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AI Engineering Pod

AI engineering capacity,
where you need it.

A three-person, AI-native engineering pod. We help software companies, AI consultancies and delivery teams ship RAG systems, AI agents, data pipelines, evaluations and full-stack integrations — inside your workflow and, when needed, under your brand.

Get in Touch

Five Things
We Deliver Well

A deliberately focused catalogue. Every service has a defined scope and concrete deliverables.

RAG

RAG & Knowledge Systems

Grounded assistants that retrieve the right evidence, cite their sources and hold up under evaluation.

  • Knowledge bases & multi-document research
  • Multimodal PDF, table and image retrieval
  • Turkish & English RAG
  • Retrieval, reranking & evaluation pipelines

You receive: working API or integrated feature, evaluation set and report, source traceability, tests, deployment setup and technical handoff.

AI Agents

AI Agents & Workflow Automation

Turn a defined, repetitive workflow into a tool-using system with clear guardrails and human review where it matters.

  • Document-processing agents
  • Research & monitoring workflows
  • Automated reporting
  • Multi-agent orchestration

You receive: workflow architecture, implemented agents and tools, audit logs, failure handling, tests and deployment documentation.

Data Pipeline

Data & AI Pipelines

Reliable data flows for AI features, analytics and operational products.

  • API & document ingestion
  • ETL/ELT & scheduled workers
  • Cleaning & validation
  • SQL optimization & model-ready datasets

You receive: reproducible pipeline, schemas and data contracts, validation tests, monitoring hooks and an operating runbook.

Evaluation

AI Evaluation & Reliability

Replace demo confidence with measurable evidence before an AI feature reaches production.

  • LLM, embedding & reranker benchmarks
  • Task-specific evaluation datasets
  • Prompt, model & pipeline comparison
  • Regression, cost & latency analysis

You receive: reusable evaluation harness, metrics report, failure taxonomy, recommendation and acceptance thresholds.

Full-Stack Integration

Full-Stack AI Integration

Move AI out of an isolated notebook and into the product and workflow your users already rely on.

  • FastAPI / ASP.NET Core services
  • React, Next.js, Blazor or Electron UIs
  • Vector DB & SQL integration
  • Docker, cloud or on-prem delivery

You receive: integrated code in your repository, API docs, tests, migration or setup scripts, deployment guide and handoff.

For teams that want
engineering support

The situation

Delivery outgrows your teamA client delivery is growing faster than your internal capacity.
A module nobody can prioritizeYour backlog contains an AI, RAG, data or integration module that keeps slipping.
A prototype that needs hardeningA promising prototype needs evaluation, hardening and integration into the product.
Specialists without a hireYou need RAG or agentic expertise without adding a permanent headcount.
Quiet, embedded capacityYou want a small pod that works quietly inside your repository and process.

How we answer

We join your repository, sprint and review process.
One bounded item, with deliverables and a handoff date agreed first.
We measure the prototype first, then harden it and integrate it into the product.
RAG and agent work from a three-person pod, without a permanent hire.
We work inside your workflow, and under your brand when the contract says so.

What we won't promise: guaranteed accuracy or ROI, or a production date before we have seen your data and codebase. AI systems have variables nobody controls — we measure them instead.

Three Ways
to Engage

Pick the model that matches how you already deliver. Your team keeps ownership of the roadmap and the client.

A

Fixed-Scope Delivery

Best for a clearly bounded backlog item or pilot.

We agree on deliverables, milestones, acceptance criteria, dependencies and a handoff date before development begins.

B

Embedded Overflow Pod

Best for teams that need temporary capacity across several backlog items.

We join your repository, sprint cadence, communication channel and review process while your team keeps roadmap ownership.

C

White-Label Delivery

Best for consultancies and agencies serving their own clients.

We deliver under your process and brand, protect your client relationship, follow agreed communication boundaries and transfer project IP as defined in the contract.

Our process

From the first call
to handoff

Each step has a defined output, from the scope note to the handoff package.

  1. 01

    Scope

    A 30-minute technical call about the backlog item, codebase, data, constraints, success criteria and access needs.

    Output Scope note, assumptions, risks, fit decision

  2. 02

    Plan

    We split the work into milestones, define interfaces and acceptance checks, and agree on communication and repository access.

    Output Statement of work, plan, architecture sketch, estimate

  3. 03

    Build

    Development in your repository or an agreed private workspace, with short demos and reviewable increments.

    Output Working increments, tests, progress notes

  4. 04

    Validate

    Functional tests and, for AI components, quality, known failure modes, latency and cost against agreed thresholds.

    Output Test & evaluation results, acceptance checklist

  5. 05

    Handoff

    Source code, deployment assets, documentation, knowledge transfer and an agreed post-delivery bug-fix window.

    Output Complete handoff package

Boundaries

  • NDA before sensitive briefs or client data
  • Client non-solicitation and agreed communication boundaries
  • IP transfer as defined in the contract
  • Least-privilege access and secret-management rules
  • Your data is never used to train unrelated models
Recommended first engagement

RAG Evaluation & Improvement Sprint

Turn an existing RAG prototype into an evidence-backed improvement plan.

In a focused sprint we inspect your current pipeline, build or refine a representative evaluation set, benchmark retrieval and answer quality, identify failure patterns, test targeted improvements and deliver a prioritized implementation plan.

Scope This Sprint

What you provide

  • Repository or API access
  • Sample documents
  • 20–100 representative questions
  • Known pain points

What you get

  • Baseline measurement
  • Retrieval & generation metrics
  • Failure taxonomy
  • 2–4 controlled improvement experiments
  • Cost & latency comparison
  • Recommendation report

Out of scope

  • Unlimited data labeling
  • Production migration
  • SLAs & continuous monitoring

These can follow as separate phases.

Selected
Engineering Work

Research and product work by our team — not client case studies. Every result below can be checked independently.

Multi-Agent RAG Published Research

SPD-RAG

Sub-Agent Per Document Retrieval-Augmented Generation

SPD-RAG 58.1
Standard RAG 33.0

API cost: 38% of the full-context baseline

Challenge
Standard RAG can miss evidence scattered across many long documents.
What was built
A hierarchical multi-agent architecture that assigns a dedicated agent to each document and combines their findings through bounded recursive synthesis.
Result
58.1 average score on LOONG versus 33.0 for standard RAG, at 38% of the full-context baseline's API cost.
AI Agents RAG FinTech

AIris

Agentic Financial Intelligence Platform

TEKNOFEST 2025 · 3rd place in Türkiye

Challenge
Financial analysis means combining documents, live market data, news and calculations into one traceable report.
What was built
Multi-source ingestion, agentic orchestration, multi-tier and multimodal RAG, data and visualization tools, automated reporting and a full-stack product layer.
Result
Third place nationally in the TEKNOFEST 2025 Financial Technologies Competition.

Research

arXiv 2603.08329

SPD-RAG

Sub-agent-per-document RAG: 58.1 vs. 33.0 for standard RAG on LOONG, at 38% of the full-context API cost.

 arXiv 2511.01386

RAGSmith

End-to-end architecture search over 46,080 feasible RAG pipeline configurations across nine technique families.

 EACL 2026 · SIGTURK

RAGTurk

Best practices for Turkish RAG, benchmarking seven stages of the pipeline.

A Compact Pod With
Research Depth

Yağız Can Akay

Yağız Can Akay

AI Systems & Product Engineer

Agentic systems, advanced and multimodal RAG, local/on-prem AI, model experimentation, applied ML and technical product architecture.

CV
Muhammed Yusuf Kartal

Muhammed Yusuf Kartal

AI Research & Evaluation Engineer

RAG design and optimization, retrieval and reranking, model evaluation and benchmarking, agent workflows and data preparation.

CV
Arda Akpınar

Arda Akpınar

Full-Stack & Integration Engineer

.NET and FastAPI backends, React / Next.js / Blazor / Electron interfaces, SQL and data layers, enterprise integrations and deployment.

CV

Send Us One
Backlog Item

Tell us what you want to work on. We will reply with an honest fit decision and, if it makes sense, a short scope note.

  • 30-minute technical call — no slide deck
  • NDA before any sensitive details or data
  • Reply within 2 business days

Based in Ankara, Türkiye · Working remotely

Project Brief ~2 minutes