The technology behind every KonexForge Pilot Build: one 6-layer system, not 6 separate products
Most services pages list technology like a spec sheet — MQTT, Kubernetes, dbt, PyTorch, React, Grafana. But technology only creates value when it's assembled correctly into a closed-loop system. An overview of the 6 layers KonexForge builds with for clients, backed by real evidence from delivered projects.
Nearly every technology services page has the same block: "we use MQTT, Kubernetes, dbt, PyTorch, React, Grafana...". The list is accurate, but nearly meaningless in isolation — a well-running IoT firmware creates no value if the data it collects can't flow into a properly designed database; an accurately trained AI model creates no value with no pipeline to get it into production; a beautiful dashboard creates no value if the underlying data isn't continuously monitored for quality. This post doesn't list technology as a spec sheet — it shows how the 6 layers KonexForge builds with for clients assemble into a single system, backed by real evidence from delivered projects, not marketing copy.
Layer 01 — IoT & Sensors: capturing physical data correctly at the source
Stack: MQTT, LoRaWAN, OPC-UA, ESP32, Edge ML. This is the layer that captures real data from sensors, cameras, and industrial equipment — with custom-built C/C++ and Rust firmware, safe OTA updates with automatic rollback, and Edge AI inference running directly on-device when network latency isn't acceptable. In the predictive maintenance system for 4 steel plants, our network of 3,200 vibration sensors and thermal cameras reached p99 latency under 12ms and cut downtime by 41% — because the failure-prediction model runs right at the edge, with no round-trip to the cloud.
Layer 02 — Server & Database: infrastructure isn't a fixed cost
Stack: Kubernetes, PostgreSQL, ClickHouse, Argo CD, Terraform. The storage and operations layer — with GitOps, hybrid cloud, and a FinOps dashboard that tracks cost continuously instead of letting the cloud bill creep up silently. Choosing the right database engine for the right problem (a topic we cover in depth in database architecture for IoT time-series systems) is a decision that directly drives the 99.98% uptime SLA and 34% cost reduction this layer achieves on systems currently in production.
Layer 03 — Data Analytics: raw data into actionable insight in weeks, not quarters
Stack: dbt, Airflow, Superset, BigQuery, Looker. Versioned, testable ELT pipelines, clear data contracts between teams, and self-service dashboards for people who aren't data engineers. In the demand-forecasting system for 240 F&B stores, an ETL pipeline merging sales and weather data cut time-to-insight by 68% — and the data quality monitoring layer (detailed in data quality monitoring from dbt tests to anomaly detection) keeps 99.4% of the data feeding the model clean before it ever influences a real business decision.
Layer 04 — AI & ML: integrated into the business process, not a shelved POC
Stack: PyTorch, MLflow, LangChain, ONNX, Triton. This is the most commonly misunderstood layer — not "add a chatbot," but forecasting, computer vision, and LLM agents wired directly into a business process with before/after measurement. In the KYC automation system for a tier-1 fintech, an LLM agent handling OCR and face matching automated 92% of applications while cutting cost per request by 68%. At the orchestration layer, KonexForge AI Core decides which AI handles which task — and as of this month, that Router layer has a new cost-optimized option: Z.ai's GLM-5.2 for high-volume, non-mission-critical coding tasks.
Layer 05 — Development: pixel-precise frontend, backend that holds up under real load
Stack: React, Next.js, PHP, C#, C++, Go, Swift. Web, mobile, and internal tools with a shared design system, end-to-end tests, and weekly deploys instead of quarterly ones. The clearest evidence of engineering quality at this layer isn't on a portfolio page — it's that we maintain and open-source our own tooling: Autumn Note is a zero-dependency rich-text editor we built and reuse across Pilot Builds that need content editing, with all the code, tests, and architecture decisions public on GitHub — not just a line of description on a services page.
Layer 06 — Optimization Loop: a system that improves every month instead of decaying over time
Stack: OpenTelemetry, Grafana, GrowthBook, Sentry. This is the layer that closes the loop — observability, A/B testing, a retraining pipeline with guardrails, and a feedback loop from real users. Without this layer, the other 5 layers are just a system "frozen at handover": data quality degrades unnoticed (see monitoring drift in production AI models), or alerts pile up until the team starts ignoring the real ones (see designing an alert pipeline that avoids alert fatigue). With the Optimization Loop layer in place, the average iteration cycle is 1 week, and the measured improvement per quarter is +14%.
Why it has to be one system, not 6 separate products
A principle we hold to consistently: every technical decision has to fit within the six-layer picture — separation is the root cause of failure. A team that hires one vendor for IoT, a different agency for web, and a separate freelancer for AI usually ends up with 3-4 systems that can't talk to each other — sensor data that never reaches the dashboard, a trained AI model no one ever puts into production, a web app that knows nothing about real operational data. KonexForge operates all 6 layers as a single closed loop, with one point of contact and one team accountable for the entire loop — not six invoices from six different vendors.
A no-lock-in approach, even to our own technology
This approach doesn't mean locking clients into a single stack. At the AI & ML layer, the Router can route between multiple models based on cost and task sensitivity; at every layer, full source code and operational documentation are handed over after a Pilot Build, meaning clients can always operate independently without depending on us. If you want to see these 6 layers applied to your actual problem — from a quick audit Discovery Sprint to a full Pilot Build — get in touch to start with a free 30-minute call.
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