Applied AI solutions for finance, legal, operations, edge computing, intelligent automation, and EV fleet management. Optimized for enterprise reliability and private deployment.
Practical, low-latency, and high-security AI engines pre-configured for specialized operational tasks.
Automated investment analyst pipeline. Extracts core financials, analyzes founder profiles, detects risks, and generates a structured investability score sheet.
Interact directly with ledger balances. Automatically identify transaction trends, detect recurring expenses, and forecast cash flow patterns instantly.
Extract compliance conditions, key legal risks, liability limits, and expiration timelines from high-volume contracts. Saves review time by up to 80%.
Visual workflow configuration for multi-agent chains. Define input boundaries, link external LLMs, and assign runtime logic without leaving the browser interface.
Lightweight GGUF engine optimized to run on low-resource hardware like ARM64 gateways, industrial PCs, and private localized clouds. Completely offline.
Monitor real-time battery status, health decay forecasts, charging safety events, and operational routing metrics for logistics & autonomous fleets.
Click any component below to explore how our enterprise AI stack functions together seamlessly.
We integrate fine-tuned and quantized LLMs engineered for specific industry verticals. By leveraging smaller, highly efficient parameter weights (7B, 13B, or 32B), we achieve rapid tokens-per-second response speeds with zero compromise on factual performance.
Spin up pipelines using clean Python, JSON declarative configs, or Deno Edge Functions. Our APIs plug seamlessly into standard enterprise telemetry, databases, and monitoring environments.
Standard JSON exchange with support for stream responses.
Configure your agents, databases, and LLMs in readable JSON files.
from idopentech_ai import Agent, Pipeline
# 1. Initialize RAG client with private vector database
pipeline = Pipeline(
model="idopentech-llama-32b-quant",
temperature=0.0
)
# 2. Spin up Document Analysis Agent
analyst = Agent(
role="Investment Analyst",
rules=["strict_compliance", "financial_vetting"]
)
pipeline.add_agent(analyst)
# 3. Process Pitch Decks
result = pipeline.execute(
source="./pitch_deck_v4.pdf",
output_format="json"
)
print(f"Vetting Score: {result.score}")Get early access to our production-ready AI systems. Complete the quick priority waitlist form below, and we will coordinate sandbox access as slots open up.