AI that ships — and stays shippable.
Every company has an AI demo; far fewer have AI in production a year later. We build LLM-powered tools and automations that survive contact with real data, real users, and real attackers — because the team wiring the model in is the same team that knows how models get abused.
See our AI work: an instant security check.
Enter any website below. Our engine inspects its public security posture and an AI writes the summary — built the same secure-by-design way we build for clients. Your input runs on Sungensis infrastructure and is never sent to any third party.
The gap between a demo and a system.
Getting a language model to do something impressive takes an afternoon. Getting it to do the same thing reliably — with messy inputs, cost controls, evaluation, fallbacks, and audit trails — is engineering. That second part is what we sell.
And because LLM systems introduce a genuinely new attack surface — prompt injection, data exfiltration through model outputs, over-privileged agents — we design them the way we design everything: assuming someone hostile is reading the docs too.
What we build
- Document pipelines — extraction, classification and summarization over invoices, contracts, reports and forms, with human review where it matters.
- Agents & assistants — internal copilots and customer-facing assistants with strictly scoped tools, grounded in your data instead of hallucinating around it.
- Workflow automation — the swivel-chair work between your systems (tickets, CRM, spreadsheets, email) automated end-to-end.
- Report & content generation — structured drafting pipelines with your templates, your tone, and verifiable sources.
- Messaging automation — AI-driven WhatsApp and chat flows that hand over to humans gracefully — see our messaging platform work.
How we keep it trustworthy
- Least-privilege agents — models get the narrowest tools that do the job; nothing talks to production data it doesn't need.
- Injection-aware design — untrusted content is treated as data, not instructions, and boundary-tested like any other input we'd attack.
- Evaluation before launch — measured accuracy on your real cases, not vibes; regressions caught when prompts or models change.
- Cost & observability — token budgets, tracing and logging, so you know what it's doing and what it costs per run.
Before you ask.
Which AI models and providers do you use?
Whichever fits the job and your constraints — commercial APIs where they're strongest, self-hosted open-weight models where data residency or cost demands it. We design so the model is a swappable component: when a better or cheaper one appears, you migrate a config, not a codebase.
Our data is sensitive — where does it go?
That's decided explicitly at design time, not discovered later. Options range from zero-retention API agreements to fully self-hosted models inside your own cloud, and we document exactly which data classes may reach which components. For DPDP-regulated data we align the design with the same controls we implement in our readiness engagements.
What if the model gets things wrong?
We assume it will, and design for it: confidence thresholds, human-in-the-loop review on consequential actions, grounding in your documents with citations, and evaluation suites that measure error rates on your real cases before anything goes live. AI that can't be wrong safely shouldn't be deployed.
Who maintains it after launch?
Your team, if you want — everything ships with documentation, runbooks and a handover session, built on mainstream tooling rather than a framework only we understand. If you'd rather not own it, a support retainer keeps us on the hook for prompts, models, and evolution.
Somewhere in your company, hours are being spent on minutes of work.
Tell us where. If AI is the wrong answer, we'll say so — that honesty is why the right answers ship.
Find the use case →