Sektus Technologies
Rapid transformations with AI, Robotics & Automation.
We help operators turn messy processes into measurable outcomes—moving from idea to production in weeks, not years. Reduce cost, compress cycle times, and raise quality with pragmatic engineering and data‑driven decisions.
What we do
Full‑stack delivery across software and hardware—so you don’t get stuck between vendors. We meet you where operations live: ERP, shop floor, and the edge.
AI AI in Operations
Operational AI & copilots
Surface KPIs, forecast issues, and automate decisions inside the tools your team already uses.
- Operations Copilot: ask about margin, demand, exceptions, shortages.
- Document AI: parse POs/invoices and auto‑create transactions.
- Anomaly detection and early‑warning alerts.
R Robotics & Vision
Robotics, perception, & motion
From proof‑of‑concept rigs to robust prototypes—sensors, motor control, safety, and autonomy.
- Machine vision & 3D sensing for inspection and guidance.
- Stepper/servo control, edge compute, real‑time control.
- Material handling, mobile platforms, and HMI.
A Automation & Integration
Systems integration & automation
Automate the boring, orchestrate the complex, and make data flow without manual babysitting.
- REST APIs, event pipelines, and workflow orchestration.
- ERP ↔ MES ↔ Edge telemetry and traceability.
- Secure deployments, monitoring, and MLOps hygiene.
Case studies (vendor‑neutral, anonymized)
Representative projects across manufacturing, logistics, and hardware. Client names and platforms are intentionally omitted; results are based on baselines and audits agreed during delivery.
⚙ Mid‑market manufacturer
Operations Copilot for planning & exceptions
A conversational interface over live operational data to surface shortages, margin risk, and next‑best actions.
Read the full case ›
Company size: ~400 FTE
SKUs: 1.6k
Sites: 4
Timeline: 12 weeks
- Problem: Reactive planning; tribal knowledge; late shortage discovery; scattered KPIs.
- Approach: Built an operations copilot that answered plain‑language questions (“what’s blocking order 45021?”), generated shortage lists with root causes, and proposed expedites/substitutions with justification. Guardrails enforced approvals and audit trails.
- Impact: 34% faster plan‑to‑commit cycle, 29% fewer stockouts, ~1.8 FTE of manual triage eliminated. First value in week 4.
- Tech notes: Data virtualization across ERP/MES, embeddings for semantic search, retrieval‑augmented generation, and role‑based access control. No vendor lock‑in.
📄 Global importer
Document flow automation (PO → receipt → invoice)
Automated extraction, validation, and posting of procurement documents with a human‑in‑the‑loop for exceptions.
Read the full case ›
Docs/month: ~800
Error baseline: 3.5%
Timeline: 10 weeks
- Problem: High manual effort to process POs, packing lists, and invoices; frequent keying errors and mismatches.
- Approach: Deployed document AI with field‑level confidence scores, business‑rule validation (quantities, terms, currency), and exception queues. Wrote back to transactional systems via APIs with full traceability.
- Impact: 71% reduction in processing time, error rate down to 0.7%, GRNI clearing 22% faster, cycle‑time predictability increased.
- Tech notes: Zero use of client data to train public models; fine‑tuning and vectorization isolated in client tenancy.
🤖 Consumer goods
Vision‑guided inspection cell
A small robotic cell to detect cosmetic defects and gate flow automatically with operator‑friendly HMI.
Read the full case ›
Lines retrofitted: 2
Shift coverage: 3
ROI: ~7 months
- Problem: Inconsistent manual inspection; high scrap and rework from late detection of cosmetic defects.
- Approach: Deployed cameras and lighting with real‑time inference; ejected non‑conforming units; operators could override with reasons logged. Safety interlocks and e‑stops added; OEE dashboards exposed.
- Impact: Scrap down 38%, rework down 52%, first‑pass yield up 11 points. Stable across lighting and run‑rate changes.
- Tech notes: Edge inference on a GPU‑enabled module, deterministic motion profiles, encrypted telemetry to central monitoring.
All case studies omit vendor and platform names by design. Detailed references are available under NDA.
The risk of not moving now
Innovation isn’t only upside—inaction carries compounding costs.
1) Compounding opportunity cost
Every quarter without automation “locks in” waste—longer lead times, higher COGS, and slower cash cycles—while competitors compound their gains.
2) Talent attraction & retention
High‑performers expect modern tools. Outdated stacks repel the exact people who could fix them.
3) Data debt
If processes aren’t instrumented today, tomorrow’s AI can’t learn from them. Poor data now becomes tomorrow’s ceiling.
4) Operational fragility
Manual, tribal workflows break under stress. Instrumented, automated flows bend—then recover.
How we work
Clear governance, rapid feedback, and production‑first engineering.
01 — Define & Prioritize
- Pick high‑leverage use cases with visible ROI.
- Set metrics, owners, and guardrails.
- Design for reversibility and safety.
02 — Build the Thin Slice
- Small vertical slice: data → model → app → workflow.
- Ship to real users; monitor in the wild.
- Iterate weekly; remove manual touches.
03 — Scale & Transfer
- Harden, document, and train.
- Handover runbooks & observability dashboards.
- Rinse & repeat on the next slice.