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Solution Blueprint
AI Superfactory Operations

High-Tech

Unolabs engineers AI superfactory infrastructure for high-tech platforms. We build the high-density data foundations to manage high-frequency telemetry, stateful reasoning loops, and massive model operations at global scale.

Operational Context

For hyper-scale software platforms, AI infrastructure providers, and technology operations groups.

Strategic Mandate

Engineering specialised data zones and autonomous processing layers for High-Tech leaders.

Inside High-Tech

Your telemetry bill grows faster than your ARR

Every platform team knows the moment: usage-based billing goes live and finance discovers the metering pipeline drops events under load — and every dropped event is either lost revenue or a customer credit. Meanwhile telemetry unit economics quietly invert, with per-event ingest and storage costs outpacing the margin the features earn, and multi-tenant isolation is enforced in application code instead of the data layer. We engineer metering-grade pipelines where the invoice and the event stream reconcile.

Industry Challenges

Architectural Bottlenecks Hindering High-Tech Transformation

Product Telemetry Fragmentation: High-frequency telemetry, customer health, and billing meters are disconnected, so usage-based invoices cannot be reconciled against raw events.

Operational Complexity: Teams cannot trace the relationship between product incidents, net revenue retention, and architectural technical debt in a single view.

Model Orchestration Gaps: AI features lack the high-fidelity feature pipelines, governed semantic definitions, and hard multi-tenant isolation required for global-scale model operations.

Anonymised Use Cases

Relevant patterns from industry work

Each pattern represents a validated technical outcome delivered for global enterprise clients.

Use Case 01

Synthetic Data Factory

Built an automated synthetic data generation pipeline for a high-tech hardware leader, providing high-fidelity training sets for AI models without compromising proprietary IP.

Use Case 02

Autonomous Agent Orchestration

Built a multi-agent framework for a high-tech platform, automating complex data remediation and task splitting through stateful reasoning loops.

Use Case 03

AI Superfactory Infrastructure

Designed the high-density datplatforms for AI compute estates, where telemetry volume and model checkpointing are costed at architecture time rather than discovered in the bill.

Read Technical Proof
Hyper-Scale Computing
AI Superfactory Operations
Explore the Demand Blueprint
Industry Flow

High-Tech Product Intelligence Flow

Input

Telemetry, CRM, support, billing, marketing

Usage events, tickets, subscriptions, campaigns, account records, and product data enter from product and go-to-market systems.

Events + APIs
Treat

Customer and product data products

Data is unified around account, user, product, feature, contract, issue, and journey entities.

Lakehouse + graph
Model

Health, growth, AI features

Features power churn prediction, recommendations, product analytics, support intelligence, and RAG context.

ML + vector
Activate

Product, CS, sales, support, AI

Teams receive dashboards, alerts, agents, APIs, and product intelligence workflows.

BI + agents
Featured Insights

Expert insights for High-Tech

Enterprise migration and production operating model stories.

Outcomes

Industry Transformation Outcomes

Metering-Grade Telemetry

Predictive Growth Intelligence

Governed AI Superfactory Foundation

Connect the Dots

Engineering High-Tech Ecosystems

How our 2026 service catalogue integrates to solve high-growth industry challenges.

Step 01

Real-Time Streaming & Event Architecture

Billable event integrity

Engineering

Engineer the metering path for ordered replay, deduplication and late-arrival rules, so an invoice line can be traced back to its events.

“A customer disputes an invoice and finance cannot tie the lines back to the event stream. Each gap is either revenue nobody billed or a credit already owed.”

Apache KafkaApache FlinkMetronomeStripe Billing
What lands

Revenue assurance trace

One billing period reconciled end to end: every invoice line traced to the events behind it, with drops, duplicates and late arrivals counted and each given a written handling rule.

Exactly-once stops at the meter. A retried call and a genuine second call look identical downstream unless the emitting service stamps idempotency at source.

Step 02

Cloud Data Infrastructure & Landing Zones

Tenant isolation boundary

Engineering

Push the tenant boundary into storage, identity and query paths, so the control a security review tests is not a line of application code.

“A large customer's security questionnaire asks where tenant isolation is enforced; the honest answer is application code, one omitted predicate from a disclosable breach.”

PostgreSQL RLSSPIFFEOpen Policy AgentSOC 2 Type II
What lands

Isolation evidence pack

Where the boundary holds at each layer — storage, identity, network, query — the test that proves each one, and the blast radius if it were bypassed: an answer to any customer security review.

Isolation kept in application code fails silently, and is usually found by the customer who reads another customer's row — a disclosure, not a defect.

Step 03

Agentic AI & Autonomous Operations

Agent authority limits

Intelligence

Build agents that assemble the evidence and recommend, leaving price, entitlement and credit calls with a named person and a written record.

“The pilot demonstrates well and then stops: nobody will sign off an agent changing a customer's price or entitlement, and what it may decide alone was never written down.”

TemporalModel Context ProtocolCedar
What lands

Autonomy level matrix

Each workflow placed at answer, recommend, or execute under policy, with the approval required, the tools the agent may call and the record written for every decision — a stated risk position.

Approval is only a control if the approver sees what the agent saw; a recommendation that arrives without its evidence trail is signed off on trust alone.

Step 04

Data Engineering & Integration

Tenant key propagation

Engineering

Carry the tenant key through every hop of the feature and context pipeline, so no product signal crosses an account boundary downstream.

SnowplowApache IcebergFeast
Step 05

Enterprise Data Platform Engineering

Telemetry unit economics

Engineering

Set retention, rollup and downsampling policy before volume sets it, and attribute ingest and storage cost to the feature that earns it.

OpenTelemetryGrafana MimirCribl StreamClickHouse
Step 06

Enterprise Decision Intelligence & Operational Analytics

Metered account health

Intelligence

Model expansion and churn from metered consumption rather than seat counts, so account health reflects what customers actually use.

AmplitudeGainsightApache Pinot
Step 07

DevOps & SRE

Error budgets on meters

Engineering Operations

Give the metering path its own SLO and release gate, so a deploy that doubles event volume fails a check rather than a margin.

OpenSLOArgo RolloutsBackstage
Discovery Cycle

Start Your High-Tech Modernisation

We'll review your current architecture, identify immediate bottlenecks, and draft a production-grade roadmap for your autonomous transformation.

"Bring one invoice a customer disputed. We'll reconcile it against the raw events."