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Agentic AI Engineering
Autonomous agents that reason over your data, call your systems, and finish multi-step work — with guardrails, escalation paths, and full audit trails.
GAMINOVA designs production AI agents, resilient cloud platforms, and automation that quietly removes the friction from how your business actually runs.
Six disciplines, one team. We take responsibility for the whole system — from the model layer up to the deployment pipeline.
Autonomous agents that reason over your data, call your systems, and finish multi-step work — with guardrails, escalation paths, and full audit trails.
Teams of specialised agents that delegate, review each other's output, and carry cross-domain workflows from trigger to completion.
LLM-driven routing, RPA, and BPM wired into the tools your teams already use — removing manual steps without a rip-and-replace programme.
Retrieval-augmented knowledge across documents, tickets, and wikis — source-grounded answers, hybrid search, and access control that respects your org chart.
Multi-tenant SaaS and internal platforms on Kubernetes, event-driven architecture, and GitOps delivery — built to be changed, not just launched.
Streaming pipelines, warehouses, and decision layers that turn operational exhaust into measurable advantage rather than another dashboard.
They didn't just ship us a platform — they rewired how our operations think. What used to take three weeks now finishes before lunch.
Four commitments that shape every engagement, written down because they are the ones most vendors quietly drop.
We design and build the system ourselves. No licence reselling, no offshore hand-offs, no gap between the architect who drew it and the team who shipped it.
A working slice in production inside the first three weeks, then tight iterations. You measure progress in released behaviour, not in slide decks.
Threat modelling, least-privilege access, encryption in transit and at rest, and SOC 2-aligned process from day one — never a retrofit before launch.
We stay on after go-live: observability, on-call rotation, and a quarterly optimisation cycle so the system keeps compounding its return.
Three engagements where the measurable outcome mattered more than the architecture diagram.
A multi-agent system that tenders loads, negotiates with carriers, and re-plans routes in real time as conditions shift mid-journey.
Empty miles down 23%; on-time delivery up 18% across 4,200 daily shipments.
Agents assemble borrower files from fourteen sources, apply policy consistently, and return an auditable decision with its rationale attached.
Decision time 6 min → 40 s; default rate down 31% within two quarters.
A feature pipeline and ranking service that re-scores every session on what viewers are doing now, not on what they did last week.
Engagement up 27%; churn down 14% over two consecutive quarters.
Two weeks inside your process, mapping the workflow that actually happens rather than the one that is documented.
A written design: data flows, agent boundaries, failure modes, and the rollback plan before a line of production code.
Incremental releases behind feature flags, with evaluation harnesses and telemetry on real usage from the first week.
Monthly tuning against your KPIs. The system is expected to get measurably better the longer it runs.
Tell us the process that slows you down. We'll come back with a concrete architecture and a date for the first release — usually inside two weeks.
Describe the bottleneck. You'll get a technical response from an engineer, not a scheduling email from a sales desk.