We don't sell AI as a product. We use AI — alongside senior engineering judgment — to deliver cloud, data, security, reliability, and cost engagements faster, more thoroughly, and with fewer blind spots than manual methods alone. Every AI-assisted output is reviewed by a senior practitioner before it reaches you.
Gigamatics is a cloud, data, security, reliability, and FinOps consultancy. That doesn't change. What has changed is how we get the work done: modern AI tooling — large language models, anomaly detection, and automation frameworks — now lets our practitioners analyse more, catch more, and document more thoroughly per engagement than was practical by hand. AI accelerates our senior engineers. It does not replace their judgment, and it is never the thing we're selling you.
AI-assisted schema mapping, automated data quality anomaly detection, and AI-generated lineage documentation speed up migration and governance work.
Learn more →AI-assisted architecture review, IaC validation, and cost/rightsizing analysis surface risks and savings faster than manual review alone.
Learn more →AI-assisted log analysis, anomaly detection, and compliance evidence mapping help us cover more ground during security assessments.
Learn more →AI-driven anomaly detection, predictive alerting, and automated first-draft RCA documentation shorten incident response and review cycles.
Learn more →AI-based usage pattern analysis and anomaly detection identify rightsizing and waste-elimination opportunities that manual review often misses.
Learn more →AI-assisted monitoring, ticket triage, and reporting help our managed operations teams respond faster and report more consistently.
Learn more →Not abstractions — specific, practical uses of AI tooling that our practitioners apply during real engagements, always with a human reviewing the output before it's acted on.
During cloud, data, and security assessments, AI tooling helps our engineers process large volumes of configuration, logs, and documentation quickly — surfacing patterns and risks that would take far longer to find manually. The assessment findings are still validated and prioritised by a senior practitioner.
Across reliability, security, and FinOps engagements, AI-based anomaly detection helps flag unusual cost spikes, configuration drift, and performance deviations earlier than threshold-based alerting alone typically catches them.
AI-assisted drafting speeds up the first pass of runbooks, architecture documentation, and incident reports — freeing our engineers to spend more time reviewing and refining accuracy rather than writing from a blank page.
We build small, targeted automations — using AI where it fits and conventional scripting where it doesn't — to remove repetitive manual work from our own delivery process, so senior time goes toward judgment calls, not data entry.
We're deliberately cautious about AI hype. Tools that generate confident-sounding but wrong answers are a liability in enterprise environments, not an asset. Our approach is narrow and practical: use AI where it demonstrably improves speed or accuracy, keep a senior human accountable for every output, and never let a tool make a decision a practitioner should be making.
AI tooling is applied selectively — to specific, well-understood tasks where it clearly saves time or catches more than manual review. It is not applied for its own sake.
Nothing AI-assisted reaches a client without a senior practitioner reviewing it first. AI drafts and flags; people decide and sign off.
If an AI-assisted recommendation is wrong, that is our error to own and fix, not a limitation we point to. The practitioner is always accountable.
Any AI tooling used on client data follows the same confidentiality, access control, and data handling standards as the rest of our engagement — no exceptions.
Some clients do need an AI or ML system built — a predictive model, a retrieval-augmented internal assistant, an automation pipeline. Where that's a genuine part of a broader Data Modernization or Cloud & Infrastructure engagement, we scope it as an extension of that practice, with the same architecture-first, governance-led standards as everything else we build. It is not a separate "AI product line" — it is applied engineering, delivered by the same senior practitioners, under the same standards.
No. We're a cloud, data, security, reliability, and FinOps consultancy. We use AI tooling to help our practitioners deliver those services faster and more thoroughly — it's a capability we apply, not a product we sell on its own.
Yes, where it's a genuine part of a Data Modernization or Cloud & Infrastructure engagement — a predictive model, an internal RAG assistant, an automation pipeline. We scope it under those practices with the same architecture and governance standards as everything else we deliver.
No. AI tooling drafts, flags, and accelerates analysis. A senior practitioner reviews and signs off on every output before it's delivered or acted on. Accountability always stays with a person, never the tool.
The same confidentiality, access control, and data handling standards that govern the rest of your engagement apply to any AI tooling we use — no separate, looser standard for AI-assisted work.
Tell us what you're working on, and we'll show you where AI-augmented delivery genuinely helps — and where it doesn't.
A structured conversation about your engagement, and where AI-assisted delivery would genuinely add speed or accuracy.
We'll tell you plainly where AI helps and where it doesn't — not a generic AI pitch.
You speak with the senior practitioner who would lead your engagement, not a pre-sales layer.