Enterprise AI
Engineering

We build AI agents that transform how enterprises operate — autonomous systems that reason, plan, and act on your data.

Get in Touch Explore Our Work

What We Do

We craft and deploy enterprise AI agents — autonomous systems that reason, plan, and act on your data, engineered to be verified, constrained, and governed.

Ingest

Documents, APIs, memory

Plan

Route, decompose & strategize

Reason

Think, analyze, decide

Execute

Tools, APIs, code

Verify

Guardrails, evals & validation

Autonomous Agents

AI agents that reason, plan, and act independently — handling complex workflows without constant human supervision.

> Analyzing quarterly report…
› plan("decompose into 3 subtasks")
› search("revenue data, Q1–Q4")
› reason("compare YoY trends")
› verify("cross-check calculations")
✓ Report summary generated

Multi-Agent Orchestration

Coordinate teams of specialized agents for long-horizon, multi-step processes — with handoffs, memory, and real-time oversight.

Custom AI Pipelines

Tailored AI workflows designed for your business — integrating with your existing systems and scaling with your needs.

Verified Constrained Governed

Agentic systems only deliver if you can trust them in production. We combine engineering depth with a foundation in technology law — so every agent is verifiable, constrained, and governable by design.

Open Source

We believe in giving back to the community. Our open-source tools are used by developers worldwide.

💎

ContextGem

Effortless LLM extraction from documents. Our open-source framework that makes it simple to extract structured data using large language models.

1.8k+ stars
13k downloads/month
Python
uv add contextgem
GitHub Documentation

Key Features

  • Minimal code — define what to extract, not how
  • Automated prompts — LLM prompt engineering handled for you
  • Reference mapping — trace every extraction back to source
  • Multi-LLM — OpenAI, Anthropic, Google, Azure, local models
extract.py
1 from contextgem import Document, StringConcept
2 doc = Document(raw_text=contract_text)
3 doc.concepts = [StringConcept(name="Anomalies", ...)]
4 doc = llm.extract_all(doc)
✓ 3 anomalies extracted with references
🦭

licenseal

New Claude Code skill

Fast cross-ecosystem dependency license compatibility checker. Our open-source tool that scans dependency trees across many ecosystems to catch incompatible licenses before release — without installing a single package.

725 downloads/month Python
uv tool install licenseal
GitHub PyPI
🪁

tethered

New

Runtime network egress control for Python. Our open-source security library that restricts which hosts your application can connect to — with a single function call.

5k downloads/month Python C
uv add tethered
GitHub PyPI

Trusted By

Enterprise AI solutions delivering measurable results.

80% Less time on invoicing

The solution is now saving 80% of the time spent on invoice attachment generation.

— Rune Larsen, Co-founder & HSE Leader at Christiania Stillas AS

Christiania Stillas AS

Christiania Stillas AS

Scaffolding business · Oslo, Norway

Agentic document intelligence platform — a multi-agent system of long-horizon AI agents that processes large volumes of scaffolding project files, performs complex billing calculations across different scaffolding project types — with deterministic tools and accuracy safeguards ensuring precision — and delivers detailed invoice attachments.

Invoice Attachment Generation

Large-scale file processing, complex calculations, multiple scaffolding project types

Project Q&A

Agentic reasoning over scaffolding project documentation with cited responses

Let's Build Together

Ready to bring AI agents into your enterprise? We'd love to hear about your challenges.

Click to copy Copied to clipboard