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gammaLabz
Discuss your AI workflow

AI & RAG development

Production AI workflows — not another chatbot demo.

gammaLabz builds AI features inside real products: retrieval pipelines, structured outputs, background jobs, and guardrails your team can operate. FastAPI, LangChain, and OpenAI where they fit — with honest limits, not marketing promises.

EU timezone · direct engineer access · paid discovery or defined build when the scope is clear

  • FastAPI and LangChain
  • OpenAI and structured outputs
  • RAG and background processing
  • Shipped in mobile and web products

Live AI features

AI that ships inside products

aeo — AI Visibility Growth System

When AI picks a winner, make it you. Grow your AI visibility with measurable results.

tryaeo.com

TryAEO icon
TryAEO
Web app
AI visibility SaaS that crawls your site, scores how well you show up in AI search, and delivers a free report by email.
Next.jsSupabaseLangChainInngestApify
Visit site
Screenshot of Guinea Pig Translator on iPhone
Guinea Pig Translator icon
Guinea Pig Translator
Mobile app
Record guinea pig sounds and get playful AI translations — with optional subscriptions on iPhone and Android.
React NativeFastAPIOpenAISupabaseRevenueCat
Download on theApp StoreGet it onGoogle Play

Production focus

What we build for AI products

Workflow design and scope
Define where AI adds value, what must stay deterministic, and how failures should behave for end users.
Retrieval and knowledge pipelines
Document ingestion, chunking, embedding, and retrieval tuned to your content — with evaluation on real queries.
LangChain orchestration
Chains, tools, and structured outputs with Pydantic validation so downstream code receives predictable data.
API layer and auth
FastAPI services that expose AI capabilities securely to web and mobile clients with rate limits and logging.
Async jobs and observability
Long-running tasks, retries, cost tracking, and traces so you can debug model behaviour in production.
Human review and fallback paths
Graceful degradation when models refuse, timeout, or return low-confidence results — users see something useful, not a blank error.

Is this the right fit?

Strong fit
  • You need AI embedded in a product users pay for or rely on daily
  • Retrieval, tooling, or multi-step workflows matter more than a single prompt box
  • You want engineers who ship FastAPI backends and can own the full pipeline
  • You understand models can be wrong and want operational guardrails
  • You have domain content or data to ground retrieval
Probably not the right fit
  • You want a generic ChatGPT wrapper with no product context
  • You expect AI to be infallible or fully replace human review in regulated flows
  • You need pure research with no path to production deployment
  • You are not willing to budget for inference, storage, and evaluation work

How we deliver AI features

01
Define the workflow
Inputs, outputs, latency expectations, and where humans stay in the loop.
02
Prototype on real data
Small-scale RAG or chain on representative content with measurable quality before full build.
03
Harden for production
APIs, auth, async processing, monitoring, and cost controls integrated with your app.
04
Evaluate and iterate
Track failure modes, refresh indexes, and tune prompts and retrieval based on production usage.
Getting started
After a short call, we will tell you whether the project needs a paid discovery, a focused sprint or a full build.

Client words

What teams say after shipping

“I started with nothing — no app, no store listing. About a month later the app was live on iPhone and Android, taking payments, and people were actually using it. I didn't think that was possible on my timeline.”

John R.

Founder · Guinea Pig Translator

“We had the vision for tryaeo.com but not the bandwidth to build it right. They turned it into a real product our customers rely on — not something we have to babysit every week.”

AI Agency

Co-founders · AI Agency

“I started with nothing — no app, no store listing. About a month later the app was live on iPhone and Android, taking payments, and people were actually using it. I didn't think that was possible on my timeline.”

“We had the vision for tryaeo.com but not the bandwidth to build it right. They turned it into a real product our customers rely on — not something we have to babysit every week.”

“I started with nothing — no app, no store listing. About a month later the app was live on iPhone and Android, taking payments, and people were actually using it. I didn't think that was possible on my timeline.”

“We had the vision for tryaeo.com but not the bandwidth to build it right. They turned it into a real product our customers rely on — not something we have to babysit every week.”

FAQ

Common questions

Discuss your AI workflow

Describe the user problem, your data sources, and where AI fits in the product. We will suggest a discovery or build path within one business day.

Prefer email? contactus@gammalabz.com