01
Agents that do real work
Tool-using agents wired into your stack: triage, research, ops automation, review queues, and internal support workflows with guardrails and kill switches.
$ berlin-labs --intro
I ship AI systems.
Not decks. Not workshops.
Systems
Berlin Labs LLC is an engineering-led AI consulting company for teams that need real AI in production: agents, pipelines, evals, integrations, and internal tools. I write the code, deploy the thing, and hand it back with the keys.
$ python -m eval.run --suite ship-check
load traces, prompts, tools, and failure cases
grade for tool use, faithfulness, latency, cost
------------------------------------
tool calls pass
retrieval pass
latency budget reviewed
handoff docs ready
------------------------------------
ok regressions: 0
write report -> docs/runbook.md
push to ci... done
$
// what I build
Not "AI strategy." The thing that actually runs when your customer is using it.
02
RAG and data pipelines
Retrieval that actually retrieves: chunking, hybrid search, reranking, freshness, and eval harnesses built on your data instead of a demo dataset.
03
Evals and observability
Offline eval suites, online tracing, regression gates, and dashboards that make AI behavior visible before it surprises the business.
04
Internal tools that scale
The script, spreadsheet, or notebook workflow becomes a real service your team can run, inspect, and improve.
05
Integration and plumbing
Stripe, Slack, HubSpot, Google Workspace, custom APIs, auth, webhooks, queues, and the unglamorous work that decides whether AI ships.
06
Technical advisory
A senior second set of eyes on architecture, prompts, eval design, vendor choices, and implementation plans. Useful, direct, and code-aware.
// how it goes
Short loops. Real artifacts. No 8-week discovery phase.
step 01 / week 1
Audit
I look at the code, the data, the workflow, and the user path. Then I tell you what is worth building and what is just noise.
step 02 / week 2
Prototype
Smallest version that proves the system works on your actual data. Not a Figma. Something that runs.
step 03 / weeks 3+
Ship
Production hardening: evals, observability, error paths, deploys, cost controls, and the boring parts that make it reliable.
step 04
Hand off
Docs, runbooks, walkthroughs, and ownership transfer so your team can operate the thing without mystery.
// rules of engagement
The honest version of who this is for.
+ good fit
- You have a real workflow with real users and want AI inside it.
- You have engineers, but they are busy on the core product.
- You would rather see a working prototype in 2 weeks than a 40-page deck.
- You care about evals, latency, cost, and ownership after launch.
- You are done buying "AI strategy" and want someone who writes code.
- bad fit
- You want a thought-leadership keynote with no implementation.
- You need a large agency with a project manager hierarchy.
- You believe a chatbot wrapper alone is going to transform the business.
- You want a 6-month AI roadmap with no shipping in between.
- You are shopping for the cheapest pair of hands. I am not it.
// company
Public company information for clients, vendors, and counterparties.
- legal name
- Berlin Labs LLC
- business type
- AI technology consulting company
- primary work
- AI systems, automation, custom software, and integrations
- public contact
- eugene@berlinlabs.net
- website
- berlinlabs.net
- service area
- United States and remote engagements
// stack
What I reach for, subject to whatever your team already runs.
models
OpenAI / Claude / Gemini / open-weights
languages
Python / TypeScript / SQL / Bash
backend
FastAPI / Node / Postgres / Redis
frontend
Next.js / React / practical UI
agents
OpenAI SDK / Anthropic SDK / MCP / custom loops
retrieval
pgvector / Pinecone / BM25 / rerankers
eval / obs
Braintrust / Langfuse / custom harnesses
infra
AWS / Vercel / Docker / serverless
$ berlin-labs contact --new
Have something that needs shipping?
Send a paragraph about the problem. I read every email and reply directly.
No forms, no funnel.