LLM Inference Performance Engineering.
Measured. Explained. Optimized.
We measure, explain, and optimize production LLM serving systems across throughput, latency, scheduling, GPU utilization, and cost. Reproducible performance engineering for vLLM and production inference workloads.
Built in Production, Not in Slide Decks
We've delivered production ML and data systems across enterprise environments. Our approach is factual, non-hype, and outcome-based: money saved, KPIs moved, grants obtained. Every engagement starts with a clear scope and measurable deliverables. We don't do open-ended retainers or "ML theater" — we ship production-grade solutions that demonstrate real impact on your business metrics.
What We Do
Outcome-driven consulting across data, ML, and R&D engineering
LLM Inference Performance
Controlled benchmarking, GPU profiling, scheduler analysis, and bottleneck attribution
Graph Engineering
Graph data systems, algorithms, analytics, and machine learning over connected data
Data Engineering
Reliable pipelines, scalable data platforms, and measurable performance
Applied Machine Learning
Models tied to revenue — conversion, retention, personalization
How It Works
Three simple steps from discovery to production handoff
Discovery Call
We understand your pipeline issues, ML goals, or R&D needs in a 30-minute call.
Scoped Engagement
We deliver a fixed-scope proposal with timeline, deliverables, and pricing.
Production Handoff
You receive production-ready code, documentation, and a complete handover.
Real Results, Real Impact
Anonymous case studies from recent engagements
40% Cost Reduction
Fixed Spark shuffle bottlenecks and optimized EMR configuration, reducing monthly cloud spend from $45K to $27K while improving pipeline reliability.
3.2x Conversion Uplift
Built recommendation system with proper A/B testing framework, increasing conversion rate from 2.1% to 6.7% with statistical significance.
TÜBİTAK Grant Approved
Structured existing ML research into auditable work packages with evidence plans, resulting in successful TÜBİTAK grant application.
Engagement Packages
Scoped engineering engagements with explicit baselines, measurable outcomes, and production-ready deliverables.
LLM Inference Performance Engineering
"Measure the serving system before changing it."
We benchmark and profile production LLM serving stacks to identify where throughput, TTFT/TPOT, GPU utilization, scheduling behavior, and serving cost are being lost. Findings are tied to reproducible evidence and prioritized engineering actions.
- • Controlled serving benchmarks and workload characterization
- • GPU profiling, scheduler analysis, and bottleneck attribution
- • Measured optimization plan with before/after validation
Data Engineering
"Reliable data systems under production constraints."
We design, repair, and optimize batch and streaming data systems with emphasis on reliability, performance, observability, and cloud efficiency.
- • Data architecture and pipeline analysis
- • Spark / EMR performance and cost engineering
- • Data quality, observability, and operational handoff
Applied Machine Learning
"Models should be evaluated against the metric they are meant to move."
We build and evaluate production ML systems around explicit objectives — recommendation, personalization, segmentation, and experimentation — with measurable offline and online evaluation.
- • Recommendation, personalization, and segmentation systems
- • Baselines, evaluation methodology, and experiment design
- • Production integration, monitoring, and handoff
R&D Engineering & Evidence
"Make technical work auditable and reviewable."
We structure legitimate engineering and research work into measurable work packages, technical evidence, experiment plans, milestones, and review-ready documentation.
- • Technical work packages and measurable milestones
- • Experiment, repository, and evidence structure
- • Technical narratives aligned with engineering reality
Core Competencies
Deep expertise in the technologies that power modern data-driven businesses
Big Data & Data Platform
Reliable batch and near-real-time pipelines with cost control and measurable SLAs. We make your data correct, fast, and available for ML and reporting.
Applied Machine Learning
Increase conversion, retention, and revenue with production ML — recommendation systems, customer segmentation, uplift logic, and proper A/B evaluation.
TÜBİTAK & R&D Incentives
Turn "we do R&D" into auditable, fundable, reportable engineering. We build measurable work packages, technical narratives, and evidence plans that match engineering reality.
Inference Optimization
Benchmark your inference stack, identify bottlenecks, and apply quick wins — reduce your GPU bill or increase throughput without sacrificing accuracy.
Need to understand where your inference stack is losing performance?
Bring us the workload, metrics, and constraints. We’ll baseline the system, isolate bottlenecks, and define the next engineering step.
About produckAI
Production ML and data systems experience from enterprise environments — delivered as focused consulting.
A Systems Background Behind the Inference Work
Inference performance rarely lives in isolation. Scheduler behavior, workload shape, data movement, distributed execution, and production constraints all interact. ProduckAI approaches inference as a systems engineering problem.
The same discipline carries across our work: measure first, isolate bottlenecks, make changes that survive production, and keep the evidence reproducible.
Production Systems
Large-scale data platforms, Spark/EMR pipelines, reliability, and cloud cost engineering.
Graph Engineering
Graph data systems, algorithms, analytics, and machine learning over connected data.
Applied Machine Learning
Recommendation, personalization, segmentation, experimentation, and production ML.
Performance Engineering
Distributed compute, profiling, bottleneck analysis, and production efficiency.
Why Work With Us
Outcome-Based Delivery
Every engagement has a clear scope, timeline, and measurable deliverables. You pay for results, not time.
Production-Grade Quality
Not prototypes — production systems with monitoring, runbooks, CI/CD, and proper failure handling.
Revenue-Tied ML
ML that moves real KPIs — conversion, retention, revenue. With proper A/B evaluation and guardrails.
Cost-Conscious Engineering
We optimize cloud costs while improving performance. No over-provisioning, no wasted compute.
Verified References
Proven track record with production systems and enterprise clients. Professional references available upon request.
R&D Incentive Expertise
We turn your R&D into auditable, fundable documentation that satisfies TÜBİTAK and other incentive programs.
Frequently Asked Questions
Need to understand where your inference stack is losing performance?
Bring us the workload, metrics, and constraints. We’ll baseline the system, isolate bottlenecks, and define the next engineering step.
Case Studies
Real engagements. Real constraints. Measurable outcomes.
Rescuing a Government-Funded Graph ML Project and Shipping It to Production
B2B MarTech / Customer Data Platform — Turkey
A product team was in the middle of a government-funded R&D project when delivery risk became visible: architectural decisions were drifting, technical debt was accumulating, and stakeholders needed clear evidence of progress. Our team was engaged to stabilize execution, align the technical direction, and ensure the program could be defended in formal reviews — without compromising production readiness.
We took end-to-end ownership across data, modeling, and cloud execution. We built a robust graph machine learning pipeline using Neo4j, Apache Spark / Spark ML, and AWS EMR. This included Spark-based preprocessing and feature engineering, consistent dataset construction, and model training/evaluation workflows. We also corrected high-impact technical decisions, addressed design choices that could weaken scientific or audit defensibility, and translated engineering work into clean, review-ready narratives.
A critical part of the engagement was stakeholder management under scrutiny. We led the communication loop: agenda setting, review preparation, technical presentations, and Q&A handling. Across three formal presentations, we clarified methodology, defended technical rationale, and communicated results in a way that matched evaluation expectations.
Outcome
- Delivered the full R&D scope on time under review pressure
- Successfully passed multiple formal review/presentation checkpoints
- Converted research-grade work into a production-ready pipeline, enabling productization
- Improved cloud cost discipline through targeted compute/storage optimization
4-Day Delivery: Fine-Tuning an LLM to Meet Grant Requirements and Pass Evaluation
AI Product Company — Turkey
A company faced a hard deadline — four days remaining — to qualify for a major government incentive program worth 35M TRY. They needed a defensible deliverable: a full fine-tuned LLM training workflow, validated to outperform the base model on agreed evaluation criteria, with correct packaging and reporting aligned to program guidelines. Our team was engaged as the last-mile delivery owner to execute end-to-end under extreme time pressure.
We designed and ran a full fine-tuning pipeline (not parameter-efficient tuning) with emphasis on measurable performance gains and reproducibility. This included dataset readiness checks, training configuration, evaluation methodology, and a clean comparison against the base model. We then prepared the complete submission artifacts: model export in the expected format, documentation, and a structured report mapping work directly to the program's requirements — ensuring it was technically correct and auditor-friendly.
Outcome
- Delivered a complete full fine-tuning + evaluation package within the four-day window
- Submitted model and documentation aligned with program requirements
- Achieved validated performance improvement over the base model per the evaluation plan
- The company passed the stage successfully, preserving eligibility for the incentive
Have a similar challenge? Let's discuss how we can help.
Discuss Your LLM Inference System
Throughput regression, TTFT/TPOT, GPU utilization, serving cost, scheduler behavior, or vLLM/SGLang behavior — send the workload, metrics, and constraints. For broader AI systems work, describe the system and the engineering problem.
Technical Triage
We start with the workload, metrics, constraints, and current evidence — not a generic sales call.
NDA-Ready
We can work under NDA before reviewing non-public system details.
Scope Before Implementation
We define the baseline, questions to answer, and deliverables before proposing engineering work.