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Product & AI Engineering Studio

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01AI PRODUCT ENGINEERINGSYSTEM ONLINE

PRODUCT/AIENGINEERING

RAGVECTORAGENTLLMSYSTEM

RAG systems, AI agents and LLM infrastructure — engineered for production, not demos.

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THE ENGINEER→
RAG / AGENTS / LLMSCROLL↓
02WHAT WE ENGINEER

THE SYSTEM

AI products are more than models. We engineer the systems around them.

  1. 01RETRIEVAL

    RAG Systems

    Give your AI a reliable memory.

    Knowledge systems that connect your data to models through controlled retrieval, real context and citations you can check.

    DOCUMENTSEMBEDDINGSRETRIEVALCONTEXTMODEL
  2. 02ORCHESTRATION

    AI Agents

    Systems that can actually act.

    Multi-step agents with tools, verification and guardrails — including what happens when a step fails.

    INPUTREASONTOOLVERIFYACTION
  3. 03INTERFACES

    Integrations

    Connect intelligence to what you already run.

    Secure links into business systems, databases, APIs and internal tools, with access control at every edge.

    APPAPIDATAAI
  4. 04ARCHITECTURE

    Full-Stack

    From model to interface.

    Applications spanning frontend, backend, data, AI orchestration and the infrastructure underneath.

    INTERFACEAPIAIDATAINFRASTRUCTURE
03Current stackStatus: active

Built on what's next.

Tools are interchangeable. Engineering isn't.

01Interface
  • Next.jsFrameworkSSR / RSC
  • ReactLibraryUI runtime
  • TypeScriptLanguageTypes
  • TailwindStylingTokens
02Intelligence
  • OpenAIModel providerLLM API
  • AnthropicModel providerLLM API
  • PythonLanguageRuntime
  • FastAPIServiceAsync API
  • LangGraphOrchestrationAgent graphs
03Data
  • PostgreSQLData layerProduction
  • pgvectorVector indexEmbeddings
  • RedisCacheQueues
  • PineconeVector DBManaged
04Infrastructure
  • DockerRuntimeContainers
  • AWSCloudCompute
  • VercelEdgeDeploy
  • CI/CDPipelineAutomated
04Method

How we build.

  1. 01

    Production from day one

    Security, scale and observability are designed in at the start, not retrofitted once something breaks.

  2. 02

    Systems, not demos

    We do not ship experiments. We ship what a team can run, maintain and extend after we hand it over.

  3. 03

    Guardrails by default

    Access control, input validation, output filtering and audit trails on every system that touches real data.

  4. 04

    We own the outcome

    Clear communication, documentation you can actually use, and support that continues past launch.

Operating constraints
Enterprise data
Access control and audit trails on sensitive records
SAP / ERP
Direct integration with real data models and business logic
AI guardrails
Query validation and result filtering before anything returns
24/7 operation
Monitoring, error handling and room to scale
05Contact

Let's discuss
your system.

From retrieval to deployment — tell us what you're building.

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