Cogni Lynch
Your Competitors Just Deployed AI. Are You Already Too Late?
We've got you covered.
We build Production & Research-level AI & ML systems—shipped fast, backed by fullstack web and mobile development.
Don't underestimate the power of AI moving at speed.
10x
Faster document workflows
97%
Agents rate in pipelines
40%
Lower inference cost
$1–2M
Estimated annual savings
Cogni Lynch Builds
AI / ML Core
DEEP LEARNING · NEURAL NETWORKS · SELF-SUPERVISED LEARNING · REPRESENTATION LEARNING · FOUNDATION ARCHITECTURES
LLMs & GenAI, MLOps / Infra
MODEL DEPLOYMENT · MODEL MONITORING · PIPELINE ORCHESTRATION · EXPERIMENT TRACKING · FEATURE STORES · SCALABLE INFERENCE
Vision / Multimodal & Retrieval
EMBEDDINGS · SEMANTIC SEARCH · KNOWLEDGE GRAPHS · HYBRID RETRIEVAL · DOCUMENT INDEXING · IMAGE UNDERSTANDING · VIDEO ANALYTICS · OCR PIPELINES · AUDIO-TEXT MODELS · CROSS-MODAL LEARNING
Inside the work
Infrastructure, models, and shipped interfaces from our own projects. Scroll to bring it forward, then click any tile to open it.
Step 01
We connect models, data, and tools into a coherent system—so teams ship faster.
Read more about custom LLM developmentFunded SaaS products, client platforms, and applied research systems — several shipped straight out of our own peer-reviewed work.
An AI trading decentralized platform using neural networks to automate crypto and NFT trading through blockchain smart contracts and DAO governance.
Product variations, inventory tracking, reviews, and order management.
Real-time security system using YOLO + ML for loss prevention.
YOLO-based number plate detection for traffic and vehicle ID.
Ultrasound kidney stone detection using knowledge-distilled CNNs.
This system uses computer vision to automatically detect, track, and label players, objects, and on-court interactions across video frames, enabling precise spatiotemporal analysis of complex sports scenarios.
1-bit quantized language models with reinforcement-tuned policies, so assistants run on edge hardware instead of a GPU bill.
Built on our research · BitRL, IEEE FET 2026
Fine-tune LLMs across hospitals, branches, and devices without raw data ever leaving the client.
Built on our research · Scientific Reports (Nature Portfolio, Q1)
Segments, classifies, and counts nuclei in histopathology slides in a single pass, so labs get one report instead of three tools.
Built on our research · MedViT-HoVer++, AHTBE 2025
An augmented 3D U-Net pipeline that turns multimodal MRI volumes into reviewable tumor masks inside existing radiology workflows.
Built on our research · Springer ICADCML 2025
Ranks products for brand-new users and freshly listed SKUs from day one, instead of waiting weeks for click history.
Built on our research · Springer ICEEE 2024
Pricing, demand, and customer-segment forecasts that ship with LIME and SHAP reason codes, so teams can defend every number.
Built on our research · ICMLA 2025 & IEEE ISDFS 2025
Federated deep reinforcement learning with a shared dynamics encoder, giving each building a personalized battery policy without pooling its consumption data.
Built on our research · FedGrid-RL, Scientific Reports (Nature Portfolio, Q1)
Measures token-length timing leakage in continuous-batching servers like vLLM and SGLang, so a shared endpoint stops leaking what other tenants asked.
Active R&D · Batch Echoes — targeting USENIX Security 2027
Stress-tests LLM honeypots against adversarial agents that suppress their own behavioral signals, and reports where the detection actually collapses.
Active R&D · Deception Collapse — targeting IEEE TIFS
An LLM-augmented agent formalized as a POMDP/Semi-MDP, probing tool-calling systems for exploitable paths so teams find them before an attacker does.
Active R&D · Tool Poisoning — targeting NDSS
An adaptive music therapy platform that personalizes each session for people with Down syndrome, ADHD, and dementia, and adjusts as the response data comes back.
Live product · $3M venture round · advised by Dr. Mustak Ibn Ayub (University of Oxford)
Reads symptoms and prescription images, explains what they mean in plain language, and routes the patient to the right specialist.
Live product · Shipped and in production use
Dedicated fiber and colocation drive low-latency performance.
On-demand bandwidth and 99.99% availability stay live under pressure.
Integrated cybersecurity intelligence puts the brakes on threats.
Cloud-based voice and collaboration keep your teams connected.
Stay ahead of today's demands with cross-industry networking built to scale AI workloads.
The market moves fast. Our network delivers secure, low-latency performance for real-time transactions.
From inventory intelligence to AI personalization, stay resilient with always-on edge connectivity.
High-throughput delivery keeps every frame streaming while protecting live creative workflows.
Research
Academic and applied research spanning medical imaging, NLP, and trustworthy AI systems.
IEEE Xplore, France, Nov 2024
ICAII, Washington D.C, USA, Oct 2025
ICMLA, Boca Raton, Florida, USA, Nov 2025
In this work, I present how machine learning models for flight fare prediction can be made both accurate and transparent, leveraging dual explainability through LIME and SHAP.
Springer, Australia, Oct 2024
Springer, Taiwan, Nov 2024
Springer, Taiwan, Nov 2024
ICITS, USA, Jan 2025
ISDFS, USA, Jan 2025
ICMI, USA, Mar 2025
ICOCT, China, Mar 2025
AHTBE, Canada, Jun 2025
ACM, Bangladesh, Nov 2024
USA, Califronia
Bring cloud, edge, and security layers into one fabric so teams ship AI services faster with fewer handoffs.
The deployment of intelligent reinforcement learn- ing (RL) agents on resource-constrained edge devices remains a fundamental challenge due to the substantial memory, computa- tional, and energy requirements of modern deep learning systems. While large language models (LLMs) have emerged as powerful cognitive architectures for decision-making agents, their multi- billion parameter scale confines them to cloud-based deployment, raising concerns about latency, privacy, and connectivity depen- dence. We introduce BitRL, a comprehensive framework for building RL agents using 1-bit quantized language models that enables practical on-device learning and inference under severe resource constraints.
Leveraging the BitNet b1.58 architecture with ternary weights and the bitnet.cpp optimized inference stack, BitRL achieves 10-16× memory reduction and 3- 5× energy efficiency improvements over full-precision baselines while maintaining 85-98% of task performance across diverse benchmarks. We provide rigorous theoretical analysis char- acterizing quantization as structured parameter perturbation, derive convergence bounds for quantized policy gradients, and identify the exploration-stability trade-off inherent to extreme quantization
Support
Talk with our team about custom AI development, LLM solutions, and production delivery.
About Our Service
Fine-tuned language systems built for accuracy and scale.
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Automate workflows with reliable AI and clear ROI.
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Customer and internal Cybersecurity & Ethical Jailbreakings with safe, grounded answers.
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Real results across AI agents, vision, and Agents.
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Meet the team behind production-ready AI systems.
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Connect with sales, tech support and billing.
ContactFAQ
Clear answers to the most common enterprise AI questions.
AI delivers impact when it's embedded in core workflows, runs on your real operational data, and is governed by transparent metrics and SLAs, not just deployed for automation's sake or as experimental pilots. With Invisible's modular platform, you plug in only the pieces you need (data, agents, humans-in-the-loop, evaluations), and drive outcomes you can measure, fast.
Typical barriers include fragmented or unstructured data, challenges with legacy system integration, talent and skills gaps, lack of internal AI expertise, and uncertainty about how to measure ROI for large AI projects.
Begin with a workflow that will drive the most impact and set clear business goals. Track a few simple metrics (time saved, cost reduced, revenue or quality gains) on a monthly/quarterly review, and keep scaling what moves the numbers while fixing or retiring what doesn't, with a named owner to keep it on track.
AI initiatives often stall due to shallow or siloed data, poor alignment with business strategy, insufficient change management, and a lack of clear governance, resulting in high pilot rates but low operational adoption.
The key is to go beyond simply adopting AI "out of the box." You need to plug the model into your own organization's data and workflows, align it with your goals and metrics, and involve internal domain experts so results are relevant at scale.
