meet know exactly what to build and have no capacity to build it while the tickets keep arriving. We take a defined piece, build it in your repositories to your review standard, and train your team on it as we go, so the capability stays after the engagement.
Because a natural language interface over undefined data is the fastest way to lose trust in the data team. Three teams get three different margins, all delivered confidently, and the next review goes back to spreadsheets. Settling the handful of metrics that actually get argued about takes a fortnight, and everything after it is defensible.
Usually, and it is rarely the model that is the problem. What is missing is the service around it: versioning, monitoring, a rollback, an owner and a call from the system that would use the output. We take one that has business value and put that around it, and then your team repeats the pattern.
With a test set built from your own cases, including the ones that go wrong, and the failure modes named up front. Every change is measured on it before it ships, and the set lives in your repository so your team can run it without us. Where we cannot construct a meaningful test set, that is a finding about the use case.
The data stays in your own cloud tenancy. Where a hosted model is used, what is sent to it is agreed and recorded before anything is built, and for sensitive workloads we build against models that run inside your boundary. That decision is made with your security team at design time.
Yes, and that is usually the better shape. Alongside the AI and machine learning tracks we run SQL, data engineering, cloud, enterprise systems and cybersecurity, because the gap in most teams is the foundation under the AI. Programmes get built around your own systems and your own data.
The pipeline and definition work is worth doing with one analyst, because the definitions are what everyone else argues about. The production and monitoring work needs enough models or enough pipelines for the operational cost to be real, which in practice means a team of three or more.
Related
The services this is built from, the training behind the handover, and where the specifics change.
Pipelines, definitions, quality and lineage.
→ ServiceModels into production, and how they are evaluated.
→ PillarThe catalogue your team learns on.
→ IndustryShipping AI in a product you sell.
→ FunctionAccess, evidence and the control record.
→ PillarThe engineering side, end to end.
→Send the reports they appear in and the queries behind them. We call you within 48 hours and go through why they disagree, what it would take to define them once, and which part of your backlog that clears.
Talk to us