Building AI That Gets Work Done
AI agents that do real work
We design and ship agentic AI products: SaaS, RAG systems, agent harnesses, and automation your team can run.
What we build
Five ways we put agents into production.
AI-Powered SaaS
Full-stack AI products built to ship, not demos. Real auth, billing, and production infrastructure.
- Streaming AI interfaces with structured output
- Usage-based billing and multi-tenant architecture
- LLM orchestration with tool use and agents
AI Agents
Autonomous agents that handle multi-step tasks end to end and run reliably without babysitting.
- Multi-step reasoning and tool-calling agents
- Human-in-the-loop escalation flows
- Scheduled, event-driven, and reactive triggers
Agentic RAG Systems
Retrieval that finds the right answer, not just the most similar text. Grounded in your data.
- Hybrid semantic and keyword retrieval
- Document ingestion pipelines for any format
- Grounded responses with source citations
Agent Harness Development
The runtime beneath the model: server, tool loop, sessions, and billing that make agents production-ready.
- Streaming tool-calling loops
- Multi-provider model registries (OpenAI, Anthropic, Google)
- Persistent sessions and conversation history
- Auth, metering, and usage-based billing
Automation Workflows
Intelligent pipelines that connect your tools, APIs, and data sources, replacing manual processes.
- API and webhook integrations
- Event-driven pipelines
- Slack, YouTube, and more
- Error handling and monitoring
How we work
From first call to a system running in production.
Scope the problem
One or two weeks to map your workflow, data, and success criteria into a concrete build plan.
Design the system
Architecture, model selection, and evaluation plan before any code. You approve the design.
Build and evaluate
Weekly working software. Agents tested against real tasks, not demos.
Deploy and iterate
Ship to your infrastructure, monitor behavior, and tighten the loop as usage grows.
Common questions
We start with a fixed-scope discovery phase to map your workflow, data, and success criteria. From there we build in weekly increments, so you see working software every week instead of waiting for a big reveal.
Discovery takes one to two weeks. Most builds ship a working system in four to eight weeks, depending on scope. Larger platforms take longer, and we tell you that upfront rather than surprise you later.
You do. Every engagement ends with a full repository handover, documentation, and deployment access. There is no vendor lock-in and no licensing tail.
You can run the system yourself, or keep us on an optional maintenance retainer covering monitoring, evaluation regressions, and model upgrades as providers ship new versions.
Least-privilege access, deployment in your cloud where possible, and no training on your data. We sign NDAs as standard and scope data access per integration.
Tell us about your project
Tell us what you're building and we'll show you how AI can accelerate it. We reply within a day.
info@92labs.ai