01
AI tools adopted without strategy — no clarity on what they're actually solving.
Repeat Until
AI strategy, custom agents, and intelligent automation that create real leverage.
Repeat Until delivers AI strategy, LLM integrations, and workflow automation for teams that want practical results — not a proof of concept that never ships.
AI StrategyLLM DevelopmentRAG PipelinesWorkflow Automation
What we build
AI focusAI Strategy & Audit
We map your business, find where AI creates real leverage, and give you a clear roadmap — not a buzzword deck.
LLM & Agent Development
Custom AI agents, RAG pipelines, and LLM integrations that fit into your stack and solve real problems.
Workflow Automation
Replace the repetitive with intelligent systems. From data processing to customer support — we automate with precision.
Where we help most
The work usually starts when AI isn't delivering the way you expected.
02
Custom LLM or agent builds scoped too broadly, stalled before they ship.
03
Workflows still manual where intelligent automation could remove the bottleneck.
Start with the right scope
Engagements built around real business outcomes, not AI for its own sake.
We start with the problem, find where AI creates genuine leverage, and scope the smallest build that proves it.
AI Strategy & Audit
We map your business, find where AI creates real leverage, and give you a clear roadmap — not a buzzword deck.
LLM & Agent Development
Custom AI agents, RAG pipelines, and LLM integrations that fit into your stack and solve real problems.
Workflow Automation
Replace the repetitive with intelligent systems. From data processing to customer support — we automate with precision.
How we work
Audit first. Build what's needed. Ship something real.
We start every engagement by understanding your business before recommending a tool — because the wrong AI solution is worse than none.
1
Map the business problem and identify where AI creates real, measurable leverage.
2
Define the right tool — agent, pipeline, or integration — and scope the first version.
3
Build and test against real inputs, not just demo scenarios.
4
Deploy, document, and hand off so the system stays useful after we leave.
Use cases
A strong fit for businesses that want AI to solve real problems, not generate demos.
AI strategy for non-technical leadership
For executives and operators who need to understand where AI creates real leverage in their business — without the buzzword deck or the vendor pitch.
Custom LLM integrations into existing products
For teams that want to bring language model capabilities into their stack without rearchitecting everything or building a science project.
RAG systems over internal knowledge
For organizations with valuable internal data — docs, transcripts, support tickets — who want intelligent retrieval instead of keyword search.
Intelligent automation pipelines
For teams where repetitive data processing, routing, or customer interactions are eating time that could be reclaimed with a well-scoped automation.
What makes this credible
Trust should come from clarity, not vendor credentialism.
We say what we do, scope what we can deliver, and hand off work that your team can actually operate.
Grounded strategy that starts with business outcomes, not technology trends.
Custom builds scoped to your stack — not off-the-shelf wrappers repackaged as strategy.
Clear process from audit through deployment, with honest tradeoff communication.
Documented handoffs so your team can operate what we build.
Projects
Open source work and tools built in public.
LLM Gateway
An abstraction layer that sits in front of multiple LLM APIs — OpenAI, Anthropic, and others — under a single OpenAI-compatible interface. Handles routing, per-consumer API keys, rate limiting, spend caps, usage tracking, and request logging.
Open SourceLLMPython
AI Chatbot
A conversational AI chatbot powered by large language models. Handles natural language queries, context-aware responses, and integrates easily into any product or workflow. Includes MCP (Model Context Protocol) support for connecting external tools and data sources.
Open SourceLLMChatbotMCP
RAG Pipeline
A retrieval-augmented generation pipeline that connects LLMs to your internal knowledge base. Supports document ingestion, vector search, and context-grounded responses over any corpus.
Open SourceRAGLLM
Let's find where AI creates real leverage
If you want to map your AI opportunities, build a custom agent, or automate a workflow that's slowing you down — let's talk.
Start with a clear conversation about the problem. We'll tell you honestly what AI can and can't do, and scope the right first step.