Put the manual work on autopilot.

What you get

• Document processing — ingest PDFs, scans, and forms, classify them, and extract structured data with validation and human-in-the-loop checks.
• Approval workflows — multi-step routing, escalations, and audit trails that move requests through your rules automatically.
• Data extraction & reconciliation — pull fields from messy sources, match them against your systems, and flag exceptions.
• Email & inbox triage — classify, summarize, route, and draft responses so the right items reach the right people.
• Decision engines — policy-driven logic that runs 24/7, escalating only the edge cases you choose to keep human.
• Your repository — the full automation codebase in your GitHub organization, with handover docs and no lock-in.

How we build it

One focused discovery session maps the process you want automated and produces a fixed-price proposal. Senior engineers then design the logic, guardrails, and escalation paths — the blueprint the AI builds against. AI agents generate the automation (pipelines, integrations, prompts, tests, monitoring) in parallel, and every pull request is reviewed line-by-line by a senior before it ships. You see weekly demos against a live staging environment, and every build includes a 30-day post-launch window for fixes and tuning.

Default technology stack

Node.js or Python (FastAPI) for the automation back end, PostgreSQL plus Redis for state and queues, hosted on AWS or Vercel. The AI layer uses OpenAI, Anthropic Claude, and open-weights models orchestrated with LangGraph, with a vector database such as Pinecone where retrieval is needed. If you have a hard requirement — Java, .NET, or on-prem — we adapt.