Stop running fragile pilots. We build deterministic, high-throughput LangChain architectures and multi-agent RAG pipelines that turn fragmented industrial data into real-time operational intelligence.
Modern enterprises sit on vast oceans of highly fragmented, multi-modal data — real-time SCADA sensor logs, geospatial infrastructure layers, unstructured PDF asset manuals, and regulatory grid codes. Naive chatbots break when exposed to any of it.
We bridge the gap between legacy operational technology and modern LLM orchestration, building stateful, auditable AI frameworks that optimize asset uptime, streamline supply chain routing, and automate complex compliance reporting.
A full-stack AI engineering framework purpose-built for high-stakes industrial environments.
Measurable operational improvements across asset management, compliance, and decision-making speed.
A phased, milestone-driven approach from data audit to full production deployment in 12 weeks.
Our Infrastructure Assessment is the lowest-friction entry point — 3 weeks, a flat fee, and a complete architectural roadmap your team can act on immediately.
Modern industrial, energy, and logistics enterprises sit on vast oceans of highly fragmented, multi-modal data — ranging from real-time SCADA sensor logs and geospatial infrastructure layers to unstructured PDF asset manuals and regulatory grid codes. We build the AI systems that make that data operational.
LangChain document loaders integrated with layout-aware parsing via Unstructured.io and Camelot extract text, tables, and hierarchical metadata from complex asset manuals and grid blueprints — including wiring diagrams and multi-page schematics.
Semantic chunking strategies ensure formulas, wiring diagrams, and multi-variable operational parameters do not lose context during tokenization. Standard chunking breaks industrial documents; our approach treats them as structured data.
A dual-engine retrieval pipeline combining dense vector search (Pgvector or Qdrant for semantic alignment) with sparse keyword search (BM25 for precise asset serial numbers, error codes, and exact technical terminology). This hybrid approach outperforms either strategy alone on industrial data.
Custom metadata schemas enable filtering by asset type, maintenance period, regulatory jurisdiction, or equipment serial range — turning raw retrieval into a precise, context-aware query engine for field operators and compliance teams.
Inspect user queries and safely delegate tasks to specific domain agents — routing a "substation failure" query to the maintenance logs vector database vs. a real-time SCADA API — without exposing the agent to both simultaneously.
Automated evaluation steps validate three dimensions at every graph node: Context Relevance (did the vector DB retrieve relevant info?), Groundedness (is the LLM response strictly derived from retrieved documents?), and Answer Relevance (does the output safely answer the engineer's prompt?).
StateGraph checkpoints pause execution and require human approval before triggering critical external actions — drafting a formal regulatory grid-deviation report, issuing an automated vendor dispatch, or modifying operational setpoints.
When a guardrail fails, the graph re-routes to a self-correction subgraph that retrieves additional context, re-evaluates groundedness, and either returns a corrected response or escalates to a human reviewer — never silently producing a hallucinated output.
We wrap SQL databases, SCADA telemetry APIs, and real-time transit and weather streams into secure LangChain tools — callable by the agent with full input/output logging via LangSmith, without exposing raw database credentials or API keys to the model layer.
Context windows and payloads are optimized to execute lightweight agentic queries over constrained-bandwidth networks — remote data centers, substations, and offshore platforms using Starlink or orbital satellite backhaul networks.
All deployments occur inside the client's cloud perimeter (AWS VPC, Azure VNET, or private cloud). No data transits to third-party model providers without explicit consent; private model deployments are available for air-gapped environments.
The entire pipeline is instrumented using LangSmith for continuous prompt debugging, latency tracing, token cost optimization, and regression testing. Every agent run generates a complete audit trail for regulatory review.
Start with our flat-fee Infrastructure Assessment — 3 weeks, a complete data audit, and a custom LangGraph architectural blueprint your team can act on immediately.
Three clearly scoped tiers — from a flat-fee feasibility assessment to a dedicated engineering retainer. Each is designed as a logical progression with no hidden scope creep.
| Feature | Assessment $15K |
MVP $45K+ |
Enterprise Custom |
|---|---|---|---|
| Data Architecture Audit | ✓ | ✓ | ✓ |
| Token Cost & Feasibility Modeling | ✓ | ✓ | ✓ |
| Custom LangGraph Blueprint | ✓ | ✓ | ✓ |
| Multi-Modal RAG Pipeline Build | — | ✓ | ✓ |
| Stateful LangGraph Runtime | — | ✓ | ✓ |
| Enterprise API Tool Integrations | — | Up to 3 | Unlimited |
| Human-in-the-Loop (HITL) Safeguards | — | ✓ | ✓ |
| LangSmith Observability & Tracing | — | ✓ | ✓ |
| Continuous Prompt Optimization | — | — | ✓ |
| Custom Model Fine-Tuning | — | — | ✓ |
| Edge / Satellite Optimization | — | — | ✓ |
| Dedicated SLA Support | — | — | ✓ |
| Assessment Fee Credit | — | 100% credited | 100% credited |
A 3-week, flat-fee Infrastructure Assessment gives you a complete LangGraph architectural blueprint — and a 100% fee credit if you move to implementation. No commitment beyond the assessment.
Tell us about your operational environment. We'll evaluate your data stack, identify the highest-ROI integration points, and return a complete LangGraph architectural blueprint in 3 weeks.
Our engineering team will review your submission and follow up within 24 business hours to schedule your technical brief.
— Syntropi Engineering TeamThank you for reaching out. Our engineering team will review your submission and follow up within 24 business hours to schedule your technical brief and discuss next steps.
— Syntropi Engineering Team