Oguzhan Durace

AI Systems Builder & Integrator

AI implementation · product ownership · Berlin

Oguzhan Durace

I turn business processes into working AI systems.

Seven years across business systems, product ownership and delivery. I combine process knowledge with practical AI building: discovering the problem, connecting the tools, testing the workflow and getting people to use it. Experience across Andercore, ubitricity and Accenture.

40%
higher conversion at Andercore
60%
less triage time at Andercore
5
markets under platform ownership
7
years across systems and delivery

Selected work

Business impact. Working integrations. Product ownership.

All workflow case studies →

01 / Enterprise delivery

AI inside the sales and service workflow

40% higher conversion · 60% less triage time

At Andercore, I connected account enrichment and support classification to the tools people already used.

  1. 1Sales or service input
  2. 2Make + AI
  3. 3Salesforce or department routing
  4. 4User feedback

What I owned

Process discovery, stakeholder alignment, integration requirements, UAT and rollout across a five market Salesforce environment.

A decision that mattered

Start with selected categories and user feedback before expanding automation.

Explore the business case →

Reported outcomes from my Andercore role, August 2024 to August 2025.

02 / Deployed independent project

Task requests become validated actions

9 offline checks passed · 11 October 2026

A connected service takes Telegram requests through structured AI plans into Trello and Google Calendar.

  1. 1Telegram
  2. 2AI plan
  3. 3Validation + stored operations
  4. 4Trello + Calendar

What I owned

Workflow rules, integration choices, acceptance and operating controls, with AI supporting code implementation.

A decision that mattered

Clarify incomplete requests and recover previous writes before retrying.

Inspect the workflow →

Interactive walkthrough of tested behaviours. External API calls are mocked in the checks.

03 / Product prototype in testing

League data becomes a publication

60 historical scores reconciled · 19 regression tests

A fantasy football publishing product turns official data into analysis, forecasts and a designed magazine.

  1. 1Official data
  2. 2Validated scores
  3. 3Analysis + editorial review
  4. 4Designed publication

What I owned

Product scope, data quality, editorial rules, acceptance and preparation for reader testing.

A decision that mattered

Validate the numbers before generating the story; freeze forecasts before results.

Explore the product case →

Recorded pipeline verification on 5 October 2026. Latest editorial milestone: a 22 page pregame format.

Business context. Practical implementation.

I connect the business problem to the system that solves it.

My background spans platform ownership at Andercore, a five department transformation at ubitricity and international delivery at Accenture. That experience gives me the process, stakeholder and data context to decide where AI will be useful.

Since December 2025, I have combined family business support with independent AI building. I develop products and integrations using coding agents, while owning the scope, business rules and checks that determine whether the result is ready to use.

01

Business process ownership

At Andercore I aligned sales, customer service, supply, logistics and finance while owning Salesforce across five markets. I connect an individual feature request to the process and data around it.

02

AI building with accountable review

I coordinate Claude and Codex across implementation tasks, connect the tools and check the resulting behaviour. I own requirements, workflow logic and acceptance throughout the build.

03

Commercial decisions from data

My work includes account prioritisation from purchase history and potential value, plus pricing support using inventory, logistics and margin inputs. The output needs to help someone make a decision.

04

Delivery through adoption

User shadowing, phased pilots, acceptance testing, documentation and training are part of my delivery work. A workflow succeeds when the people responsible can use and maintain it.

Experience

Professional Journey

Enterprise delivery, family business support and independent AI building. Expand a role for the scope, tools and outcomes.

How I deliver

Own the outcome from discovery to everyday use.

Business process knowledge, hands on configuration and AI supported implementation work together. I stay accountable for the workflow, its boundaries and whether it helps the team.

01

Watch the work

Map the tasks, handoffs and workarounds with the people doing them. Define the outcome before choosing the tool.

At Andercore, shadowing sales users exposed repetitive account research.

02

Build a bounded workflow

Connect the existing systems. Decide where AI interprets information and where explicit rules or human review control the action.

My task service validates structured plans before permitting external writes.

03

Make failure manageable

Test incomplete inputs, incorrect decisions and interrupted operations. Give the user a clear way to recover.

The integration checks cover missing dates, query immutability and retry recovery.

04

Earn everyday use

Pilot with users, make corrections easy and train the process owner. Review both business outcomes and the effort left behind.

My Salesforce delivery included UAT, localisation, embedded feedback, documentation and training.

A starting point for your team

One useful pilot. A clear decision on what comes next.

An illustrative first month, adapted to your systems, access and priorities. The aim is to establish a useful workflow and the evidence for expanding it.

  1. Discover and prioritise

    Choose one recurring process. Establish volume, manual effort, failure cost and the person accountable for the result.

  2. Build and test with users

    Deliver a narrow workflow with access boundaries, review points and a fallback. Test useful and difficult cases.

  3. Measure and decide

    Review completion time, corrections and continued use. Expand, improve or stop based on what the pilot shows.

More of the work

Explore by the problem you need to solve.

The selected cases above are the starting point. This catalogue covers sales, service, finance, knowledge and data workflows, with a status for each implementation.

Enterprise delivery3 cases

AI workflows and platform transformation at Andercore and ubitricity.

AI lead enrichment & sales preparation

Andercore · 2024 to 2025Enterprise delivery

▸ 40% higher lead conversion

Account research brought into the Salesforce workflow, with user feedback guiding rollout.

for: Sales and revenue operations teams

My ownership: Process discovery, integration requirements, localisation, UAT and rollout.

Account record→LLM enrichment via Make→Salesforce context→Representative review→User feedback
context:work delivered during the stated employment period
▸ problem, architecture, tradeoffs

Delivery and adoption

Shadowed sales users, embedded feedback in the workflow and refined the implementation around their work.

The problem

Sales representatives needed account context to prioritise and prepare their next interaction. Repetitive research interrupted the sales workflow.

Implementation or proposed flow

Shadowed sales representatives to identify the research bottleneck, then connected LLM enrichment with Make and Salesforce. Localised the workflow and embedded a feedback widget so users could inform subsequent changes. Owned requirements, acceptance testing and adoption.

The tradeoffs

  • Research inside the existing CRM, because representatives could use the result in their normal workflow.
  • Iteration with sales users, because local terminology and account context matter as much as the initial model output.

Outcome

40% higher lead conversion at Andercore, with account research integrated into the sales workflow.

What I would watch next

  • Enrichment completeness and incorrect account matches
  • Conversion by segment and use of enriched records
  • Feedback from sales representatives
SalesforceMakeLLM enrichmentUser discoveryUAT

AI support classification & routing

Andercore · 2024 to 2025Enterprise delivery

▸ 60% less triage time

Incoming email classified by category and subcategory, then routed to the responsible department.

for: Customer service and operations teams

My ownership: Routing requirements, category logic, integration coordination and pilot acceptance.

Incoming email→Make→OpenAI classification→Category and subcategory→Department routing
context:work delivered during the stated employment period
▸ problem, architecture, tradeoffs

Delivery and adoption

Started with selected categories and expanded the routing scope through a phased pilot.

The problem

Requests required manual sorting before the responsible team could start resolving them.

Implementation or proposed flow

Connected incoming email, Make and OpenAI to classify requests and route them to the responsible department. Piloted selected categories before expanding. Owned stakeholder alignment, testing, documentation and training.

The tradeoffs

  • A limited category pilot, because routing errors are easier to inspect before the scope expands.
  • Department ownership in the workflow, because a useful classification must lead to a responsible team and a next action.

Outcome

60% less support triage time at Andercore, following a pilot and expansion into additional routing categories.

What I would watch next

  • Incorrect department assignments
  • Time waiting for the first responsible owner
  • New request types outside the initial categories
MakeOpenAIEmail integrationWorkflow routing

Five department platform rebuild

ubitricity · 2023 to 2024Enterprise delivery

▸ Five departments · on time and within budget

A common Salesforce roadmap across engineering, operations and commercial teams.

for: Organisations reconciling processes across departments

My ownership: Roadmap, requirements, migration coordination, phased rollout and training.

Department requirements→Prioritised roadmap→Migration→Phased rollout→Training
context:work delivered during the stated employment period
▸ problem, architecture, tradeoffs

Delivery and adoption

Aligned engineering, operations and commercial teams, with field and office users included in the rollout.

The problem

Five departments had different process and data requirements that needed to work together in one operational platform.

Implementation or proposed flow

Owned the product roadmap, user stories and technical specifications. Coordinated migration and a phased rollout of Sales Cloud, Service Cloud and Sitetracker, with approvals, lead routing, onboarding and training for field and office teams.

The tradeoffs

  • A phased rollout, because migration dependencies and user readiness needed to be coordinated.
  • Shared requirements with explicit priorities, because local process requests still had to fit a coherent data and operating model.

Outcome

Delivered on time and within budget across five departments.

What I would watch next

  • Migration exceptions
  • Adoption across field and office teams
  • Ownership of process changes after rollout
Sales CloudService CloudSitetrackerSalesforce FlowData migration
Independent builds and prototypes11 cases

Working integrations, knowledge tools, a publishing product and further experiments.

AI task intake & calendar integration

Independent projectDeployed project

Natural language requests become validated task and calendar actions, with recovery when an external write fails.

for: Teams coordinating tasks across chat and planning tools

My ownership: Workflow rules, tool integration, operating controls and acceptance, with AI supported implementation.

Inspect tested workflow behaviours →
Telegram request→Structured AI plan→Validation→Stored operations→Trello and Calendar
context:independent implementation with operating controls
▸ problem, architecture, tradeoffs

Delivery and adoption

Deployed for personal coordination. Validation, retries, backups and restore behaviour are part of the operating design.

The problem

Natural language requests need to become consistent actions across multiple tools, including when dates are unclear or a downstream service fails.

Implementation or proposed flow

Built and deployed an independent service using OpenAI structured plans and Pydantic validation. PostgreSQL stores plans and operations. Queries cannot carry write actions, ambiguous dates trigger clarification, and retries reconcile earlier writes. Authenticated intake, health checks, backups and a verified restore support operation. I own workflow rules and acceptance, with coding agents supporting implementation.

The tradeoffs

  • Deterministic calendar routines, because mirroring and date sorting have explicit rules and need no model call.
  • Persisted operations and reconciliation, because a retry must account for an external write that may already have succeeded.

Outcome

Deployed for personal task coordination, with validated actions and tested recovery after failed operations.

What I would watch next

  • Duplicate actions after a retry
  • Requests that require clarification
  • Backup restoration and failed operation recovery
PythonOpenAIPydanticPostgreSQLTelegramTrelloGoogle CalendarDockerHetzner

AI publishing product | Fantasy football

Preparing for reader launchPrototype in testing

Official league data becomes a designed publication, with score validation and editorial review before release.

for: Participants who want useful analysis and a readable story from their league data

My ownership: Product scope, data quality, editorial decisions and release acceptance, with AI supported implementation.

Official league data→Score validation→Forecast comparison→AI assisted editorial→Designed magazine
context:testing before reader launch
▸ problem, architecture, tradeoffs

Delivery and adoption

Latest completed editorial milestone: a 22 page pregame format. Next: reader feedback on usefulness and publication quality.

The problem

Raw scores tell readers what happened, but a useful recurring publication needs reliable numbers, context and an editorial structure.

Implementation or proposed flow

Building a publishing workflow that combines official league results and player data with analysis and a designed magazine. Numerical validation comes before editorial generation. Forecasts are fixed before matches and assessed against outcomes. I own scope, data quality, editorial review and preparation for reader launch, using AI coding agents for implementation.

The tradeoffs

  • Verified numbers before generated prose, because a persuasive narrative cannot compensate for an incorrect score.
  • Forecasts fixed before results, because evaluation is meaningful only when predictions cannot change after the event.

Outcome

The prototype reconciled 60 historical scores and passed 19 regression tests. Current focus: reader testing, editorial usefulness and a repeatable publication workflow.

What I would watch next

  • Score agreement with official results
  • Editorial usefulness in reader testing
  • Publication preparation time
PythonREST APIsSQLiteAI assisted editorialHTMLPDF

MCP knowledge tools for AI delivery

Independent projectIn use

Source retrieval and capture keep research, project decisions and working context available across AI sessions.

for: Builders and operators coordinating work across AI sessions

My ownership: Tool boundaries, retrieval and capture requirements, fixture testing and ongoing use.

Capture→Reviewed knowledge→MCP search→Full source retrieval→Project work
context:used in my own research and project delivery
▸ problem, architecture, tradeoffs

Delivery and adoption

Four focused MCP tools keep source material and working context accessible across AI sessions.

The problem

Research and decisions are easily lost across separate AI conversations, leading to repeated work and inconsistent assumptions.

Implementation or proposed flow

Built a knowledge environment with custom MCP tools for keyword search, full note retrieval, new captures and recent activity. Inputs are validated, article paths are restricted and earlier captures are preserved. Source material stays separate from operating instructions. I use the environment for ongoing research and delivery.

The tradeoffs

  • Small tools with explicit file boundaries, because each operation can be understood and checked.
  • Keyword search and full source reads, because this implementation needs inspectable retrieval without claiming a vector search platform.

Outcome

Used in ongoing research, planning and project delivery. Fixture checks cover retrieval, capture preservation and file access boundaries.

What I would watch next

  • Whether retrieved sources answer the working question
  • Stale or conflicting decisions
  • Captures that have not yet been reviewed
ObsidianPythonFastMCPPydanticMCP

Account research to reviewable outreach

Independent projectWorking prototype

Three AI stages connect account enrichment, segmentation and outreach preparation.

for: Sales teams handling repetitive account preparation

My ownership: Business rules, structured handoffs, integration choices and acceptance.

Account→API enrichment→Segmentation→Outreach draft→Human review
context:prototype with explicit review and validation steps
▸ problem, architecture, tradeoffs

Delivery and adoption

The prototype separates account enrichment, segmentation and drafting. A person reviews outreach before sending.

The problem

Account research, segmentation and outreach drafting are often separate tasks with inconsistent handoffs.

Implementation or proposed flow

Built a CRM prototype where enrichment, segmentation and drafting agents exchange structured outputs. Segmentation uses deal history and engagement; stage outputs and anomaly flags remain inspectable. I own the business rules, integration choices and acceptance. External outreach remains subject to human approval.

The tradeoffs

  • Separate stages, because a failed step can be investigated without rerunning the whole workflow.
  • Human approval before outreach, because a draft needs a commercial owner before it reaches another person.

Outcome

Working prototype with reviewable stages. No production customer adoption or conversion result is attributed to this independent project.

What I would watch next

  • Incorrect account matches
  • Draft quality and required edits
  • Errors at handoff between stages
OpenAIOllamapandasStreamlitREST APIs

Support routing evaluation

Evaluation harnessWorking prototype

Compare rules and semantic classification before deciding which requests should be automated.

for: Teams deciding where automated routing is appropriate

My ownership: Test cases, expected routes, review thresholds and interpretation of errors.

Authored support cases→Rules and semantic routes→Expected versus actual→Error inspection→Review threshold
context:prototype with explicit review and validation steps
▸ problem, architecture, tradeoffs

Delivery and adoption

Authored cases make failure patterns inspectable before applying a routing policy to real traffic.

The problem

A routing workflow needs a way to expose incorrect automatic decisions and the requests it sends for review.

Implementation or proposed flow

Built a harness comparing keyword rules with semantic classification on 20 authored support cases. Expected and actual routes and review thresholds remain visible. Failures can be retained as regression cases.

The tradeoffs

  • Errors alongside automation coverage, because a higher routing rate is not useful if it hides more incorrect assignments.

Outcome

Working evaluation demonstration. Authored test cases do not establish production accuracy.

What I would watch next

  • Wrong automatic decisions
  • Review workload
  • Coverage of new request categories
RulesSemantic classificationEvaluationReview thresholds

Polywhale | Signal analysis prototype

Independent prototype

A data analysis prototype exploring convergence patterns across participant activity.

▸ how it works

A data analysis prototype exploring convergence patterns across participant activity. Built with AI implementation support.

FastAPIReactSupabaseREST APIs

PolyGod | Market analytics prototype

Independent prototype

A prototype exploring scheduled data collection and structured market analysis.

▸ how it works

A prototype exploring scheduled data collection and structured market analysis. Built with AI implementation support.

SupabaseData pipelinesWorkflow automation

Canvas Pro | AI image tools

Independent prototype

A prototype for working with image generation across model providers.

▸ how it works

A prototype for working with image generation across model providers. Built with AI implementation support.

TypeScriptReactSupabaseAI APIs

Draft AI Ace | Draft decision support

Independent prototype

A fantasy sports prototype exploring player recommendations under a draft clock.

▸ how it works

A fantasy sports prototype exploring player recommendations under a draft clock. Built with AI implementation support.

TypeScriptReactSupabaseAI assistance

Real Cost Finder

Independent prototype

A travel comparison prototype separating language based input interpretation from cost calculations.

▸ how it works

A travel comparison prototype separating language based input interpretation from cost calculations. Built with AI implementation support.

PythonFastAPIGemini APIREST APIs

FootballAnalyzer

Independent prototype

A football data analysis project combining API ingestion, transformations and visual exploration.

▸ how it works

A football data analysis project combining API ingestion, transformations and visual exploration. Built with AI implementation support.

pandasNumPyStreamlitAPI Football
Reference designs and next implementations3 cases

Proposed workflows with scope, review boundaries and pilot criteria.

Textile sales inquiry to quotation brief

Proposed family business workflowReference design

A proposed workflow for Dodanlı / Prizma that prepares complete inquiries and keeps price and availability approval with the team.

for: Small textile and carpet operations handling sales inquiries

Email or website inquiry→Required fields→Missing information check→Quotation brief→Team approval
context:proposed flow; pilot outcomes remain to be established
▸ problem, architecture, tradeoffs

The problem

An inquiry may lack dimensions, units, quantity or delivery requirements, leaving the team to collect essentials before preparing a quote.

Implementation or proposed flow

Proposed flow: extract product, dimensions, units, quantity, destination and timing; request missing fields; save the source message and owner in a shared queue; prepare a draft for approval. Related proposals cover approved customer replies, consistent website product information and follow up tracking. Tool choices are proposed, not deployed.

The tradeoffs

  • Complete inquiry data before drafting a quote, because fabric units and carpet dimensions cannot be guessed safely.
  • Human price and availability approval, because general product information does not establish stock or a delivery commitment.

Outcome

Ready for a scoped pilot with measures for inquiry completeness, preparation time and reviewer corrections.

What I would watch next

  • Inquiry completeness
  • Quotation preparation time
  • Overdue follow ups
MakeStructured AI extractionShared inquiry trackingHuman review

Proactive spend intelligence

Architecture proposalReference design

A reference design for surfacing actionable spending exceptions from consistent financial data.

for: Finance teams evaluating AI assistance

Financial event→Verified data→Scoped analysis→Evidence→Review queue
context:proposed flow; pilot outcomes remain to be established
▸ problem, architecture, tradeoffs

The problem

A financial assistant needs to make exceptions visible without producing inconsistent calculations or acting beyond its authority.

Implementation or proposed flow

Design a shared calculation and data layer for scoped finance, accounting and policy assistants. Surface the evidence behind an exception and send consequential actions to a responsible reviewer. The proposal defines pilot criteria for data quality, useful exceptions and reviewer effort.

The tradeoffs

  • A shared calculation layer, because financial totals need consistent definitions across assistants.
  • Reviewed actions, because an explanation and an instruction to change a financial record carry different consequences.

Outcome

Reference design. A pilot would need to demonstrate correct exception detection and useful reviewer decisions.

What I would watch next

  • False alerts
  • Evidence completeness
  • Time to a reviewed decision
Scoped agentsMCPVerified calculationsHuman review

Invoice preparation & exception review

Architecture proposalReference design

A reference design that separates extraction from matching and approval.

for: Finance operations teams processing supplier invoices

Invoice→Extracted fields→Order and receipt match→Exception reason→Review
context:proposed flow; pilot outcomes remain to be established
▸ problem, architecture, tradeoffs

The problem

Invoice handling mixes repeatable extraction with decisions that need purchasing context and accountable approval.

Implementation or proposed flow

Propose structured extraction, deterministic matching against order and receipt data, and an exception queue with source evidence. Partial deliveries and missing references remain visible to a reviewer. Payment execution is outside this proposed workflow.

The tradeoffs

  • Deterministic matching, because quantity and value comparisons must follow explicit rules.
  • Diagnosed exceptions, because the reviewer needs the mismatch and evidence, rather than only a confidence score.

Outcome

Pilot success would be measured through matching accuracy, reviewer effort and the handling of incomplete documents.

What I would watch next

  • Extraction errors
  • Incorrect matches
  • Reviewer correction time
Document extractionMatching rulesReview queuesERP integration design
Explore the portfolio through the command console
oguzhan.osv2.0
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Technical Skills

The Working Set

I own process design, integration choices and delivery. Claude and Codex support implementation across the programming stack below.

AI tools & orchestration

ClaudeCodexOpenAI APIOllamaMCPWorkflow evaluation

Business systems & integration

Salesforce Sales Cloud, Service Cloud & FlowMakeZapiern8nREST APIs & webhooksSitetrackerAdobe Experience Manager

Product & delivery ownership

Process discoveryStakeholder managementCommercial analysisRoadmaps & requirementsAcceptance testingRollout & adoption

Stack managed with AI support

PythonTypeScript / JavaScriptSQL & PostgreSQLFastAPIReact / Next.jsSupabasepandas / StreamlitPydanticDocker / Hetzner

Education & Credentials

Academic Background

Master of Science in Management

2017 to 2019
ESMT Berlin
Berlin, Germany
  • Quantitative Business Track
  • Specialization: Business Analytics
  • Partial Merit Scholarship Recipient

Bachelor of Business Administration

2011 to 2016
Koc University
Istanbul, Turkey

Certifications

✓Adobe Certified Professional: Adobe Analytics Business Practitioner
✓Adobe Qualified: Adobe Experience Platform

Languages

TurkishNative
EnglishC1
GermanB1

Contact

Let’s build the next useful AI system.

I am interested in AI product ownership, implementation, adoption, automation and solutions roles in Berlin or remote within Germany. If you need someone who connects business processes, technical delivery and the people using the result, let’s talk.

Download my CV ↓

Berlin, Germany