Your Data Pipelines Deserve Better.
So Does Your ML.
produckAI helps growth-stage and enterprise teams fix unreliable data pipelines, ship machine learning that actually moves conversion and retention, and cut cloud compute costs. We deliver production-grade data engineering and applied ML consulting, with measurable outcomes.
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
Big Data & Data Platform
Reliable pipelines, cost control, measurable SLAs
Applied Machine Learning
Revenue-tied ML that moves KPIs, not theater
TÜBİTAK & R&D Consulting
Funding-ready engineering documentation
Inference Optimization
Reduce GPU bills, increase throughput
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, outcome-driven engagements — not open-ended retainers. Pick a package, get measurable results.
Data Pipeline Rescue
"Your dashboards shouldn't lie. Your pipelines shouldn't break at 3 AM."
We audit your batch and streaming pipelines end-to-end, identify and fix the top 3 cost/performance bottlenecks (e.g., Spark shuffle, skew, checkpointing), and implement robust data quality checks with operational runbooks. Outcomes: trustworthy, fast data, reduced cloud spend.
- • End-to-end pipeline & cost audit
- • Top 3 bottleneck fixes (Spark, EMR)
- • Data quality checks & runbooks
ML Growth Sprint
"A measurable uplift, not 'ML theater'."
We pick one KPI (CTR, conversion, or retention), build a baseline model, improve it, and deliver a concrete rollout plan with A/B test design.
- • Single-KPI focus (CTR / conversion / retention)
- • Baseline → improvement → rollout plan
- • Offline evaluation & online A/B design
TÜBİTAK Technical Backbone
"Funding-ready engineering documentation."
We create auditable R&D work packages, milestones, evidence plans, and a repository/experiment structure that satisfies TÜBİTAK requirements.
- • R&D work packages & milestones
- • Evidence plan & experiment structure
- • Risk register aligned with engineering
Inference Cost & Latency Audit
"Reduce GPU bill or increase throughput."
End-to-end benchmarking of your inference stack, bottleneck analysis, and a prioritized list of quick wins to cut costs or boost performance.
- • Inference benchmarks & profiling
- • Bottleneck analysis
- • Quick-win optimization roadmap
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.
Ready to fix your pipelines?
Let's scope your engagement and deliver measurable results.
About produckAI
Production ML and data systems experience from enterprise environments — delivered as focused consulting.
Built in Production, Not in Slide Decks
Before produckAI, we built the systems that power real products — production recommendation engines, large-scale data platforms processing millions of events, and ML pipelines that moved actual business metrics. Our experience taught us what separates consulting theater from engineering that ships.
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.
Track Record
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
Ready to fix your pipelines?
Let's scope your engagement and deliver measurable results.
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.
Let's Scope Your Engagement
Tell us what's broken — a failing pipeline, an ML model that isn't moving metrics, a grant deadline. We'll come back with a scoped proposal, timeline, and fixed price.
Fast Response
We typically respond within 24 hours with a preliminary assessment.
NDA-Ready
We sign NDAs before any technical discussion. Your data stays yours.
Fixed-Price Proposal
No hourly billing surprises. You'll know the scope and cost upfront.