Real-Time Intelligence
Operational events become live KPIs and alerts, with a path from each event to an action.
Explore Real-Time IntelligenceEvery decision needs an owner.
Give it the right intelligence.
Live decisions, trusted analytics and production AI, each built around a decision someone owns.
Live where it moves
Operational events become live KPIs and alerts.
Numbers people trust
Governed metrics your team can inspect.
Agents that pass a gate
Each agent passes its evaluation tests before users meet it.
Pilot in four to six weeks, typically · Fixed price · On your own data
A platform pays
when intelligence runs on it.
The platform is live and the data is landing, yet the business still runs on the same spreadsheet. The infrastructure changed; the decision did not.
Start from the decision
A dispatch exception needs an event stream, not a month-end report.
Fit the mechanism to the work
A recurring HR question needs a governed metric before it needs an assistant.
Accountable in production
An agent that can change a record gets a scoped identity and a person to escalate to.
This is the right engagement when
- The platform is live, and the business still decides from spreadsheets.
- A decision has to happen while the work is still in motion, not at month end.
- An agent demo worked, and now it has to act inside real systems, safely.
- Teams argue about whose number is right.
From a live signal
to an accountable action.
Data Integration
Incoming data is checked at the boundary and lands as reusable feeds in a governed lakehouse.
Explore Data IntegrationAnalytics & BI
Governed semantic models on Power BI or Looker, with metric definitions your team can inspect.
Explore Analytics & BIAgentic AI
Bounded agents that pass an evaluation gate before release, with scoped tool permissions and an agreed operating plan.
Explore Agentic AIComputer Vision & ML Engineering
An inspection or prediction task, taken from labeled data to a measured model with an operating review loop.
Explore Computer Vision & ML EngineeringTrust Layer
Identity, policy, safety and traceability sit in the request path, so every action leaves an auditable trail.
Explore Trust Layer
Agree the bar.
Prove the workload can clear it.
Four stages take one operating decision to a measured workload in production.
Name the workflow, its owner and the result it must improve. Assess shortlists the use cases worth building.
You receive
Use-case verdict
The workflow, its owner and the result it must improve, written down before the build.
The Mandelbulb Kit Data & AI editionBuilt and maintained in-house
Engineered in-house.
The Mandelbulb Kit.
Accelerators, agents and products we build and maintain ourselves, brought into an engagement wherever they fit.
Agent runtime · Runs the agents
Blumcore
The runtime every Kit agent runs on, built to act inside the systems you already run.
- Reads enterprise data through MCP-native connectors
- Acts inside systems of record through tool calls, not screen scraping
- Runs in the cloud, on-premise or air-gapped, every action audited
Plugs into Design the production path
Explore more about BlumcoreAlso in this edition
- PulseFeeds the PilotTakes your operational events to live KPIs, anomaly alerts and automated action, starting with two weeks on one stream family., explore Pulse
- AI Tax OfficerTax administrationReconciles returns, e-way bills and e-invoices on one data fabric, scores the risk and drafts notices an officer reviews and signs., explore AI Tax Officer
- TheTranquilVoice agentsVoice agents that detect tone, handle interruptions, keep context across turns and escalate cleanly to a supervisor., explore TheTranquil
- Atlas AIPortfolio inboxTurns a project inbox into an operating picture: every message routed to its project, today’s priorities surfaced, commitments tracked., explore Atlas AI
- AEC PlatformConstruction agentsRFIs, schedules, proposals and CVs, drafted from the project record., explore AEC Platform
- SAPBridge · BOBJ TransformerSAP data bridgeSAP data on Microsoft Fabric or Databricks, reconciled to the source, with a planned exit from BusinessObjects built in., explore SAPBridge · BOBJ Transformer
What this is not.
This practice starts when the use case is funded and the estate can carry it.
Need to choose and fund the roadmap first?
That is Data & AI StrategyDoes the data estate need to move first?
That is Modernization
The story this practice stands on.
Government of Andhra Pradesh
Government · Tax administration
- Manual compliance paperwork cut through automated notice drafting
- 80%
- Dossier preparation time, through automated notice drafting
- Days → minutes
- Net GST growth, department-reported
- 21%
AI-Drafted GST Enforcement in Andhra Pradesh, Signed by an Officer
AI Tax Officer, which we built for Andhra Pradesh’s Commercial Taxes Department, is intelligence running on a governed platform. Microsoft Fabric reconciles GST returns, e-way bills and e-invoices, Azure Machine Learning scores anomalies, and Azure OpenAI drafts statutory notices that an officer reviews, decides and signs. Automated drafting cut manual compliance paperwork 80% and dossier preparation from days to minutes. The department reports net GST up 21% and credits AI-based scrutiny as one driver among several.
Every notice carries a version-controlled trail under Microsoft Entra ID, and officer corrections feed a learning loop, so the intelligence stays accountable to the person who signs.
AI drafts, but officers decide. Every notice and order carries the digital signature of the responsible officer and is fully traceable through the back office case ID and document identification number.
Partners and recognition.
Questions buyers ask.
What does a Data & AI engineering practice build?
We build production workloads around a decision someone owns: real-time intelligence, data integration, analytics and BI, agentic AI, computer vision and ML engineering, and the trust layer that keeps AI accountable. Each starts from the workflow, its owner and the result it must improve; then the data, the controls and the decision point are connected. Agents pass an evaluation gate before users meet them.
How long does a Data & AI pilot take, and what does it cost?
A Pilot typically takes four to six weeks and is a fixed-price, fixed-scope production slice on your own data, judged against a success metric agreed before it starts. It follows Assess, typically two weeks, and the Blueprint phase, typically two to three weeks. After the Pilot, the work expands from a priced backlog on a quarterly rhythm, with a weekly written recap.
Do you build Data & AI workloads on Fabric or Databricks?
We are platform-independent: the work starts from the operating decision, and the mechanism follows the platform your estate already runs. A live event path, a governed metric and a bounded agent each need different parts of the platform, and those are designed in the Blueprint phase. If the platform itself is still undecided, Strategy & Advisory settles it on your workloads before a Pilot is scoped.
When should we use an AI agent instead of a dashboard or alert?
Use an agent where judgment in a bounded task can be delegated and tested; use a dashboard or live alert where a person still makes the call. A dispatch exception needs an event stream, not a month-end report. A recurring HR question needs a governed metric before it needs an assistant. An agent that can change a record needs a scoped identity, an evaluation suite and a human escalation path.
What do we need before a Data & AI pilot can start?
You need a funded use case and an estate that can carry it: a named workflow, its owner and the result it must improve, with the data it depends on in reach. If the roadmap is not funded yet, start with Data & AI Strategy; if the data estate has to move first, start with Modernization. Data preparation and controls are included in the agreed Pilot scope.
What comes next.
When the estate has been running long enough to drift, the next step is Tune. The workload measurements and operating cost baseline cross with it. The question changes from whether the workload works to what it costs to keep it working.
This practice
Run
Data & AI
When the estate has been running long enough to drift
Next practice
Next practice: Tune, Data Estate OptimizationBring one operating decision.
Give it a production test.
Book a 30-minute working session on one workflow, its data and the metric it must move.