Multi-Agent Systems
Pipelines of specialised agents, each with a contract, a budget and a trace.
14LabsAI Engineering and Research Lab
From AI Researchto Production Systems
14Labs builds reliable AI solutions by combining research, engineering, and evaluation. We create intelligent agents, retrieval systems, and machine learning products designed for real-world users.
Why 14Labs
We don't just build AI systems. We measure them. Research guides our decisions. Evaluation validates our systems. Engineering turns ideas into reliable products that perform in the real world. That is the difference between a demo and a product.
We Measure Quality
We test the system regularly to understand what is improving and what needs attention.
We Understand Failures
We track how the system works so problems can be identified and fixed quickly.
We Control Performance and Cost
We monitor speed and usage costs to keep the system reliable and practical.
Retrieval & Knowledge Systems
The right context in front of the model — measured, not guessed.
Applied Machine Learning
Models chosen for the decision they support, not for the hype cycle.
Evaluation & Reliability
Golden sets, regression gates and tracing, so every change is measurable.
Research notes
What we measured, what failed and what we would do again.
15+
Projects Delivered
In production, not pilots
10+
Client Teams
Across four industries
4+
Years Building
Since before the agent boom
95%
Satisfaction
Measured post-engagement
Building an AI prototype is only the beginning. The real challenges appear when systems need to become reliable, scalable, and ready for real users.
- 01 / 04Production
The Demo Works. The System Doesn't.
Moving from a controlled prototype to a real product requires handling unpredictable users, data, and scale. Most pilots die between the notebook and the first real user.
See our approach ›Median against tail latency, once the traffic is real.
- 02 / 04Evaluation
You Can't Improve What You Can't Measure.
AI systems can change from one release to the next without anyone knowing what actually improved. Good evaluation makes those changes measurable.
How we approach it ›Per-suite change across one release. The blend moved +0.01.
- 03 / 04Retrieval
The Knowledge Is There. The Model Can't Reach It.
Your knowledge may live across documents, databases, wikis, and internal tools. The challenge is finding the right information when the user needs it.
Our approach ›Top-k retrieval for one query. The answer ranked twelfth.
- 04 / 04Multi-Agent Systems
Adding More Agents Doesn't Always Help.
Complex workflows can become difficult to debug, trace, and control. The first step is understanding what each agent is doing and why.
Explore the practice ›One request on a clock. Four of twelve seconds were retries.
Research
We research, experiment, and measure before committing to a production build. What we learn helps us understand what works, what doesn't, and what is worth building.
Our research explores applied machine learning across different domains. The methods we develop inform how we approach real-world AI systems.
- 5
- Papers Published
- 3
- Under Review
- 4
- In Progress
- Aug 2026
- Latest Paper
Latest Papers
All 5 PapersResults in EngineeringAug 2026
Solar Irradiance Forecasting Meta-review: An In-Depth Systematic Meta-review on Solar Irradiance Forecasting Datasets, Predictive Pipelines and Performance
Faiza Mehmood, Muhammad Nabeel Asim, Aamir Mehmood, Waqar Mahmood, Sebastian Vollmer, Mehmood Ul Haq, Faisal Ali, Andreas Dengel
Progress in Additive ManufacturingMar 2026
Machine learning and physics based empirical equations for fatigue life prediction of AM Ti-6Al-4 V
Muhammad Muteeb Butt, Mehmood ul Haq, Varma KVVSSN, Hossein Laieghi, Hakan Yavas, Metin U Salamci, Hüseyin Kızıl
International Journal of Precision Engineering and ManufacturingOct 2025
Machine Learning Driven Prediction and GUI Based Optimization of Quasi-Static Mechanical Properties in SLM Fabricated Ti6Al4V Alloy
Muhammad Muteeb Butt, Sidra Rashid, Mehmood ul Haq, Ayyaz Mustafa, Arshad Iqbal, Hossein Laieghi, Varma KVVSSN, Metin U. Salamci, Enrico Salvati, Hüseyin Kızıl
Real Estate
AI Find Sites Real Estate Solution
AI-Driven Commercial Real Estate Site Discovery - Automating hours of manual research into minutes of intelligent analysis
- Timeline
- 1 Month
- Engineers
- 3
- Pipeline Nodes
- 11
EdTech
AI-Powered Admission Counselor
Transforming University Admissions with Multi-Agent Intelligence
- Timeline
- 3 months
- Team Size
- 4 engineers
- Agents
- 7 AI
Clear principles. Practical engineering. No unnecessary complexity.
We Measure What We Build
We define how success will be measured from the start, so we can see what is improving and what needs to change.
We Start With What Works
We use the simplest approach that can solve the problem well. We add complexity only when it brings a clear improvement.
You Own What We Build
The code, infrastructure, evaluation, and documentation stay with you. We don't build systems that depend on us to keep working.
Tell Us What Isn't Working Yet
Send the shape of the problem, the data you have and what a good outcome would look like. We will reply with an honest read on whether it is worth building — including when it is not.