Databallast
Data ready for AI, at scale
Get your organisation's data ready for AI at scale - a shared business language, mapped and certified data, and DAMA-aligned governance that keeps every AI grounded in data it can trust.
AI doesn't fail on models. It fails on data.
Every serious AI initiative lands on the same blockers: no shared business language, no clear picture of what data exists or who owns it, and no way to say whether a dataset can legally and safely be used by an AI. Generative AI amplifies data debt - an ungoverned data estate becomes an ungoverned AI estate.
Data catalogs and governance programs exist, but none of them answer the question boards now ask: is our data actually ready for AI? Databallast is built around that question - getting one dataset ready is a project; keeping the whole estate ready, use case after use case, is what Databallast does. The ballast that keeps the AI ship stable.
How Databallast helps your organisation
One business language for people and AI
A governed business glossary and logical business information model give your organisation - and your AI - a single, approved meaning for every term.
Know your data, end to end
A living catalog of datasets with ownership, classification, quality and lineage - including which AI systems consume which data.
Certify data AI-ready
Every dataset is assessed for rights and licensing, privacy, quality, freshness, bias, documentation and retrieval-readiness - and earns an AI-Ready mark a CDO can stand behind.
Governance as an operating capability
DAMA-aligned stewardship, policies, quality rules and issue management - plus a maturity assessment that shows the board measurable progress.
What Databallast does
Chief Data Officers, data governance and architecture leaders, and AI leads who need their data estate ready for AI - and provable.
- Business glossary with approval workflow and AI-context annotations
- Metadata catalog, classification and data lineage
- Logical business information model mapped to glossary and datasets
- DAMA-DMBOK data maturity assessment with uplift roadmap
- Data products and AI use cases with readiness gating
- DAMA-aligned governance: stewardship, policies, quality rules, issues
- AI-readiness certification per dataset (rights, privacy, quality, bias, retrieval)
- Feeds Execdive's semantic layer, Meshbone data dependencies and Orbit roadmaps
How every deployment is structured
Standard rails. Made-to-measure services.
The governance platform is ready-made and the same for every customer, which is why it can be assessed once. The services that run on it are built to fit the way your organisation actually works. You get software that fits, and it is still governed.
Governance rails
Ready-made. Identical for every customer.
AIG Sentinel sets the policy and holds the evidence. Meshbone enforces it at run time on every agent and service. AgentGUARD™ is the set of rogue-agent controls the rails enforce on every agent they run. Because the rails are the same everywhere, an auditor assesses them once and every service built on them inherits the result.
Service templates
Productised. Drawn from the catalogue.
Grants, cases, permits, procurement, records, assets: each starts from a template that already runs on the rails, with intake, assessment, decision and record built in. A new service begins most of the way there rather than from a blank page.
Made to measure
Built for you. A small share of every build.
Your rules, your data model, your thresholds and your approval chain, configured and coded onto the template. Anything a second customer could use goes back into the template, so the custom share stays small and each build is faster than the last.
Run and support
Ongoing, on the platform.
Hosting, monitoring, policy re-attestation and upgrades, in your region. After go-live your own team changes a threshold or a form within guardrails, without waiting on us.
The rule
No made-to-measure build without the platform underneath it. That is the rule that keeps a custom service governed, upgradeable and provable, rather than a fork somebody has to maintain by hand forever.
Guarded by AgentGUARD™
Every agent, on every product, under the same four classes of control
Directive, preventive, detective and corrective. Written in AIG Sentinel, enforced on Meshbone, carried in every Nanolitte template, evidenced on one chain.
Databallast: says what an agent may read. Classification, lineage and AI-readiness per dataset tell an agent what a field means, where it came from and whether it may be used, before the agent reads it.
See the controlsDirective
What the agent is allowed to do, as data.
Preventive
What stops it before it acts.
Detective
How we know when it goes wrong.
Corrective
How it is stopped, reversed and fixed.
One architecture, every product
A full AI-agentic experience
Every Arrochar Labs product is built the same way: agentic, event-driven, and governed with human-in-the-loop controls, so AI does the work while your people stay in control.
Agentic
AI agents do the work, end to end. Every product runs on autonomous agents that take action across your processes, not just a chat box, but software that actually gets the job done.
Event-driven
It reacts in real time. Products respond to what's actually happening (new data, a policy breach, an incoming request, a change in your estate) instead of waiting on a batch run or a person to press go.
Human-in-the-loop
Your people stay in control. Approval gates, oversight and a tamper-evident audit trail keep every agent accountable, so automation never runs unchecked.
See Databallast for yourself
Visit databallast.com to go deeper, or talk to us about how Databallast fits your organisation.
Part of one accountable AI lifecycle
Arrochar Labs builds AI as one lifecycle - discover, govern, build, operate, ground, decide. Each product is a pillar.