[FIELD-01]LIVE
Real-Time Data Systems
Live price feeds, WebSocket delivery, failover and watchdog architectures.
→ DISPARCO AI: DATA PIPELINE
// PRODUCTION-DRIVEN R&D · AI · PRODUCT SHOWCASE
Distilling Data into Models, Models into Products.
Disparco R&D is a production-driven research line working on real-time data systems, audio & music technologies, automation and applied AI. We don't just research here — we ship products too: the work that passes through the lab is put on display in a showcase. Every idea enters the lab as a hypothesis and is tested against real data.
LAB ACTIVE· PRODUCTION · DATA · MODEL · PRODUCT· BINGOL, TR· --:--:--
MODEL CORE // v2.0 STATUS: WARMING UP
PARTICLES
—
SAMPLES PROCESSED
0
SIGNAL
● LIVE
SESSION SAMPLES
0
LOCAL TIME
--:--:--
MONITORING
24/7
The numeric values on this strip are being measured in your browser right now — no claims here, only measurement.
// RESEARCH FIELDS — DATA · MODEL · PRODUCTION
What Are We Working On?
Every field carries an honest status label: LIVE is used only for systems running in production — the rest is development or research, and we say so.
[FIELD-02]LIVE
Automation & Bots
Community bots, OTA update systems, self-healing service patterns.
→ DISPARCO AI: AUTOMATION INTERFACE
[FIELD-03]IN DEV
Audio & Music Technologies
Distribution platform experience, catalog metadata enrichment, audio processing.
→ DISPARCO AI: AUDIO-METADATA INTELLIGENCE
[FIELD-04]IN DEV
Forecasting & Data Engineering
Time-series analysis, anomaly detection, data pipelines.
→ DISPARCO AI: PREDICTION LAYER
[FIELD-05]IN DEV
Applied AI
LLM-assisted workflows, model serving, human-in-the-loop automation.
→ DISPARCO AI: CORE
[FIELD-06]RESEARCH
Computer Vision
Exploration of visual quality control and media tagging scenarios.
→ LAB NOTEBOOK: OBSERVATION
// PRODUCT SHOWCASE — FROM LAB TO FIELD
Products in Production
R&D is not only a research line; the work that passes through the lab turns into products. Each product carries an honest status label — no hype, we describe what the product actually does.
[PRODUCT-01]IN DEV
Health Assistant
PLATFORM: Mobile appA personal assistant for health: a mobile app that helps users organize their health tracking and manage their daily routine.
WHAT IT DOES Soon
[PRODUCT-02]IN DEV
AI Prompt Builder
PLATFORM: Web appA web app that helps users craft effective prompts for AI models: it turns a scattered idea into a clear, result-oriented instruction.
WHAT IT DOES Soon
STATUS: CLOSED-LOOP TEST SOON
DİSPARCO AIv2.0
Alongside the products in the showcase, a larger effort is underway that converges every line of the lab into a single product. We are working on the data pipeline, prediction layer, audio-metadata intelligence and automation interface — and we aim to finish measuring before we start describing. No rush, further down the road.
"Specifications will be published on this page once validation is complete."
[MODULE 01]
Data Pipeline
[MODULE 02]
Prediction Layer
[MODULE 03]
Audio-Metadata Intelligence
[MODULE 04]
Automation Interface
v1.0
INTERNAL PROTOTYPE
completed
v2.0
CLOSED BETA
in preparation
—
GENERAL ACCESS
queued
// METHOD
The Research Loop
R&D here is not a line but a loop: signals from production seed the next hypothesis.
01
OBSERVE
We collect signals and questions from production systems in the field.
[LOG] source=field → queue: 3 questions02
HYPOTHESIZE
We turn the problem into the smallest measurable, verifiable claim.
[DEF] hypothesis=H-12 · metric: set03
EXPERIMENT
We build a small, fast prototype and test it on real data. A failing hypothesis is shelved — and we say so.
[MEASURE] hypothesis=H-12 → result: REJECTED04
SHIP
A passing experiment ships with monitoring, failover and a rollback plan.
[RELEASE] H-09 → production · monitoring: ON Nothing counts as a result until it is validated in the field.
// LAB NOTEBOOK
Records from the Field
Not showcase projects — notebook entries: experiments born from real production systems, unnamed, without number claims, as they are.
[EXP-A1]IN PRODUCTION
HYPOTHESISLive price delivery can fail over to a diff-based backup source at the moment of outage.
SETUPRetail chain field screens; dual source + watchdog.
OBSERVATIONOutage scenario validated in the field; screens stayed live.
[EXP-A2]IN PRODUCTION
HYPOTHESISA distributed device fleet can be updated safely via centrally signed manifests.
SETUPAndroid-based field devices; OTA manifest + version control.
OBSERVATIONFleet-wide updates validated in the field; rollback plan tested.
[EXP-A3]IN PRODUCTION
HYPOTHESISCommunity automation can stay uninterrupted with a self-healing architecture.
SETUPMessaging platform bot; retries + heartbeat monitoring.
OBSERVATIONConnection drops observed in the field; service recovered without intervention.
[EXP-A4]IN DEV
HYPOTHESISMetadata enrichment in a music catalog can be accelerated with automated matching.
SETUPCatalog of our own distribution platform; matching pipeline.
OBSERVATIONIn development; first matching results under internal review.
[PRINCIPLE-01]
No Claim Without Measurement
every capability we publish is validated with production data
[PRINCIPLE-02]
A Failed Experiment Is Still a Record
shelved hypotheses stay in the notebook; no mistake is made twice
[PRINCIPLE-03]
Production or It Doesn't Count
code isn't discarded after the experiment: it ships with monitoring and rollback
[PRINCIPLE-04]
Data Privacy
KVKK-compliant data minimization; experiments run on anonymized data
// CONTACT Have a problem?
Have a problem?
Let's Experiment.
If you have a data-heavy problem, a process waiting to become automation, or a question about Disparco AI — write to us, and let's turn the problem into a measurable hypothesis together. The first call is free.