Bastian Kuberek

What I do

What I build

Where I take AI systems from prototype to production — and own the whole thing: the pipeline, the product, and the infrastructure underneath.

01

From Prototype to Production

I take a working prototype and make it a production system — real load, real data, uptime, cost caps, and the unglamorous parts that make it real.

The service

Most AI never ships. Industry surveys put pilot-to-production failure around 88% for agent projects, and up to half of GenAI proofs-of-concept get abandoned — not because the demo didn't work, but because nobody closed the gap between a notebook that runs once and a system that runs every time, under real load, on real data, at a cost the business can sustain. Closing that gap is most of the work.

~88% of agent pilots never reach production; up to 50% of GenAI PoCs are abandoned before they ship.

Who it's for

Companies whose AI is stuck in the pilot stage, and founders who need one senior engineer to just build the whole thing.

What's in scope

  • End-to-end build: data pipeline → API → UI → cloud infrastructure
  • The essentials that make it real: auth, billing, admin tooling, monitoring, CI/CD
  • Incident response, postmortems, and production hardening
  • Clean documentation and handoff

What good looks like

  1. Why pilots die — the execution gap

    A prototype proves the idea; production proves the discipline. Most failures are organizational and data-quality problems long before they're model problems. I go in expecting the real blockers — messy inputs, no evaluation, no cost ceiling, no clear owner — and close them first.

  2. Reliability, idempotency & self-healing

    Real systems re-run, retry, and recover. I make writes idempotent so re-runs skip already-finished work, and jobs self-healing so a stuck run recovers instead of blocking users. That's the line between a demo and something you can leave running.

  3. The data pipeline underneath

    Most of the work is upstream of the model. Ingesting messy real-world data at scale — change detection so you don't reprocess what hasn't changed, dedup, content-addressed storage — is what makes the AI layer possible at all.

  4. Eval before you ship

    You can't harden what you can't measure. I put an evaluation loop in front of the release so quality and cost are known quantities before anything reaches a user — not a surprise afterward.

  5. Cost as a first-class constraint

    Token spend is a top reason PoCs die on contact with production. I budget it from the start — caps, caching, model routing — so spend is a known quantity before launch, not a bill you discover after.

  6. Deploy, operate, and own the incident

    Infrastructure-as-code, CI/CD, monitoring, and the boring essentials so the thing can be operated, not just launched. I tend to be the one who root-causes the incident, writes the postmortem, and folds the fix back in. Shipping is the start of the loop, not the end.

How I approach the hard parts

I take end-to-end ownership — pipeline → API → UI → infrastructure, plus the auth, billing, monitoring, and CI that make it real. Systems I've built this way are still running: pipelines that ingest on a schedule without anyone watching, and services other systems depend on. The pattern is the same every time: find the real blockers, make it reliable and affordable, document it, and hand off something that runs.

Rough idea to running system — the whole path, owned end-to-end.

02

Agentic AI Platforms & Workflows

Agent systems a team can configure, evaluate, and rely on in production — not demos that stop working after the pilot.

The service

A demo that calls a tool once is nothing like a system a team can configure, evaluate, and run in production — with memory, orchestration, human review, and evaluation around it. That difference is where most agentic projects stall, and it's the part I build.

Analysts forecast ~40% of enterprise apps will embed agents by end-2026, though many agentic projects are expected to be cancelled along the way.

Who it's for

Teams building agent or copilot systems, and orgs with a promising AI prototype that needs to become a real product.

What's in scope

  • Agent and prompt authoring, versioning, and orchestration (LangChain / LangGraph)
  • Human-in-the-loop review and multi-agent flows
  • Evaluation harnesses against curated datasets to tune quality vs. cost
  • Deployment into product surfaces and programmatic feeds

What good looks like

  1. Agent design & tool/function calling

    The hard part isn't calling a tool, it's designing tools an agent can use reliably — tight contracts, predictable failure, the right granularity — so the agent's behavior is legible instead of a black box.

  2. Multi-step orchestration & state

    Real workflows are multi-step and stateful. Orchestrating them (I build on LangChain / LangGraph) means managing memory, branching, and partial failure so one bad step doesn't collapse the whole run.

  3. Prompt & version management

    Prompts are production artifacts. Teams need to author them, version them, and roll them back — and see exactly what changed — the same way they manage code.

  4. Human-in-the-loop review

    What earns trust is a review surface: the people who own the domain see, diff, and approve what the AI proposes before it takes effect. I've built exactly this — a line-by-line accept/reject flow for AI-proposed changes.

  5. Agent evaluation

    You tune quality against cost by evaluating agents on curated datasets — ratings, notes, side-by-side input and output — not by eyeballing the happy-path demo.

  6. Guardrails, cost & latency

    The production constraints — safe failure, token budgets, latency — decide whether the agent is even affordable to run at scale. They're design inputs, not afterthoughts.

How I approach the hard parts

I've built internal platforms teams use to author, version, and evaluate agents and prompts, and customer-facing agent pipelines with self-healing resilience so stuck runs recover instead of blocking anyone. I design agents to be configurable and reviewable by the people who own the domain, not just by the engineers who built them.

I build the platforms teams use to author, evaluate, and run agents in production.

03

LLM Evaluation, Guardrails & Cost Control

The evaluation, guardrails, and cost control that make an AI system measurable, safe, and cheap enough to ship — and keep it that way in production.

The service

The most-cited blocker to production is the evaluation gap: teams can't measure whether the output is good, can't guarantee it fails safely, and can't predict what it costs. It's the unglamorous discipline that decides whether an AI system ships at all — measurement, safe failure, and a hard cost ceiling.

The evaluation gap is the most-cited blocker to production (~64% of leaders); most teams say they lack the tooling to monitor agents in production.

Who it's for

Teams whose AI works in the demo but can't yet be trusted, governed, or afforded at scale — the number-one reason pilots stall.

What's in scope

  • Evaluation datasets and review workflows (ratings, notes, labels), tuning quality vs. cost
  • Guardrails and safe-failure design; observability and tracing (LangSmith, Grafana)
  • Token-cost control — budget caps, caching, model routing and fallback, spend observability
  • Deprecated-model migration, incident response, and postmortems

What good looks like

  1. Eval datasets & metrics

    You can't improve what you don't measure. It starts with curated evaluation datasets and metrics that actually track the outcome you care about — not a proxy that looks good on a slide.

  2. Offline vs. online eval & LLM-as-judge

    Offline eval catches regressions before release; online eval watches real traffic. LLM-as-judge scales human judgment — carefully, because the judge itself needs evaluating before you trust it.

  3. Regression testing

    Prompt and model changes break things silently. A regression suite turns 'it feels worse' into a number you can gate a release on, so a quiet quality drop can't ship unnoticed.

  4. Guardrails & safe failure

    The system has to fail closed, not fail wrong. I design guardrails so the bad path produces nothing rather than a confident mistake — withheld output is an inconvenience; a wrong answer shipped to a user is a liability.

  5. Observability & tracing

    You can't debug what you can't see. Tracing every step (LangSmith, Grafana) is how you find the one stage that's burning tokens or quietly dropping quality.

  6. Cost control

    Caching, model routing, token budgets, retry and thinking-budget tuning — plus spend observability, so cost per output is a number you watch, not a bill you discover. Cost gets engineered like latency: continuously, with a hard ceiling.

How I approach the hard parts

I treat eval, safety, and cost as one discipline. I build the evaluation datasets and human-review loops, wire up tracing, design for safe failure, and put hard ceilings on spend. When something goes wrong, I tend to be the one who root-causes it and writes the postmortem that keeps it from recurring.

Eval, guardrails, and a hard cost ceiling — treated as one discipline, in production.

04

RAG, Knowledge & Document Intelligence

Proprietary and messy real-world data made answerable — retrieved, grounded, and structured for AI.

The service

Most organizations are sitting on documents and proprietary knowledge they want to ask questions of — without the model making things up. RAG is the common pattern for that, but it fails without governance and grounding: retrieval quality, access control, and traceability back to the source are what separate a useful answer from a liability.

The RAG market is projected to grow from ~$2B to ~$10B by 2030; 80%+ of enterprise data is unstructured and largely unqueryable today.

Who it's for

Orgs sitting on documents and knowledge they want to query reliably, and teams needing structured data out of PDFs at scale.

What's in scope

  • RAG pipelines with hybrid retrieval (vector + keyword + graph)
  • Knowledge graphs (Neo4j) and search infrastructure (Elasticsearch / OpenSearch)
  • Document processing (PDF, Excel, Word, zip) → structured extraction into typed schemas
  • Entity extraction, dedup, normalization, and governance-aware retrieval

What good looks like

  1. Ingestion & parsing of messy documents

    Real documents are PDFs, spreadsheets, Word files, and zips — inconsistent, malformed, at scale. Parsing them reliably, with change detection so you don't reprocess what hasn't changed, is most of the battle before retrieval even starts.

  2. Chunking & embeddings

    How you split and embed content decides what retrieval can even find. It's a design choice with real consequences, not a default you accept from a tutorial.

  3. Retrieval, ranking & hybrid + graph

    Vector search alone misses. I combine vector + keyword + graph retrieval so both fuzzy meaning and exact relationships are reachable — I've run knowledge-graph-backed retrieval in production, not just in a notebook.

  4. Structured extraction

    Beyond Q&A, the high-value move is pulling messy documents into typed schemas — entity extraction, dedup, and normalization included — so the output is data, not just answers.

  5. Grounding & citations

    Answers have to trace back to the source. Citations aren't a nicety — they're what makes the output trustworthy and auditable instead of a plausible-sounding guess.

  6. Retrieval-quality eval & governance

    Retrieval quality needs its own evaluation, and access control has to precede retrieval — the model should never surface what the user isn't allowed to see. Governance is a precondition, not a feature you bolt on later.

How I approach the hard parts

I've stood up knowledge-graph and RAG infrastructure that stayed in production long after the R&D that spawned it, with hosting and cost tradeoffs documented for a real build-vs-buy decision. And I've built document pipelines that turn large volumes of messy real-world files into structured, queryable, grounded intelligence — the whole path from a raw PDF to a typed, cited answer.

Messy documents in; structured, queryable, grounded data out — in production.

05

MCP Servers & Integration Layers

Data and tools connected to AI assistants (Claude, Cursor, ChatGPT) and agents — securely, over MCP.

The service

MCP went from a new idea to a widely adopted standard for connecting data and tools to AI assistants in about eighteen months — adopted across the major AI vendors and handed to a foundation. It's the layer that makes a product and its data usable by Claude, Cursor, ChatGPT, and the agents built on top of them.

MCP became a cross-vendor standard in ~18 months; analysts expect 75% of API-gateway vendors to ship MCP support by end-2026.

Who it's for

Teams that want their product and data usable by assistants and agents, and platform teams standardizing on MCP.

What's in scope

  • Production MCP servers (tool and resource design)
  • Multi-client auth — JWT, OAuth 2.1 (PKCE / S256), API keys
  • Reliable transport under load (Redis-backed SSE, sticky sessions)
  • API and tool-layer design to expose internal systems safely

What good looks like

  1. Exposing data & tools via MCP

    The core work is turning existing systems into well-formed MCP tools and resources — deciding what to expose, at what granularity, and with what contracts an assistant can rely on.

  2. Tool design

    A tool an assistant can use reliably is a design problem: clear inputs and outputs, predictable errors, and no surprising side effects. Get it wrong and the model flails; get it right and it just works.

  3. Auth & security

    Exposing internal systems safely is the whole game. I've built multi-client auth — JWT bearer, OAuth 2.1 with PKCE, and API keys — so different callers get exactly the right access and nothing leaks.

  4. Reliable transport under load

    Streaming connections drop and sessions get lost behind load balancers. Redis-backed sessions, sticky routing, and graceful shutdown keep it stable under real, load-balanced traffic.

  5. Wiring into assistants

    The proof is driving the live tools from a real client — Claude, Cursor, ChatGPT — and getting real work back, not a green checkmark in a test harness.

  6. Gateway & versioning

    As tools multiply, you need a gateway and a versioning story so clients don't break when the surface evolves. It's the difference between a demo server and one a platform can depend on.

How I approach the hard parts

I've shipped production MCP servers end-to-end — the tool design, multi-client auth (JWT, OAuth 2.1 with PKCE, API keys), and streaming transport that stays reliable behind load balancers — and validated them the only way that counts: driving the live tools from a real assistant and getting real work back.

Production MCP layers, built and operated end-to-end — real tools, real auth, real client traffic.

Open to new work.

I'm open to senior and staff engineering roles and a little consulting — systems built and running, not just designed. If something here fits what you need, get in touch.

Get in touch