Built for production. Across every data team.

Teams need public web content as a queryable corpus for AI, but crawling, cleaning, and embedding it is a bespoke project every time, and the result usually ends up outside the governance model that covers everything else.

Data Ingester pulls the selected sites from Common Crawl; Transformer refines and normalises the crawled content; the refined corpus is loaded into a vector database; and agents are built on top to answer questions over it. The corpus is governed by the same catalog and RBAC as every other source.

Standing up a data plane per environment or region is a manual, slow, and inconsistent piece of work, and the storage layer that serves AI agents has to be provisioned and wired up by hand each time.

Deployment of the DataByte data plane onto GCP infrastructure is automated end to end and triggered in one action. The served data layer for agents runs on BigQuery, PostgreSQL with pgvector, and Google Cloud Storage, provisioned and connected as part of the same deployment.

Network operations teams need near-real-time and 15-minute aggregated visibility across RAN, Core, and Transport layers, often from Ericsson, Nokia, Huawei, and ZTE in different vendor formats.

Data Ingester normalises multi-vendor counters; Transformer runs matched aggregation windows on Spark; Anomaly Detector flags regressions on the live stream; Forecaster projects capacity; Analytics delivers NOC dashboards.

Faults are detected, but root cause requires manual investigation across EMS systems, logs, and expert notebooks, usually at 3am.

Anomaly Detector surfaces anomalies on live telemetry; Sherlock runs decision-tree RCA correlating alarms, change events, and historical failures; ProcBot triggers and verifies remediation.

Cell parameter edits, tilt changes and power increases are approved in tickets and applied by script. A neighbour-list edit that removes a handover relation drops calls across a corridor, and a capacity-driven power increase can put a site above its filed EIRP with nobody noticing until audit.

Explainability Fabric evaluates every change before it reaches the network. Parameters are bounded, the resulting neighbour list has to retain at least one relation, and no more than a quarter of a cluster changes at once. Radiated power is computed from transmit power, antenna gain and feeder loss, then checked against the licence block, any exclusion zone the site sits inside, and the border coordination limit, with the decision naming which one binds.

Maintenance windows generate alarm noise, so engineers suppress broadly and by wildcard. Suppressions outlive the window that justified them, and a genuine outage can sit unnoticed for hours behind a rule set six weeks earlier.

Explainability Fabric scopes every suppression and takes its end time from the scheduling system, so a closed window cannot leave an alarm muted. Wildcards are refused, broad suppression needs NOC approval, and fire detection, tower structural, RF exposure and emergency call failure can never be silenced at all.

Vendor procedures are manual, error-prone, and depend on specialised knowledge that lives in three engineers who are always on the critical path.

Agents read vendor documentation and draft executable scripts; ProcBot orchestrates execution against the target elements; Sherlock validates post-run outcomes before handing back to the operator.

Billing and rating systems drift out of sync with the network, causing revenue leakage that is invisible until a monthly audit.

Anomaly Detector detects misalignments in near-real-time; Sherlock diagnoses the cause by correlating rating events with billing records; ProcBot opens a case-management workflow for accounting to resolve.

Treasury teams need accurate multi-week cash-flow predictions pulled from a patchwork of ERP, billing, and banking feeds, usually reconciled in a 40-tab spreadsheet by one senior analyst.

Data Ingester pulls AR, AP, and bank feeds on schedule; Transformer normalises currency and timing; Forecaster runs an ensemble of time-series models with confidence intervals; Analytics delivers a governed treasury dashboard.

Finance data lives across ERP, billing, and banking systems in different grains, currencies, and calendars, with no lineage anyone trusts at audit time.

Advance ETL consolidates sources into a single analytical layer; Data Catalog emits source-to-report lineage automatically; PII is auto-classified on arrival.

Leadership needs consistent weekly KPI reporting without asking an analyst to rebuild the pack every Monday.

Scheduled Analytics dashboards with RBAC-governed access; delivery via email or SFTP on any cadence; drill-through into the underlying governed data.

Nightly batch windows leave operational reporting hours to a day behind the transactional truth.

Change Data Capture (log, query, or trigger-based) replaces the nightly batch with minute-grained sync; schema drift is caught before it breaks downstream.

Product teams wait weeks for the data team to build the custom report they need for this sprint.

Data Insider exposes governed data as a versioned REST API with rate limits and row and column-level security; product engineers build against it like any other service.

Manual feature engineering in notebooks delays every model retraining cycle.

Transformer builds a visual Spark feature pipeline; ML Studio trains, deploys, and monitors for drift with versioned REST endpoints.

Manual forecasting spreadsheets buckle under hundreds of SKUs and seasonality changes.

Forecaster runs time-series algorithms per SKU on a daily schedule; Analytics surfaces accuracy trends and backtests.

Repetitive ticket triage and provisioning tasks drain IT hours with zero strategic upside.

ProcBot workflows handle routing, approvals, provisioning, and notifications end to end, with full audit.

Datasets are self-classified at creation and never revisited. A table of national identifiers sat classified as internal for two years because the person who created it picked the default and nobody reviewed it.

Explainability Fabric derives a classification floor from three independent sources: the tags on the columns, regex on the column names, and the classification of every upstream dataset. A derived dataset cannot be classified lower than what it was built from, and a downgrade needs a data steward approval with a written justification.

A pipeline published a table with 40 percent nulls in a key column and three dashboards were wrong for a week before anyone noticed.

Explainability Fabric evaluates completeness, uniqueness, freshness, row-count deviation and schema change against per-tier thresholds before publication. A check that was skipped counts as a failure, and an override needs a named approver and a reason. Every outcome emits a quality record, including the blocks.

Nothing is ever deleted, subject access requests take weeks, and a cleanup script once purged a dataset that was already under legal hold.

Retention is computed from the purpose the data was collected for. An active legal hold blocks deletion absolutely, an erasure request against a legal-obligation basis is refused with the basis named, and the decision returns the derived datasets that would also have to go. Every outcome writes a deletion record.

Releases get approved in a chat channel. Two incidents were traced to a release approved by its own author during a change freeze.

An approval only counts when it is for the same commit, inside its validity window, from someone other than the author, and from a person who currently holds the approver role for that service. Required checks must have passed, and a freeze window can only be crossed with an incident reference and an on-call approver.

A pipeline misses its window overnight. The breach is found the next morning by the person whose dashboard is empty.

DataOps tracks the SLA clock and raises the breach as it happens. Sherlock correlates the failure against change events and historical incidents to isolate the cause, ProcBot runs the remediation, and closing a breached run requires a written reason that feeds the recurring-failure analysis.

An unattended agent with a broad service account and no named owner closed 300 open tickets while interpreting its goal.

Every agent action is checked against a delegation that names an accountable human and carries an expiry. Actions are capped per run and per hour, an agent can never touch its own configuration, permissions, or the audit log, and anything irreversible or wide-reaching has to be approved before it runs.

Tools are granted to the assistant, so anyone using it inherits every capability the assistant has, regardless of their own permissions.

Tool authorisation is evaluated against the end user behind the request. A refund tool checks the amount against that user own limit, recipient addresses are checked against an allowlist, and high-risk tools need an explicit entitlement. Parameters coming back from the model are treated as untrusted input.

Workloads reach production with no owner label, no resource limits, and images tagged latest, so nobody can answer which release is affected by a new CVE.

Every workload is checked at admission for an owner and environment label, CPU and memory limits on all containers including init containers, an image pinned by digest from an approved registry with a valid signature, and no host networking in production namespaces.

Credentials get committed to config, passed as plain environment variables, and left unrotated for years. A leaked key was found in a public repository with no way to tell what it reached.

Explainability Fabric detects credentials two ways: by variable name and by the shape of the value itself, including access key ids, private key blocks and bearer tokens. Inline values are refused in production, the secret manager must be an approved one, and a secret past its rotation window fails on its own.

Compliance audits require manual classification of PII and hand-written lineage documentation.

Data Catalog auto-tags PII at ingest, applies classification, and generates audit-ready lineage reports across every module.